fix gofmt formatting

Fix two issues in the HybridCache:

1. Stale conv state on slot reuse: When a slot is freed and reused by
   a new sequence, the conv state tensor retained stale data from the
   previous sequence. Now tracks newly allocated slots in StartForward
   and zeros their conv state before use.

2. SetRows requires F32 source: The ggml SetRows operation requires
   the source tensor to be F32. Added casts in three places:
   - zeroConvSlots: Create zeros tensor as F32
   - EnsureWritable: Cast source to F32 before copy-on-write
   - UpdateConvState: Cast source to F32 before updating state

Also adds tests for the slot zeroing logic.

.github/ISSUE_TEMPLATE/10_bug_report.yml

@@ -13,7 +13,7 @@ body:
     id: logs
     attributes:
       label: Relevant log output
-      description: Please copy and paste any relevant log output. See [Troubleshooting Guide](https://github.com/ollama/ollama/blob/main/docs/troubleshooting.md#how-to-troubleshoot-issues) for details.
+      description: Please copy and paste any relevant log output. See [Troubleshooting Guide](https://github.com/ollama/ollama/blob/main/docs/troubleshooting.mdx#how-to-troubleshoot-issues) for details.
       render: shell
     validations:
       required: false

.github/workflows/release.yaml

@@ -68,6 +68,7 @@ jobs:
           name: bundles-darwin
           path: |
             dist/*.tgz
+            dist/*.tar.zst
             dist/*.zip
             dist/*.dmg
 
@@ -371,13 +372,17 @@ jobs:
           outputs: type=local,dest=dist/${{ matrix.os }}-${{ matrix.arch }}
           cache-from: type=registry,ref=${{ vars.DOCKER_REPO }}:latest
           cache-to: type=inline
+      - name: Deduplicate CUDA libraries
+        run: |
+          ./scripts/deduplicate_cuda_libs.sh dist/${{ matrix.os }}-${{ matrix.arch }}
       - run: |
           for COMPONENT in bin/* lib/ollama/*; do
             case "$COMPONENT" in
-              bin/ollama)                echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}.tar.in ;;
+              bin/ollama*)               echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}.tar.in ;;
               lib/ollama/*.so*)          echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}.tar.in ;;
               lib/ollama/cuda_v*)        echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}.tar.in ;;
               lib/ollama/vulkan*)        echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}.tar.in ;;
+              lib/ollama/mlx*)           echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}.tar.in ;;
               lib/ollama/cuda_jetpack5)  echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}-jetpack5.tar.in ;;
               lib/ollama/cuda_jetpack6)  echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}-jetpack6.tar.in ;;
               lib/ollama/rocm)           echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}-rocm.tar.in ;;
@@ -392,13 +397,13 @@ jobs:
           done
       - run: |
           for ARCHIVE in dist/${{ matrix.os }}-${{ matrix.arch }}/*.tar.in; do
-            tar c -C dist/${{ matrix.os }}-${{ matrix.arch }} -T $ARCHIVE --owner 0 --group 0 | pigz -9vc >$(basename ${ARCHIVE//.*/}.tgz);
+            tar c -C dist/${{ matrix.os }}-${{ matrix.arch }} -T $ARCHIVE --owner 0 --group 0 | zstd --ultra -22 -T0 >$(basename ${ARCHIVE//.*/}.tar.zst);
           done
       - uses: actions/upload-artifact@v4
         with:
           name: bundles-${{ matrix.os }}-${{ matrix.arch }}-${{ matrix.target }}
           path: |
-            *.tgz
+            *.tar.zst
 
   # Build each Docker variant (OS, arch, and flavor) separately. Using QEMU is unreliable and slower.
   docker-build-push:
@@ -531,7 +536,7 @@ jobs:
       - name: Upload release artifacts
         run: |
           pids=()
-          for payload in dist/*.txt dist/*.zip dist/*.tgz dist/*.exe dist/*.dmg ; do
+          for payload in dist/*.txt dist/*.zip dist/*.tgz dist/*.tar.zst dist/*.exe dist/*.dmg ; do
             echo "Uploading $payload"
             gh release upload ${GITHUB_REF_NAME} $payload --clobber &
             pids[$!]=$!

CMakeLists.txt

@@ -2,6 +2,22 @@ cmake_minimum_required(VERSION 3.21)
 
 project(Ollama C CXX)
 
+# Handle cross-compilation on macOS: when CMAKE_OSX_ARCHITECTURES is set to a
+# single architecture different from the host, override CMAKE_SYSTEM_PROCESSOR
+# to match. This is necessary because CMAKE_SYSTEM_PROCESSOR defaults to the
+# host architecture, but downstream projects (like MLX) use it to detect the
+# target architecture.
+if(CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_OSX_ARCHITECTURES MATCHES ";")
+    # Single architecture specified
+    if(CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" AND NOT CMAKE_SYSTEM_PROCESSOR STREQUAL "x86_64")
+        message(STATUS "Cross-compiling for x86_64: overriding CMAKE_SYSTEM_PROCESSOR from ${CMAKE_SYSTEM_PROCESSOR} to x86_64")
+        set(CMAKE_SYSTEM_PROCESSOR "x86_64")
+    elseif(CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" AND NOT CMAKE_SYSTEM_PROCESSOR STREQUAL "arm64")
+        message(STATUS "Cross-compiling for arm64: overriding CMAKE_SYSTEM_PROCESSOR from ${CMAKE_SYSTEM_PROCESSOR} to arm64")
+        set(CMAKE_SYSTEM_PROCESSOR "arm64")
+    endif()
+endif()
+
 include(CheckLanguage)
 include(GNUInstallDirs)
 
@@ -12,7 +28,7 @@ set(BUILD_SHARED_LIBS ON)
 
 set(CMAKE_CXX_STANDARD 17)
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
-set(CMAKE_CXX_EXTENSIONS OFF)
+set(CMAKE_CXX_EXTENSIONS ON) # Recent versions of MLX Requires gnu++17 extensions to compile properly
 
 set(GGML_BUILD ON)
 set(GGML_SHARED ON)
@@ -32,9 +48,10 @@ if((CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_OSX_ARCHITECTURES MATCHES "arm64")
     set(GGML_CPU_ALL_VARIANTS ON)
 endif()
 
-if (CMAKE_OSX_ARCHITECTURES MATCHES "x86_64")
+if(APPLE)
     set(CMAKE_BUILD_RPATH "@loader_path")
     set(CMAKE_INSTALL_RPATH "@loader_path")
+    set(CMAKE_BUILD_WITH_INSTALL_RPATH ON)
 endif()
 
 set(OLLAMA_BUILD_DIR ${CMAKE_BINARY_DIR}/lib/ollama)
@@ -147,14 +164,56 @@ if(CMAKE_HIP_COMPILER)
     endif()
 endif()
 
-find_package(Vulkan)
-if(Vulkan_FOUND)
-    add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src/ggml-vulkan)
-    install(TARGETS ggml-vulkan
-        RUNTIME_DEPENDENCIES
-            PRE_INCLUDE_REGEXES vulkan
-            PRE_EXCLUDE_REGEXES ".*"
-        RUNTIME DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT Vulkan
-        LIBRARY DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT Vulkan
-    )
+if(NOT APPLE)
+    find_package(Vulkan)
+    if(Vulkan_FOUND)
+        add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src/ggml-vulkan)
+        install(TARGETS ggml-vulkan
+            RUNTIME_DEPENDENCIES
+                PRE_INCLUDE_REGEXES vulkan
+                PRE_EXCLUDE_REGEXES ".*"
+            RUNTIME DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT Vulkan
+            LIBRARY DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT Vulkan
+        )
+    endif()
 endif()
+
+option(MLX_ENGINE "Enable MLX backend" OFF)
+
+if(MLX_ENGINE)
+    message(STATUS "Setting up MLX (this takes a while...)")
+    add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/x/ml/backend/mlx)
+
+    # Find CUDA toolkit if MLX is built with CUDA support
+    find_package(CUDAToolkit)
+
+    install(TARGETS mlx mlxc
+        RUNTIME_DEPENDENCIES
+            DIRECTORIES ${CUDAToolkit_BIN_DIR} ${CUDAToolkit_BIN_DIR}/x64 ${CUDAToolkit_LIBRARY_DIR}
+            PRE_INCLUDE_REGEXES cublas cublasLt cudart nvrtc nvrtc-builtins cudnn nccl openblas gfortran
+            PRE_EXCLUDE_REGEXES ".*"
+        RUNTIME DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT MLX
+        LIBRARY DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT MLX
+        FRAMEWORK DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT MLX
+    )
+
+    # Install the Metal library for macOS arm64 (must be colocated with the binary)
+    # Metal backend is only built for arm64, not x86_64
+    if(APPLE AND CMAKE_SYSTEM_PROCESSOR STREQUAL "arm64")
+        install(FILES ${CMAKE_BINARY_DIR}/_deps/mlx-build/mlx/backend/metal/kernels/mlx.metallib
+            DESTINATION ${OLLAMA_INSTALL_DIR}
+            COMPONENT MLX)
+    endif()
+
+    # Manually install cudart and cublas since they might not be picked up as direct dependencies
+    if(CUDAToolkit_FOUND)
+        file(GLOB CUDART_LIBS
+            "${CUDAToolkit_LIBRARY_DIR}/libcudart.so*"
+            "${CUDAToolkit_LIBRARY_DIR}/libcublas.so*")
+        if(CUDART_LIBS)
+            install(FILES ${CUDART_LIBS}
+                DESTINATION ${OLLAMA_INSTALL_DIR}
+                COMPONENT MLX)
+        endif()
+    endif()
+endif()

CMakePresets.json

@@ -41,7 +41,7 @@
       "inherits": [ "CUDA" ],
       "cacheVariables": {
         "CMAKE_CUDA_ARCHITECTURES": "75-virtual;80-virtual;86-virtual;87-virtual;89-virtual;90-virtual;90a-virtual;100-virtual;103-virtual;110-virtual;120-virtual;121-virtual",
-        "CMAKE_CUDA_FLAGS": "-t 2",
+        "CMAKE_CUDA_FLAGS": "-t 4",
         "OLLAMA_RUNNER_DIR": "cuda_v13"
       }
     },
@@ -83,6 +83,28 @@
       "cacheVariables": {
         "OLLAMA_RUNNER_DIR": "vulkan"
       }
+    },
+    {
+      "name": "MLX",
+      "inherits": [ "Default" ],
+      "cacheVariables": {
+        "MLX_ENGINE": "ON",
+        "OLLAMA_RUNNER_DIR": "mlx"
+      }
+    },
+    {
+      "name": "MLX CUDA 12",
+      "inherits": [ "MLX", "CUDA 12" ],
+      "cacheVariables": {
+        "OLLAMA_RUNNER_DIR": "mlx_cuda_v12"
+      }
+    },
+    {
+      "name": "MLX CUDA 13",
+      "inherits": [ "MLX", "CUDA 13" ],
+      "cacheVariables": {
+        "OLLAMA_RUNNER_DIR": "mlx_cuda_v13"
+      }
     }
   ],
   "buildPresets": [
@@ -140,6 +162,21 @@
       "name": "Vulkan",
       "targets": [ "ggml-vulkan" ],
       "configurePreset": "Vulkan"
+    },
+    {
+      "name": "MLX",
+      "targets": [ "mlx", "mlxc" ],
+      "configurePreset": "MLX"
+    },
+    {
+      "name": "MLX CUDA 12",
+      "targets": [ "mlx", "mlxc" ],
+      "configurePreset": "MLX CUDA 12"
+    },
+    {
+      "name": "MLX CUDA 13",
+      "targets": [ "mlx", "mlxc" ],
+      "configurePreset": "MLX CUDA 13"
     }
   ]
 }

Dockerfile

@@ -32,7 +32,7 @@ ENV PATH=/${VULKANVERSION}/x86_64/bin:$PATH
 FROM --platform=linux/arm64 almalinux:8 AS base-arm64
 # install epel-release for ccache
 RUN yum install -y yum-utils epel-release \
-    && dnf install -y clang ccache \
+    && dnf install -y clang ccache git \
     && yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel8/sbsa/cuda-rhel8.repo
 ENV CC=clang CXX=clang++
 
@@ -131,8 +131,32 @@ COPY ml/backend/ggml/ggml ml/backend/ggml/ggml
 RUN --mount=type=cache,target=/root/.ccache \
     cmake --preset 'Vulkan' \
         && cmake --build --parallel --preset 'Vulkan' \
-        && cmake --install build --component Vulkan --strip --parallel 8 
+        && cmake --install build --component Vulkan --strip --parallel 8
 
+FROM base AS mlx
+ARG CUDA13VERSION=13.0
+RUN dnf install -y cuda-toolkit-${CUDA13VERSION//./-} \
+    && dnf install -y openblas-devel lapack-devel \
+    && dnf install -y libcudnn9-cuda-13 libcudnn9-devel-cuda-13 \
+    && dnf install -y libnccl libnccl-devel
+ENV PATH=/usr/local/cuda-13/bin:$PATH
+ENV BLAS_INCLUDE_DIRS=/usr/include/openblas
+ENV LAPACK_INCLUDE_DIRS=/usr/include/openblas
+ENV CGO_LDFLAGS="-L/usr/local/cuda-13/lib64 -L/usr/local/cuda-13/targets/x86_64-linux/lib/stubs"
+ARG PARALLEL
+WORKDIR /go/src/github.com/ollama/ollama
+COPY CMakeLists.txt CMakePresets.json .
+COPY ml/backend/ggml/ggml ml/backend/ggml/ggml
+COPY x/ml/backend/mlx x/ml/backend/mlx
+COPY go.mod go.sum .
+COPY MLX_VERSION .
+RUN curl -fsSL https://golang.org/dl/go$(awk '/^go/ { print $2 }' go.mod).linux-$(case $(uname -m) in x86_64) echo amd64 ;; aarch64) echo arm64 ;; esac).tar.gz | tar xz -C /usr/local
+ENV PATH=/usr/local/go/bin:$PATH
+RUN go mod download
+RUN --mount=type=cache,target=/root/.ccache \
+    cmake --preset 'MLX CUDA 13' -DBLAS_INCLUDE_DIRS=/usr/include/openblas -DLAPACK_INCLUDE_DIRS=/usr/include/openblas \
+        && cmake --build --parallel ${PARALLEL} --preset 'MLX CUDA 13' \
+        && cmake --install build --component MLX --strip --parallel ${PARALLEL}
 
 FROM base AS build
 WORKDIR /go/src/github.com/ollama/ollama
@@ -141,18 +165,21 @@ RUN curl -fsSL https://golang.org/dl/go$(awk '/^go/ { print $2 }' go.mod).linux-
 ENV PATH=/usr/local/go/bin:$PATH
 RUN go mod download
 COPY . .
+# Clone mlx-c headers for CGO (version from MLX_VERSION file)
+RUN git clone --depth 1 --branch "$(cat MLX_VERSION)" https://github.com/ml-explore/mlx-c.git build/_deps/mlx-c-src
 ARG GOFLAGS="'-ldflags=-w -s'"
 ENV CGO_ENABLED=1
-ARG CGO_CFLAGS
+ENV CGO_CFLAGS="-I/go/src/github.com/ollama/ollama/build/_deps/mlx-c-src"
 ARG CGO_CXXFLAGS
 RUN --mount=type=cache,target=/root/.cache/go-build \
-    go build -trimpath -buildmode=pie -o /bin/ollama .
+    go build -tags mlx -trimpath -buildmode=pie -o /bin/ollama .
 
 FROM --platform=linux/amd64 scratch AS amd64
 # COPY --from=cuda-11 dist/lib/ollama/ /lib/ollama/
 COPY --from=cuda-12 dist/lib/ollama /lib/ollama/
 COPY --from=cuda-13 dist/lib/ollama /lib/ollama/
 COPY --from=vulkan  dist/lib/ollama  /lib/ollama/
+COPY --from=mlx     /go/src/github.com/ollama/ollama/dist/lib/ollama /lib/ollama/
 
 FROM --platform=linux/arm64 scratch AS arm64
 # COPY --from=cuda-11 dist/lib/ollama/ /lib/ollama/
@@ -171,7 +198,7 @@ COPY --from=build /bin/ollama /bin/ollama
 
 FROM ubuntu:24.04
 RUN apt-get update \
-    && apt-get install -y ca-certificates libvulkan1 \
+    && apt-get install -y ca-certificates libvulkan1 libopenblas0 \
     && apt-get clean \
     && rm -rf /var/lib/apt/lists/*
 COPY --from=archive /bin /usr/bin

MLX_VERSION

@@ -0,0 +1 @@
+v0.4.1

README.md

@@ -48,7 +48,7 @@ ollama run gemma3
 
 ## Model library
 
-Ollama supports a list of models available on [ollama.com/library](https://ollama.com/library 'ollama model library')
+Ollama supports a list of models available on [ollama.com/library](https://ollama.com/library "ollama model library")
 
 Here are some example models that can be downloaded:
 
@@ -79,7 +79,7 @@ Here are some example models that can be downloaded:
 | Code Llama         | 7B         | 3.8GB | `ollama run codellama`           |
 | Llama 2 Uncensored | 7B         | 3.8GB | `ollama run llama2-uncensored`   |
 | LLaVA              | 7B         | 4.5GB | `ollama run llava`               |
-| Granite-3.3         | 8B         | 4.9GB | `ollama run granite3.3`          |
+| Granite-3.3        | 8B         | 4.9GB | `ollama run granite3.3`          |
 
 > [!NOTE]
 > You should have at least 8 GB of RAM available to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.
@@ -260,6 +260,38 @@ Finally, in a separate shell, run a model:
 ./ollama run llama3.2
 ```
 
+## Building with MLX (experimental)
+
+First build the MLX libraries:
+
+```shell
+cmake --preset MLX
+cmake --build --preset MLX --parallel
+cmake --install build --component MLX
+```
+
+When building with the `-tags mlx` flag, the main `ollama` binary includes MLX support for experimental features like image generation:
+
+```shell
+go build -tags mlx .
+```
+
+Finally, start the server:
+
+```
+./ollama serve
+```
+
+### Building MLX with CUDA
+
+When building with CUDA, use the preset "MLX CUDA 13" or "MLX CUDA 12" to enable CUDA with default architectures:
+
+```shell
+cmake --preset 'MLX CUDA 13'
+cmake --build --preset 'MLX CUDA 13' --parallel
+cmake --install build --component MLX
+```
+
 ## REST API
 
 Ollama has a REST API for running and managing models.
@@ -290,6 +322,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 
 ### Web & Desktop
 
+- [Onyx](https://github.com/onyx-dot-app/onyx)
 - [Open WebUI](https://github.com/open-webui/open-webui)
 - [SwiftChat (macOS with ReactNative)](https://github.com/aws-samples/swift-chat)
 - [Enchanted (macOS native)](https://github.com/AugustDev/enchanted)
@@ -421,7 +454,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [AppFlowy](https://github.com/AppFlowy-IO/AppFlowy) (AI collaborative workspace with Ollama, cross-platform and self-hostable)
 - [Lumina](https://github.com/cushydigit/lumina.git) (A lightweight, minimal React.js frontend for interacting with Ollama servers)
 - [Tiny Notepad](https://pypi.org/project/tiny-notepad) (A lightweight, notepad-like interface to chat with ollama available on PyPI)
-- [macLlama (macOS native)](https://github.com/hellotunamayo/macLlama) (A native macOS GUI application for interacting with Ollama models, featuring a chat interface.) 
+- [macLlama (macOS native)](https://github.com/hellotunamayo/macLlama) (A native macOS GUI application for interacting with Ollama models, featuring a chat interface.)
 - [GPTranslate](https://github.com/philberndt/GPTranslate) (A fast and lightweight, AI powered desktop translation application written with Rust and Tauri. Features real-time translation with OpenAI/Azure/Ollama.)
 - [ollama launcher](https://github.com/NGC13009/ollama-launcher) (A launcher for Ollama, aiming to provide users with convenient functions such as ollama server launching, management, or configuration.)
 - [ai-hub](https://github.com/Aj-Seven/ai-hub) (AI Hub supports multiple models via API keys and Chat support via Ollama API.)
@@ -493,7 +526,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 ### Database
 
 - [pgai](https://github.com/timescale/pgai) - PostgreSQL as a vector database (Create and search embeddings from Ollama models using pgvector)
-   - [Get started guide](https://github.com/timescale/pgai/blob/main/docs/vectorizer-quick-start.md)
+  - [Get started guide](https://github.com/timescale/pgai/blob/main/docs/vectorizer-quick-start.md)
 - [MindsDB](https://github.com/mindsdb/mindsdb/blob/staging/mindsdb/integrations/handlers/ollama_handler/README.md) (Connects Ollama models with nearly 200 data platforms and apps)
 - [chromem-go](https://github.com/philippgille/chromem-go/blob/v0.5.0/embed_ollama.go) with [example](https://github.com/philippgille/chromem-go/tree/v0.5.0/examples/rag-wikipedia-ollama)
 - [Kangaroo](https://github.com/dbkangaroo/kangaroo) (AI-powered SQL client and admin tool for popular databases)
@@ -636,6 +669,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [llama.cpp](https://github.com/ggml-org/llama.cpp) project founded by Georgi Gerganov.
 
 ### Observability
+
 - [Opik](https://www.comet.com/docs/opik/cookbook/ollama) is an open-source platform to debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards. Opik supports native integration to Ollama.
 - [Lunary](https://lunary.ai/docs/integrations/ollama) is the leading open-source LLM observability platform. It provides a variety of enterprise-grade features such as real-time analytics, prompt templates management, PII masking, and comprehensive agent tracing.
 - [OpenLIT](https://github.com/openlit/openlit) is an OpenTelemetry-native tool for monitoring Ollama Applications & GPUs using traces and metrics.
@@ -644,4 +678,5 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [MLflow Tracing](https://mlflow.org/docs/latest/llms/tracing/index.html#automatic-tracing) is an open source LLM observability tool with a convenient API to log and visualize traces, making it easy to debug and evaluate GenAI applications.
 
 ### Security
+
 - [Ollama Fortress](https://github.com/ParisNeo/ollama_proxy_server)

anthropic/anthropic.go

@@ -0,0 +1,778 @@
+package anthropic
+
+import (
+	"crypto/rand"
+	"encoding/base64"
+	"encoding/json"
+	"errors"
+	"fmt"
+	"log/slog"
+	"net/http"
+	"strings"
+	"time"
+
+	"github.com/ollama/ollama/api"
+)
+
+// Error types matching Anthropic API
+type Error struct {
+	Type    string `json:"type"`
+	Message string `json:"message"`
+}
+
+type ErrorResponse struct {
+	Type      string `json:"type"` // always "error"
+	Error     Error  `json:"error"`
+	RequestID string `json:"request_id,omitempty"`
+}
+
+// NewError creates a new ErrorResponse with the appropriate error type based on HTTP status code
+func NewError(code int, message string) ErrorResponse {
+	var etype string
+	switch code {
+	case http.StatusBadRequest:
+		etype = "invalid_request_error"
+	case http.StatusUnauthorized:
+		etype = "authentication_error"
+	case http.StatusForbidden:
+		etype = "permission_error"
+	case http.StatusNotFound:
+		etype = "not_found_error"
+	case http.StatusTooManyRequests:
+		etype = "rate_limit_error"
+	case http.StatusServiceUnavailable, 529:
+		etype = "overloaded_error"
+	default:
+		etype = "api_error"
+	}
+
+	return ErrorResponse{
+		Type:      "error",
+		Error:     Error{Type: etype, Message: message},
+		RequestID: generateID("req"),
+	}
+}
+
+// Request types
+
+// MessagesRequest represents an Anthropic Messages API request
+type MessagesRequest struct {
+	Model         string          `json:"model"`
+	MaxTokens     int             `json:"max_tokens"`
+	Messages      []MessageParam  `json:"messages"`
+	System        any             `json:"system,omitempty"` // string or []ContentBlock
+	Stream        bool            `json:"stream,omitempty"`
+	Temperature   *float64        `json:"temperature,omitempty"`
+	TopP          *float64        `json:"top_p,omitempty"`
+	TopK          *int            `json:"top_k,omitempty"`
+	StopSequences []string        `json:"stop_sequences,omitempty"`
+	Tools         []Tool          `json:"tools,omitempty"`
+	ToolChoice    *ToolChoice     `json:"tool_choice,omitempty"`
+	Thinking      *ThinkingConfig `json:"thinking,omitempty"`
+	Metadata      *Metadata       `json:"metadata,omitempty"`
+}
+
+// MessageParam represents a message in the request
+type MessageParam struct {
+	Role    string `json:"role"`    // "user" or "assistant"
+	Content any    `json:"content"` // string or []ContentBlock
+}
+
+// ContentBlock represents a content block in a message.
+// Text and Thinking use pointers so they serialize as the field being present (even if empty)
+// only when set, which is required for SDK streaming accumulation.
+type ContentBlock struct {
+	Type string `json:"type"` // text, image, tool_use, tool_result, thinking
+
+	// For text blocks - pointer so field only appears when set (SDK requires it for accumulation)
+	Text *string `json:"text,omitempty"`
+
+	// For image blocks
+	Source *ImageSource `json:"source,omitempty"`
+
+	// For tool_use blocks
+	ID    string `json:"id,omitempty"`
+	Name  string `json:"name,omitempty"`
+	Input any    `json:"input,omitempty"`
+
+	// For tool_result blocks
+	ToolUseID string `json:"tool_use_id,omitempty"`
+	Content   any    `json:"content,omitempty"` // string or []ContentBlock
+	IsError   bool   `json:"is_error,omitempty"`
+
+	// For thinking blocks - pointer so field only appears when set (SDK requires it for accumulation)
+	Thinking  *string `json:"thinking,omitempty"`
+	Signature string  `json:"signature,omitempty"`
+}
+
+// ImageSource represents the source of an image
+type ImageSource struct {
+	Type      string `json:"type"` // "base64" or "url"
+	MediaType string `json:"media_type,omitempty"`
+	Data      string `json:"data,omitempty"`
+	URL       string `json:"url,omitempty"`
+}
+
+// Tool represents a tool definition
+type Tool struct {
+	Type        string          `json:"type,omitempty"` // "custom" for user-defined tools
+	Name        string          `json:"name"`
+	Description string          `json:"description,omitempty"`
+	InputSchema json.RawMessage `json:"input_schema,omitempty"`
+}
+
+// ToolChoice controls how the model uses tools
+type ToolChoice struct {
+	Type                   string `json:"type"` // "auto", "any", "tool", "none"
+	Name                   string `json:"name,omitempty"`
+	DisableParallelToolUse bool   `json:"disable_parallel_tool_use,omitempty"`
+}
+
+// ThinkingConfig controls extended thinking
+type ThinkingConfig struct {
+	Type         string `json:"type"` // "enabled" or "disabled"
+	BudgetTokens int    `json:"budget_tokens,omitempty"`
+}
+
+// Metadata for the request
+type Metadata struct {
+	UserID string `json:"user_id,omitempty"`
+}
+
+// Response types
+
+// MessagesResponse represents an Anthropic Messages API response
+type MessagesResponse struct {
+	ID           string         `json:"id"`
+	Type         string         `json:"type"` // "message"
+	Role         string         `json:"role"` // "assistant"
+	Model        string         `json:"model"`
+	Content      []ContentBlock `json:"content"`
+	StopReason   string         `json:"stop_reason,omitempty"`
+	StopSequence string         `json:"stop_sequence,omitempty"`
+	Usage        Usage          `json:"usage"`
+}
+
+// Usage contains token usage information
+type Usage struct {
+	InputTokens  int `json:"input_tokens"`
+	OutputTokens int `json:"output_tokens"`
+}
+
+// Streaming event types
+
+// MessageStartEvent is sent at the start of streaming
+type MessageStartEvent struct {
+	Type    string           `json:"type"` // "message_start"
+	Message MessagesResponse `json:"message"`
+}
+
+// ContentBlockStartEvent signals the start of a content block
+type ContentBlockStartEvent struct {
+	Type         string       `json:"type"` // "content_block_start"
+	Index        int          `json:"index"`
+	ContentBlock ContentBlock `json:"content_block"`
+}
+
+// ContentBlockDeltaEvent contains incremental content updates
+type ContentBlockDeltaEvent struct {
+	Type  string `json:"type"` // "content_block_delta"
+	Index int    `json:"index"`
+	Delta Delta  `json:"delta"`
+}
+
+// Delta represents an incremental update
+type Delta struct {
+	Type        string `json:"type"` // "text_delta", "input_json_delta", "thinking_delta", "signature_delta"
+	Text        string `json:"text,omitempty"`
+	PartialJSON string `json:"partial_json,omitempty"`
+	Thinking    string `json:"thinking,omitempty"`
+	Signature   string `json:"signature,omitempty"`
+}
+
+// ContentBlockStopEvent signals the end of a content block
+type ContentBlockStopEvent struct {
+	Type  string `json:"type"` // "content_block_stop"
+	Index int    `json:"index"`
+}
+
+// MessageDeltaEvent contains updates to the message
+type MessageDeltaEvent struct {
+	Type  string       `json:"type"` // "message_delta"
+	Delta MessageDelta `json:"delta"`
+	Usage DeltaUsage   `json:"usage"`
+}
+
+// MessageDelta contains stop information
+type MessageDelta struct {
+	StopReason   string `json:"stop_reason,omitempty"`
+	StopSequence string `json:"stop_sequence,omitempty"`
+}
+
+// DeltaUsage contains cumulative token usage
+type DeltaUsage struct {
+	OutputTokens int `json:"output_tokens"`
+}
+
+// MessageStopEvent signals the end of the message
+type MessageStopEvent struct {
+	Type string `json:"type"` // "message_stop"
+}
+
+// PingEvent is a keepalive event
+type PingEvent struct {
+	Type string `json:"type"` // "ping"
+}
+
+// StreamErrorEvent is an error during streaming
+type StreamErrorEvent struct {
+	Type  string `json:"type"` // "error"
+	Error Error  `json:"error"`
+}
+
+// FromMessagesRequest converts an Anthropic MessagesRequest to an Ollama api.ChatRequest
+func FromMessagesRequest(r MessagesRequest) (*api.ChatRequest, error) {
+	var messages []api.Message
+
+	if r.System != nil {
+		switch sys := r.System.(type) {
+		case string:
+			if sys != "" {
+				messages = append(messages, api.Message{Role: "system", Content: sys})
+			}
+		case []any:
+			// System can be an array of content blocks
+			var content strings.Builder
+			for _, block := range sys {
+				if blockMap, ok := block.(map[string]any); ok {
+					if blockMap["type"] == "text" {
+						if text, ok := blockMap["text"].(string); ok {
+							content.WriteString(text)
+						}
+					}
+				}
+			}
+			if content.Len() > 0 {
+				messages = append(messages, api.Message{Role: "system", Content: content.String()})
+			}
+		}
+	}
+
+	for _, msg := range r.Messages {
+		converted, err := convertMessage(msg)
+		if err != nil {
+			return nil, err
+		}
+		messages = append(messages, converted...)
+	}
+
+	options := make(map[string]any)
+
+	options["num_predict"] = r.MaxTokens
+
+	if r.Temperature != nil {
+		options["temperature"] = *r.Temperature
+	}
+
+	if r.TopP != nil {
+		options["top_p"] = *r.TopP
+	}
+
+	if r.TopK != nil {
+		options["top_k"] = *r.TopK
+	}
+
+	if len(r.StopSequences) > 0 {
+		options["stop"] = r.StopSequences
+	}
+
+	var tools api.Tools
+	for _, t := range r.Tools {
+		tool, err := convertTool(t)
+		if err != nil {
+			return nil, err
+		}
+		tools = append(tools, tool)
+	}
+
+	var think *api.ThinkValue
+	if r.Thinking != nil && r.Thinking.Type == "enabled" {
+		think = &api.ThinkValue{Value: true}
+	}
+
+	stream := r.Stream
+
+	return &api.ChatRequest{
+		Model:    r.Model,
+		Messages: messages,
+		Options:  options,
+		Stream:   &stream,
+		Tools:    tools,
+		Think:    think,
+	}, nil
+}
+
+// convertMessage converts an Anthropic MessageParam to Ollama api.Message(s)
+func convertMessage(msg MessageParam) ([]api.Message, error) {
+	var messages []api.Message
+	role := strings.ToLower(msg.Role)
+
+	switch content := msg.Content.(type) {
+	case string:
+		messages = append(messages, api.Message{Role: role, Content: content})
+
+	case []any:
+		var textContent strings.Builder
+		var images []api.ImageData
+		var toolCalls []api.ToolCall
+		var thinking string
+		var toolResults []api.Message
+
+		for _, block := range content {
+			blockMap, ok := block.(map[string]any)
+			if !ok {
+				return nil, errors.New("invalid content block format")
+			}
+
+			blockType, _ := blockMap["type"].(string)
+
+			switch blockType {
+			case "text":
+				if text, ok := blockMap["text"].(string); ok {
+					textContent.WriteString(text)
+				}
+
+			case "image":
+				source, ok := blockMap["source"].(map[string]any)
+				if !ok {
+					return nil, errors.New("invalid image source")
+				}
+
+				sourceType, _ := source["type"].(string)
+				if sourceType == "base64" {
+					data, _ := source["data"].(string)
+					decoded, err := base64.StdEncoding.DecodeString(data)
+					if err != nil {
+						return nil, fmt.Errorf("invalid base64 image data: %w", err)
+					}
+					images = append(images, decoded)
+				} else {
+					return nil, fmt.Errorf("invalid image source type: %s. Only base64 images are supported.", sourceType)
+				}
+				// URL images would need to be fetched - skip for now
+
+			case "tool_use":
+				id, ok := blockMap["id"].(string)
+				if !ok {
+					return nil, errors.New("tool_use block missing required 'id' field")
+				}
+				name, ok := blockMap["name"].(string)
+				if !ok {
+					return nil, errors.New("tool_use block missing required 'name' field")
+				}
+				tc := api.ToolCall{
+					ID: id,
+					Function: api.ToolCallFunction{
+						Name: name,
+					},
+				}
+				if input, ok := blockMap["input"].(map[string]any); ok {
+					tc.Function.Arguments = mapToArgs(input)
+				}
+				toolCalls = append(toolCalls, tc)
+
+			case "tool_result":
+				toolUseID, _ := blockMap["tool_use_id"].(string)
+				var resultContent string
+
+				switch c := blockMap["content"].(type) {
+				case string:
+					resultContent = c
+				case []any:
+					for _, cb := range c {
+						if cbMap, ok := cb.(map[string]any); ok {
+							if cbMap["type"] == "text" {
+								if text, ok := cbMap["text"].(string); ok {
+									resultContent += text
+								}
+							}
+						}
+					}
+				}
+
+				toolResults = append(toolResults, api.Message{
+					Role:       "tool",
+					Content:    resultContent,
+					ToolCallID: toolUseID,
+				})
+
+			case "thinking":
+				if t, ok := blockMap["thinking"].(string); ok {
+					thinking = t
+				}
+			}
+		}
+
+		if textContent.Len() > 0 || len(images) > 0 || len(toolCalls) > 0 || thinking != "" {
+			m := api.Message{
+				Role:      role,
+				Content:   textContent.String(),
+				Images:    images,
+				ToolCalls: toolCalls,
+				Thinking:  thinking,
+			}
+			messages = append(messages, m)
+		}
+
+		// Add tool results as separate messages
+		messages = append(messages, toolResults...)
+
+	default:
+		return nil, fmt.Errorf("invalid message content type: %T", content)
+	}
+
+	return messages, nil
+}
+
+// convertTool converts an Anthropic Tool to an Ollama api.Tool
+func convertTool(t Tool) (api.Tool, error) {
+	var params api.ToolFunctionParameters
+	if len(t.InputSchema) > 0 {
+		if err := json.Unmarshal(t.InputSchema, &params); err != nil {
+			return api.Tool{}, fmt.Errorf("invalid input_schema for tool %q: %w", t.Name, err)
+		}
+	}
+
+	return api.Tool{
+		Type: "function",
+		Function: api.ToolFunction{
+			Name:        t.Name,
+			Description: t.Description,
+			Parameters:  params,
+		},
+	}, nil
+}
+
+// ToMessagesResponse converts an Ollama api.ChatResponse to an Anthropic MessagesResponse
+func ToMessagesResponse(id string, r api.ChatResponse) MessagesResponse {
+	var content []ContentBlock
+
+	if r.Message.Thinking != "" {
+		content = append(content, ContentBlock{
+			Type:     "thinking",
+			Thinking: ptr(r.Message.Thinking),
+		})
+	}
+
+	if r.Message.Content != "" {
+		content = append(content, ContentBlock{
+			Type: "text",
+			Text: ptr(r.Message.Content),
+		})
+	}
+
+	for _, tc := range r.Message.ToolCalls {
+		content = append(content, ContentBlock{
+			Type:  "tool_use",
+			ID:    tc.ID,
+			Name:  tc.Function.Name,
+			Input: tc.Function.Arguments,
+		})
+	}
+
+	stopReason := mapStopReason(r.DoneReason, len(r.Message.ToolCalls) > 0)
+
+	return MessagesResponse{
+		ID:         id,
+		Type:       "message",
+		Role:       "assistant",
+		Model:      r.Model,
+		Content:    content,
+		StopReason: stopReason,
+		Usage: Usage{
+			InputTokens:  r.Metrics.PromptEvalCount,
+			OutputTokens: r.Metrics.EvalCount,
+		},
+	}
+}
+
+// mapStopReason converts Ollama done_reason to Anthropic stop_reason
+func mapStopReason(reason string, hasToolCalls bool) string {
+	if hasToolCalls {
+		return "tool_use"
+	}
+
+	switch reason {
+	case "stop":
+		return "end_turn"
+	case "length":
+		return "max_tokens"
+	default:
+		if reason != "" {
+			return "stop_sequence"
+		}
+		return ""
+	}
+}
+
+// StreamConverter manages state for converting Ollama streaming responses to Anthropic format
+type StreamConverter struct {
+	ID              string
+	Model           string
+	firstWrite      bool
+	contentIndex    int
+	inputTokens     int
+	outputTokens    int
+	thinkingStarted bool
+	thinkingDone    bool
+	textStarted     bool
+	toolCallsSent   map[string]bool
+}
+
+func NewStreamConverter(id, model string) *StreamConverter {
+	return &StreamConverter{
+		ID:            id,
+		Model:         model,
+		firstWrite:    true,
+		toolCallsSent: make(map[string]bool),
+	}
+}
+
+// StreamEvent represents a streaming event to be sent to the client
+type StreamEvent struct {
+	Event string
+	Data  any
+}
+
+// Process converts an Ollama ChatResponse to Anthropic streaming events
+func (c *StreamConverter) Process(r api.ChatResponse) []StreamEvent {
+	var events []StreamEvent
+
+	if c.firstWrite {
+		c.firstWrite = false
+		c.inputTokens = r.Metrics.PromptEvalCount
+
+		events = append(events, StreamEvent{
+			Event: "message_start",
+			Data: MessageStartEvent{
+				Type: "message_start",
+				Message: MessagesResponse{
+					ID:      c.ID,
+					Type:    "message",
+					Role:    "assistant",
+					Model:   c.Model,
+					Content: []ContentBlock{},
+					Usage: Usage{
+						InputTokens:  c.inputTokens,
+						OutputTokens: 0,
+					},
+				},
+			},
+		})
+	}
+
+	if r.Message.Thinking != "" && !c.thinkingDone {
+		if !c.thinkingStarted {
+			c.thinkingStarted = true
+			events = append(events, StreamEvent{
+				Event: "content_block_start",
+				Data: ContentBlockStartEvent{
+					Type:  "content_block_start",
+					Index: c.contentIndex,
+					ContentBlock: ContentBlock{
+						Type:     "thinking",
+						Thinking: ptr(""),
+					},
+				},
+			})
+		}
+
+		events = append(events, StreamEvent{
+			Event: "content_block_delta",
+			Data: ContentBlockDeltaEvent{
+				Type:  "content_block_delta",
+				Index: c.contentIndex,
+				Delta: Delta{
+					Type:     "thinking_delta",
+					Thinking: r.Message.Thinking,
+				},
+			},
+		})
+	}
+
+	if r.Message.Content != "" {
+		if c.thinkingStarted && !c.thinkingDone {
+			c.thinkingDone = true
+			events = append(events, StreamEvent{
+				Event: "content_block_stop",
+				Data: ContentBlockStopEvent{
+					Type:  "content_block_stop",
+					Index: c.contentIndex,
+				},
+			})
+			c.contentIndex++
+		}
+
+		if !c.textStarted {
+			c.textStarted = true
+			events = append(events, StreamEvent{
+				Event: "content_block_start",
+				Data: ContentBlockStartEvent{
+					Type:  "content_block_start",
+					Index: c.contentIndex,
+					ContentBlock: ContentBlock{
+						Type: "text",
+						Text: ptr(""),
+					},
+				},
+			})
+		}
+
+		events = append(events, StreamEvent{
+			Event: "content_block_delta",
+			Data: ContentBlockDeltaEvent{
+				Type:  "content_block_delta",
+				Index: c.contentIndex,
+				Delta: Delta{
+					Type: "text_delta",
+					Text: r.Message.Content,
+				},
+			},
+		})
+	}
+
+	for _, tc := range r.Message.ToolCalls {
+		if c.toolCallsSent[tc.ID] {
+			continue
+		}
+
+		if c.textStarted {
+			events = append(events, StreamEvent{
+				Event: "content_block_stop",
+				Data: ContentBlockStopEvent{
+					Type:  "content_block_stop",
+					Index: c.contentIndex,
+				},
+			})
+			c.contentIndex++
+			c.textStarted = false
+		}
+
+		argsJSON, err := json.Marshal(tc.Function.Arguments)
+		if err != nil {
+			slog.Error("failed to marshal tool arguments", "error", err, "tool_id", tc.ID)
+			continue
+		}
+
+		events = append(events, StreamEvent{
+			Event: "content_block_start",
+			Data: ContentBlockStartEvent{
+				Type:  "content_block_start",
+				Index: c.contentIndex,
+				ContentBlock: ContentBlock{
+					Type:  "tool_use",
+					ID:    tc.ID,
+					Name:  tc.Function.Name,
+					Input: map[string]any{},
+				},
+			},
+		})
+
+		events = append(events, StreamEvent{
+			Event: "content_block_delta",
+			Data: ContentBlockDeltaEvent{
+				Type:  "content_block_delta",
+				Index: c.contentIndex,
+				Delta: Delta{
+					Type:        "input_json_delta",
+					PartialJSON: string(argsJSON),
+				},
+			},
+		})
+
+		events = append(events, StreamEvent{
+			Event: "content_block_stop",
+			Data: ContentBlockStopEvent{
+				Type:  "content_block_stop",
+				Index: c.contentIndex,
+			},
+		})
+
+		c.toolCallsSent[tc.ID] = true
+		c.contentIndex++
+	}
+
+	if r.Done {
+		if c.textStarted {
+			events = append(events, StreamEvent{
+				Event: "content_block_stop",
+				Data: ContentBlockStopEvent{
+					Type:  "content_block_stop",
+					Index: c.contentIndex,
+				},
+			})
+		} else if c.thinkingStarted && !c.thinkingDone {
+			events = append(events, StreamEvent{
+				Event: "content_block_stop",
+				Data: ContentBlockStopEvent{
+					Type:  "content_block_stop",
+					Index: c.contentIndex,
+				},
+			})
+		}
+
+		c.outputTokens = r.Metrics.EvalCount
+		stopReason := mapStopReason(r.DoneReason, len(c.toolCallsSent) > 0)
+
+		events = append(events, StreamEvent{
+			Event: "message_delta",
+			Data: MessageDeltaEvent{
+				Type: "message_delta",
+				Delta: MessageDelta{
+					StopReason: stopReason,
+				},
+				Usage: DeltaUsage{
+					OutputTokens: c.outputTokens,
+				},
+			},
+		})
+
+		events = append(events, StreamEvent{
+			Event: "message_stop",
+			Data: MessageStopEvent{
+				Type: "message_stop",
+			},
+		})
+	}
+
+	return events
+}
+
+// generateID generates a unique ID with the given prefix using crypto/rand
+func generateID(prefix string) string {
+	b := make([]byte, 12)
+	if _, err := rand.Read(b); err != nil {
+		// Fallback to time-based ID if crypto/rand fails
+		return fmt.Sprintf("%s_%d", prefix, time.Now().UnixNano())
+	}
+	return fmt.Sprintf("%s_%x", prefix, b)
+}
+
+// GenerateMessageID generates a unique message ID
+func GenerateMessageID() string {
+	return generateID("msg")
+}
+
+// ptr returns a pointer to the given string value
+func ptr(s string) *string {
+	return &s
+}
+
+// mapToArgs converts a map to ToolCallFunctionArguments
+func mapToArgs(m map[string]any) api.ToolCallFunctionArguments {
+	args := api.NewToolCallFunctionArguments()
+	for k, v := range m {
+		args.Set(k, v)
+	}
+	return args
+}

anthropic/anthropic_test.go

@@ -0,0 +1,953 @@
+package anthropic
+
+import (
+	"encoding/base64"
+	"encoding/json"
+	"testing"
+
+	"github.com/google/go-cmp/cmp"
+
+	"github.com/ollama/ollama/api"
+)
+
+const (
+	testImage = `iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNk+A8AAQUBAScY42YAAAAASUVORK5CYII=`
+)
+
+// testArgs creates ToolCallFunctionArguments from a map (convenience function for tests)
+func testArgs(m map[string]any) api.ToolCallFunctionArguments {
+	args := api.NewToolCallFunctionArguments()
+	for k, v := range m {
+		args.Set(k, v)
+	}
+	return args
+}
+
+func TestFromMessagesRequest_Basic(t *testing.T) {
+	req := MessagesRequest{
+		Model:     "test-model",
+		MaxTokens: 1024,
+		Messages: []MessageParam{
+			{Role: "user", Content: "Hello"},
+		},
+	}
+
+	result, err := FromMessagesRequest(req)
+	if err != nil {
+		t.Fatalf("unexpected error: %v", err)
+	}
+
+	if result.Model != "test-model" {
+		t.Errorf("expected model 'test-model', got %q", result.Model)
+	}
+
+	if len(result.Messages) != 1 {
+		t.Fatalf("expected 1 message, got %d", len(result.Messages))
+	}
+
+	if result.Messages[0].Role != "user" || result.Messages[0].Content != "Hello" {
+		t.Errorf("unexpected message: %+v", result.Messages[0])
+	}
+
+	if numPredict, ok := result.Options["num_predict"].(int); !ok || numPredict != 1024 {
+		t.Errorf("expected num_predict 1024, got %v", result.Options["num_predict"])
+	}
+}
+
+func TestFromMessagesRequest_WithSystemPrompt(t *testing.T) {
+	req := MessagesRequest{
+		Model:     "test-model",
+		MaxTokens: 1024,
+		System:    "You are a helpful assistant.",
+		Messages: []MessageParam{
+			{Role: "user", Content: "Hello"},
+		},
+	}
+
+	result, err := FromMessagesRequest(req)
+	if err != nil {
+		t.Fatalf("unexpected error: %v", err)
+	}
+
+	if len(result.Messages) != 2 {
+		t.Fatalf("expected 2 messages, got %d", len(result.Messages))
+	}
+
+	if result.Messages[0].Role != "system" || result.Messages[0].Content != "You are a helpful assistant." {
+		t.Errorf("unexpected system message: %+v", result.Messages[0])
+	}
+}
+
+func TestFromMessagesRequest_WithSystemPromptArray(t *testing.T) {
+	req := MessagesRequest{
+		Model:     "test-model",
+		MaxTokens: 1024,
+		System: []any{
+			map[string]any{"type": "text", "text": "You are helpful."},
+			map[string]any{"type": "text", "text": " Be concise."},
+		},
+		Messages: []MessageParam{
+			{Role: "user", Content: "Hello"},
+		},
+	}
+
+	result, err := FromMessagesRequest(req)
+	if err != nil {
+		t.Fatalf("unexpected error: %v", err)
+	}
+
+	if len(result.Messages) != 2 {
+		t.Fatalf("expected 2 messages, got %d", len(result.Messages))
+	}
+
+	if result.Messages[0].Content != "You are helpful. Be concise." {
+		t.Errorf("unexpected system message content: %q", result.Messages[0].Content)
+	}
+}
+
+func TestFromMessagesRequest_WithOptions(t *testing.T) {
+	temp := 0.7
+	topP := 0.9
+	topK := 40
+	req := MessagesRequest{
+		Model:         "test-model",
+		MaxTokens:     2048,
+		Messages:      []MessageParam{{Role: "user", Content: "Hello"}},
+		Temperature:   &temp,
+		TopP:          &topP,
+		TopK:          &topK,
+		StopSequences: []string{"\n", "END"},
+	}
+
+	result, err := FromMessagesRequest(req)
+	if err != nil {
+		t.Fatalf("unexpected error: %v", err)
+	}
+
+	if result.Options["temperature"] != 0.7 {
+		t.Errorf("expected temperature 0.7, got %v", result.Options["temperature"])
+	}
+	if result.Options["top_p"] != 0.9 {
+		t.Errorf("expected top_p 0.9, got %v", result.Options["top_p"])
+	}
+	if result.Options["top_k"] != 40 {
+		t.Errorf("expected top_k 40, got %v", result.Options["top_k"])
+	}
+	if diff := cmp.Diff([]string{"\n", "END"}, result.Options["stop"]); diff != "" {
+		t.Errorf("stop sequences mismatch: %s", diff)
+	}
+}
+
+func TestFromMessagesRequest_WithImage(t *testing.T) {
+	imgData, _ := base64.StdEncoding.DecodeString(testImage)
+
+	req := MessagesRequest{
+		Model:     "test-model",
+		MaxTokens: 1024,
+		Messages: []MessageParam{
+			{
+				Role: "user",
+				Content: []any{
+					map[string]any{"type": "text", "text": "What's in this image?"},
+					map[string]any{
+						"type": "image",
+						"source": map[string]any{
+							"type":       "base64",
+							"media_type": "image/png",
+							"data":       testImage,
+						},
+					},
+				},
+			},
+		},
+	}
+
+	result, err := FromMessagesRequest(req)
+	if err != nil {
+		t.Fatalf("unexpected error: %v", err)
+	}
+
+	if len(result.Messages) != 1 {
+		t.Fatalf("expected 1 message, got %d", len(result.Messages))
+	}
+
+	if result.Messages[0].Content != "What's in this image?" {
+		t.Errorf("expected content 'What's in this image?', got %q", result.Messages[0].Content)
+	}
+
+	if len(result.Messages[0].Images) != 1 {
+		t.Fatalf("expected 1 image, got %d", len(result.Messages[0].Images))
+	}
+
+	if string(result.Messages[0].Images[0]) != string(imgData) {
+		t.Error("image data mismatch")
+	}
+}
+
+func TestFromMessagesRequest_WithToolUse(t *testing.T) {
+	req := MessagesRequest{
+		Model:     "test-model",
+		MaxTokens: 1024,
+		Messages: []MessageParam{
+			{Role: "user", Content: "What's the weather in Paris?"},
+			{
+				Role: "assistant",
+				Content: []any{
+					map[string]any{
+						"type":  "tool_use",
+						"id":    "call_123",
+						"name":  "get_weather",
+						"input": map[string]any{"location": "Paris"},
+					},
+				},
+			},
+		},
+	}
+
+	result, err := FromMessagesRequest(req)
+	if err != nil {
+		t.Fatalf("unexpected error: %v", err)
+	}
+
+	if len(result.Messages) != 2 {
+		t.Fatalf("expected 2 messages, got %d", len(result.Messages))
+	}
+
+	if len(result.Messages[1].ToolCalls) != 1 {
+		t.Fatalf("expected 1 tool call, got %d", len(result.Messages[1].ToolCalls))
+	}
+
+	tc := result.Messages[1].ToolCalls[0]
+	if tc.ID != "call_123" {
+		t.Errorf("expected tool call ID 'call_123', got %q", tc.ID)
+	}
+	if tc.Function.Name != "get_weather" {
+		t.Errorf("expected tool name 'get_weather', got %q", tc.Function.Name)
+	}
+}
+
+func TestFromMessagesRequest_WithToolResult(t *testing.T) {
+	req := MessagesRequest{
+		Model:     "test-model",
+		MaxTokens: 1024,
+		Messages: []MessageParam{
+			{
+				Role: "user",
+				Content: []any{
+					map[string]any{
+						"type":        "tool_result",
+						"tool_use_id": "call_123",
+						"content":     "The weather in Paris is sunny, 22°C",
+					},
+				},
+			},
+		},
+	}
+
+	result, err := FromMessagesRequest(req)
+	if err != nil {
+		t.Fatalf("unexpected error: %v", err)
+	}
+
+	if len(result.Messages) != 1 {
+		t.Fatalf("expected 1 message, got %d", len(result.Messages))
+	}
+
+	msg := result.Messages[0]
+	if msg.Role != "tool" {
+		t.Errorf("expected role 'tool', got %q", msg.Role)
+	}
+	if msg.ToolCallID != "call_123" {
+		t.Errorf("expected tool_call_id 'call_123', got %q", msg.ToolCallID)
+	}
+	if msg.Content != "The weather in Paris is sunny, 22°C" {
+		t.Errorf("unexpected content: %q", msg.Content)
+	}
+}
+
+func TestFromMessagesRequest_WithTools(t *testing.T) {
+	req := MessagesRequest{
+		Model:     "test-model",
+		MaxTokens: 1024,
+		Messages:  []MessageParam{{Role: "user", Content: "Hello"}},
+		Tools: []Tool{
+			{
+				Name:        "get_weather",
+				Description: "Get current weather",
+				InputSchema: json.RawMessage(`{"type":"object","properties":{"location":{"type":"string"}},"required":["location"]}`),
+			},
+		},
+	}
+
+	result, err := FromMessagesRequest(req)
+	if err != nil {
+		t.Fatalf("unexpected error: %v", err)
+	}
+
+	if len(result.Tools) != 1 {
+		t.Fatalf("expected 1 tool, got %d", len(result.Tools))
+	}
+
+	tool := result.Tools[0]
+	if tool.Type != "function" {
+		t.Errorf("expected type 'function', got %q", tool.Type)
+	}
+	if tool.Function.Name != "get_weather" {
+		t.Errorf("expected name 'get_weather', got %q", tool.Function.Name)
+	}
+	if tool.Function.Description != "Get current weather" {
+		t.Errorf("expected description 'Get current weather', got %q", tool.Function.Description)
+	}
+}
+
+func TestFromMessagesRequest_WithThinking(t *testing.T) {
+	req := MessagesRequest{
+		Model:     "test-model",
+		MaxTokens: 1024,
+		Messages:  []MessageParam{{Role: "user", Content: "Hello"}},
+		Thinking:  &ThinkingConfig{Type: "enabled", BudgetTokens: 1000},
+	}
+
+	result, err := FromMessagesRequest(req)
+	if err != nil {
+		t.Fatalf("unexpected error: %v", err)
+	}
+
+	if result.Think == nil {
+		t.Fatal("expected Think to be set")
+	}
+	if v, ok := result.Think.Value.(bool); !ok || !v {
+		t.Errorf("expected Think.Value to be true, got %v", result.Think.Value)
+	}
+}
+
+// TestFromMessagesRequest_ThinkingOnlyBlock verifies that messages containing only
+// a thinking block (no text, images, or tool calls) are preserved and not dropped.
+func TestFromMessagesRequest_ThinkingOnlyBlock(t *testing.T) {
+	req := MessagesRequest{
+		Model:     "test-model",
+		MaxTokens: 1024,
+		Messages: []MessageParam{
+			{Role: "user", Content: "Hello"},
+			{
+				Role: "assistant",
+				Content: []any{
+					map[string]any{
+						"type":     "thinking",
+						"thinking": "Let me think about this...",
+					},
+				},
+			},
+		},
+	}
+
+	result, err := FromMessagesRequest(req)
+	if err != nil {
+		t.Fatalf("unexpected error: %v", err)
+	}
+
+	if len(result.Messages) != 2 {
+		t.Fatalf("expected 2 messages, got %d", len(result.Messages))
+	}
+
+	assistantMsg := result.Messages[1]
+	if assistantMsg.Thinking != "Let me think about this..." {
+		t.Errorf("expected thinking content, got %q", assistantMsg.Thinking)
+	}
+}
+
+func TestFromMessagesRequest_ToolUseMissingID(t *testing.T) {
+	req := MessagesRequest{
+		Model:     "test-model",
+		MaxTokens: 1024,
+		Messages: []MessageParam{
+			{
+				Role: "assistant",
+				Content: []any{
+					map[string]any{
+						"type": "tool_use",
+						"name": "get_weather",
+					},
+				},
+			},
+		},
+	}
+
+	_, err := FromMessagesRequest(req)
+	if err == nil {
+		t.Fatal("expected error for missing tool_use id")
+	}
+	if err.Error() != "tool_use block missing required 'id' field" {
+		t.Errorf("unexpected error message: %v", err)
+	}
+}
+
+func TestFromMessagesRequest_ToolUseMissingName(t *testing.T) {
+	req := MessagesRequest{
+		Model:     "test-model",
+		MaxTokens: 1024,
+		Messages: []MessageParam{
+			{
+				Role: "assistant",
+				Content: []any{
+					map[string]any{
+						"type": "tool_use",
+						"id":   "call_123",
+					},
+				},
+			},
+		},
+	}
+
+	_, err := FromMessagesRequest(req)
+	if err == nil {
+		t.Fatal("expected error for missing tool_use name")
+	}
+	if err.Error() != "tool_use block missing required 'name' field" {
+		t.Errorf("unexpected error message: %v", err)
+	}
+}
+
+func TestFromMessagesRequest_InvalidToolSchema(t *testing.T) {
+	req := MessagesRequest{
+		Model:     "test-model",
+		MaxTokens: 1024,
+		Messages:  []MessageParam{{Role: "user", Content: "Hello"}},
+		Tools: []Tool{
+			{
+				Name:        "bad_tool",
+				InputSchema: json.RawMessage(`{invalid json`),
+			},
+		},
+	}
+
+	_, err := FromMessagesRequest(req)
+	if err == nil {
+		t.Fatal("expected error for invalid tool schema")
+	}
+}
+
+func TestToMessagesResponse_Basic(t *testing.T) {
+	resp := api.ChatResponse{
+		Model: "test-model",
+		Message: api.Message{
+			Role:    "assistant",
+			Content: "Hello there!",
+		},
+		Done:       true,
+		DoneReason: "stop",
+		Metrics: api.Metrics{
+			PromptEvalCount: 10,
+			EvalCount:       5,
+		},
+	}
+
+	result := ToMessagesResponse("msg_123", resp)
+
+	if result.ID != "msg_123" {
+		t.Errorf("expected ID 'msg_123', got %q", result.ID)
+	}
+	if result.Type != "message" {
+		t.Errorf("expected type 'message', got %q", result.Type)
+	}
+	if result.Role != "assistant" {
+		t.Errorf("expected role 'assistant', got %q", result.Role)
+	}
+	if len(result.Content) != 1 {
+		t.Fatalf("expected 1 content block, got %d", len(result.Content))
+	}
+	if result.Content[0].Type != "text" || result.Content[0].Text == nil || *result.Content[0].Text != "Hello there!" {
+		t.Errorf("unexpected content: %+v", result.Content[0])
+	}
+	if result.StopReason != "end_turn" {
+		t.Errorf("expected stop_reason 'end_turn', got %q", result.StopReason)
+	}
+	if result.Usage.InputTokens != 10 || result.Usage.OutputTokens != 5 {
+		t.Errorf("unexpected usage: %+v", result.Usage)
+	}
+}
+
+func TestToMessagesResponse_WithToolCalls(t *testing.T) {
+	resp := api.ChatResponse{
+		Model: "test-model",
+		Message: api.Message{
+			Role: "assistant",
+			ToolCalls: []api.ToolCall{
+				{
+					ID: "call_123",
+					Function: api.ToolCallFunction{
+						Name:      "get_weather",
+						Arguments: testArgs(map[string]any{"location": "Paris"}),
+					},
+				},
+			},
+		},
+		Done:       true,
+		DoneReason: "stop",
+	}
+
+	result := ToMessagesResponse("msg_123", resp)
+
+	if len(result.Content) != 1 {
+		t.Fatalf("expected 1 content block, got %d", len(result.Content))
+	}
+	if result.Content[0].Type != "tool_use" {
+		t.Errorf("expected type 'tool_use', got %q", result.Content[0].Type)
+	}
+	if result.Content[0].ID != "call_123" {
+		t.Errorf("expected ID 'call_123', got %q", result.Content[0].ID)
+	}
+	if result.Content[0].Name != "get_weather" {
+		t.Errorf("expected name 'get_weather', got %q", result.Content[0].Name)
+	}
+	if result.StopReason != "tool_use" {
+		t.Errorf("expected stop_reason 'tool_use', got %q", result.StopReason)
+	}
+}
+
+func TestToMessagesResponse_WithThinking(t *testing.T) {
+	resp := api.ChatResponse{
+		Model: "test-model",
+		Message: api.Message{
+			Role:     "assistant",
+			Content:  "The answer is 42.",
+			Thinking: "Let me think about this...",
+		},
+		Done:       true,
+		DoneReason: "stop",
+	}
+
+	result := ToMessagesResponse("msg_123", resp)
+
+	if len(result.Content) != 2 {
+		t.Fatalf("expected 2 content blocks, got %d", len(result.Content))
+	}
+	if result.Content[0].Type != "thinking" {
+		t.Errorf("expected first block type 'thinking', got %q", result.Content[0].Type)
+	}
+	if result.Content[0].Thinking == nil || *result.Content[0].Thinking != "Let me think about this..." {
+		t.Errorf("unexpected thinking content: %v", result.Content[0].Thinking)
+	}
+	if result.Content[1].Type != "text" {
+		t.Errorf("expected second block type 'text', got %q", result.Content[1].Type)
+	}
+}
+
+func TestMapStopReason(t *testing.T) {
+	tests := []struct {
+		reason       string
+		hasToolCalls bool
+		want         string
+	}{
+		{"stop", false, "end_turn"},
+		{"length", false, "max_tokens"},
+		{"stop", true, "tool_use"},
+		{"other", false, "stop_sequence"},
+		{"", false, ""},
+	}
+
+	for _, tt := range tests {
+		got := mapStopReason(tt.reason, tt.hasToolCalls)
+		if got != tt.want {
+			t.Errorf("mapStopReason(%q, %v) = %q, want %q", tt.reason, tt.hasToolCalls, got, tt.want)
+		}
+	}
+}
+
+func TestNewError(t *testing.T) {
+	tests := []struct {
+		code int
+		want string
+	}{
+		{400, "invalid_request_error"},
+		{401, "authentication_error"},
+		{403, "permission_error"},
+		{404, "not_found_error"},
+		{429, "rate_limit_error"},
+		{500, "api_error"},
+		{503, "overloaded_error"},
+		{529, "overloaded_error"},
+	}
+
+	for _, tt := range tests {
+		result := NewError(tt.code, "test message")
+		if result.Type != "error" {
+			t.Errorf("NewError(%d) type = %q, want 'error'", tt.code, result.Type)
+		}
+		if result.Error.Type != tt.want {
+			t.Errorf("NewError(%d) error.type = %q, want %q", tt.code, result.Error.Type, tt.want)
+		}
+		if result.Error.Message != "test message" {
+			t.Errorf("NewError(%d) message = %q, want 'test message'", tt.code, result.Error.Message)
+		}
+		if result.RequestID == "" {
+			t.Errorf("NewError(%d) request_id should not be empty", tt.code)
+		}
+	}
+}
+
+func TestGenerateMessageID(t *testing.T) {
+	id1 := GenerateMessageID()
+	id2 := GenerateMessageID()
+
+	if id1 == "" {
+		t.Error("GenerateMessageID returned empty string")
+	}
+	if id1 == id2 {
+		t.Error("GenerateMessageID returned duplicate IDs")
+	}
+	if len(id1) < 10 {
+		t.Errorf("GenerateMessageID returned short ID: %q", id1)
+	}
+	if id1[:4] != "msg_" {
+		t.Errorf("GenerateMessageID should start with 'msg_', got %q", id1[:4])
+	}
+}
+
+func TestStreamConverter_Basic(t *testing.T) {
+	conv := NewStreamConverter("msg_123", "test-model")
+
+	// First chunk
+	resp1 := api.ChatResponse{
+		Model: "test-model",
+		Message: api.Message{
+			Role:    "assistant",
+			Content: "Hello",
+		},
+		Metrics: api.Metrics{PromptEvalCount: 10},
+	}
+
+	events1 := conv.Process(resp1)
+	if len(events1) < 3 {
+		t.Fatalf("expected at least 3 events for first chunk, got %d", len(events1))
+	}
+
+	// Should have message_start, content_block_start, content_block_delta
+	if events1[0].Event != "message_start" {
+		t.Errorf("expected first event 'message_start', got %q", events1[0].Event)
+	}
+	if events1[1].Event != "content_block_start" {
+		t.Errorf("expected second event 'content_block_start', got %q", events1[1].Event)
+	}
+	if events1[2].Event != "content_block_delta" {
+		t.Errorf("expected third event 'content_block_delta', got %q", events1[2].Event)
+	}
+
+	// Final chunk
+	resp2 := api.ChatResponse{
+		Model: "test-model",
+		Message: api.Message{
+			Role:    "assistant",
+			Content: " world!",
+		},
+		Done:       true,
+		DoneReason: "stop",
+		Metrics:    api.Metrics{EvalCount: 5},
+	}
+
+	events2 := conv.Process(resp2)
+
+	// Should have content_block_delta, content_block_stop, message_delta, message_stop
+	hasStop := false
+	for _, e := range events2 {
+		if e.Event == "message_stop" {
+			hasStop = true
+		}
+	}
+	if !hasStop {
+		t.Error("expected message_stop event in final chunk")
+	}
+}
+
+func TestStreamConverter_WithToolCalls(t *testing.T) {
+	conv := NewStreamConverter("msg_123", "test-model")
+
+	resp := api.ChatResponse{
+		Model: "test-model",
+		Message: api.Message{
+			Role: "assistant",
+			ToolCalls: []api.ToolCall{
+				{
+					ID: "call_123",
+					Function: api.ToolCallFunction{
+						Name:      "get_weather",
+						Arguments: testArgs(map[string]any{"location": "Paris"}),
+					},
+				},
+			},
+		},
+		Done:       true,
+		DoneReason: "stop",
+		Metrics:    api.Metrics{PromptEvalCount: 10, EvalCount: 5},
+	}
+
+	events := conv.Process(resp)
+
+	hasToolStart := false
+	hasToolDelta := false
+	for _, e := range events {
+		if e.Event == "content_block_start" {
+			if start, ok := e.Data.(ContentBlockStartEvent); ok {
+				if start.ContentBlock.Type == "tool_use" {
+					hasToolStart = true
+				}
+			}
+		}
+		if e.Event == "content_block_delta" {
+			if delta, ok := e.Data.(ContentBlockDeltaEvent); ok {
+				if delta.Delta.Type == "input_json_delta" {
+					hasToolDelta = true
+				}
+			}
+		}
+	}
+
+	if !hasToolStart {
+		t.Error("expected tool_use content_block_start event")
+	}
+	if !hasToolDelta {
+		t.Error("expected input_json_delta event")
+	}
+}
+
+func TestStreamConverter_ToolCallWithUnmarshalableArgs(t *testing.T) {
+	// Test that unmarshalable arguments (like channels) are handled gracefully
+	// and don't cause a panic or corrupt stream
+	conv := NewStreamConverter("msg_123", "test-model")
+
+	// Create a channel which cannot be JSON marshaled
+	unmarshalable := make(chan int)
+	badArgs := api.NewToolCallFunctionArguments()
+	badArgs.Set("channel", unmarshalable)
+
+	resp := api.ChatResponse{
+		Model: "test-model",
+		Message: api.Message{
+			Role: "assistant",
+			ToolCalls: []api.ToolCall{
+				{
+					ID: "call_bad",
+					Function: api.ToolCallFunction{
+						Name:      "bad_function",
+						Arguments: badArgs,
+					},
+				},
+			},
+		},
+		Done:       true,
+		DoneReason: "stop",
+	}
+
+	// Should not panic and should skip the unmarshalable tool call
+	events := conv.Process(resp)
+
+	// Verify no tool_use block was started (since marshal failed before block start)
+	hasToolStart := false
+	for _, e := range events {
+		if e.Event == "content_block_start" {
+			if start, ok := e.Data.(ContentBlockStartEvent); ok {
+				if start.ContentBlock.Type == "tool_use" {
+					hasToolStart = true
+				}
+			}
+		}
+	}
+
+	if hasToolStart {
+		t.Error("expected no tool_use block when arguments cannot be marshaled")
+	}
+}
+
+func TestStreamConverter_MultipleToolCallsWithMixedValidity(t *testing.T) {
+	// Test that valid tool calls still work when mixed with invalid ones
+	conv := NewStreamConverter("msg_123", "test-model")
+
+	unmarshalable := make(chan int)
+	badArgs := api.NewToolCallFunctionArguments()
+	badArgs.Set("channel", unmarshalable)
+
+	resp := api.ChatResponse{
+		Model: "test-model",
+		Message: api.Message{
+			Role: "assistant",
+			ToolCalls: []api.ToolCall{
+				{
+					ID: "call_good",
+					Function: api.ToolCallFunction{
+						Name:      "good_function",
+						Arguments: testArgs(map[string]any{"location": "Paris"}),
+					},
+				},
+				{
+					ID: "call_bad",
+					Function: api.ToolCallFunction{
+						Name:      "bad_function",
+						Arguments: badArgs,
+					},
+				},
+			},
+		},
+		Done:       true,
+		DoneReason: "stop",
+	}
+
+	events := conv.Process(resp)
+
+	// Count tool_use blocks - should only have 1 (the valid one)
+	toolStartCount := 0
+	toolDeltaCount := 0
+	for _, e := range events {
+		if e.Event == "content_block_start" {
+			if start, ok := e.Data.(ContentBlockStartEvent); ok {
+				if start.ContentBlock.Type == "tool_use" {
+					toolStartCount++
+					if start.ContentBlock.Name != "good_function" {
+						t.Errorf("expected tool name 'good_function', got %q", start.ContentBlock.Name)
+					}
+				}
+			}
+		}
+		if e.Event == "content_block_delta" {
+			if delta, ok := e.Data.(ContentBlockDeltaEvent); ok {
+				if delta.Delta.Type == "input_json_delta" {
+					toolDeltaCount++
+				}
+			}
+		}
+	}
+
+	if toolStartCount != 1 {
+		t.Errorf("expected 1 tool_use block, got %d", toolStartCount)
+	}
+	if toolDeltaCount != 1 {
+		t.Errorf("expected 1 input_json_delta, got %d", toolDeltaCount)
+	}
+}
+
+// TestContentBlockJSON_EmptyFieldsPresent verifies that empty text and thinking fields
+// are serialized in JSON output. The Anthropic SDK requires these fields to be present
+// (even when empty) in content_block_start events to properly accumulate streaming deltas.
+// Without these fields, the SDK throws: "TypeError: unsupported operand type(s) for +=: 'NoneType' and 'str'"
+func TestContentBlockJSON_EmptyFieldsPresent(t *testing.T) {
+	tests := []struct {
+		name     string
+		block    ContentBlock
+		wantKeys []string
+	}{
+		{
+			name: "text block includes empty text field",
+			block: ContentBlock{
+				Type: "text",
+				Text: ptr(""),
+			},
+			wantKeys: []string{"type", "text"},
+		},
+		{
+			name: "thinking block includes empty thinking field",
+			block: ContentBlock{
+				Type:     "thinking",
+				Thinking: ptr(""),
+			},
+			wantKeys: []string{"type", "thinking"},
+		},
+		{
+			name: "text block with content",
+			block: ContentBlock{
+				Type: "text",
+				Text: ptr("hello"),
+			},
+			wantKeys: []string{"type", "text"},
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			data, err := json.Marshal(tt.block)
+			if err != nil {
+				t.Fatalf("failed to marshal: %v", err)
+			}
+
+			var result map[string]any
+			if err := json.Unmarshal(data, &result); err != nil {
+				t.Fatalf("failed to unmarshal: %v", err)
+			}
+
+			for _, key := range tt.wantKeys {
+				if _, ok := result[key]; !ok {
+					t.Errorf("expected key %q to be present in JSON output, got: %s", key, string(data))
+				}
+			}
+		})
+	}
+}
+
+// TestStreamConverter_ContentBlockStartIncludesEmptyFields verifies that content_block_start
+// events include the required empty fields for SDK compatibility.
+func TestStreamConverter_ContentBlockStartIncludesEmptyFields(t *testing.T) {
+	t.Run("text block start includes empty text", func(t *testing.T) {
+		conv := NewStreamConverter("msg_123", "test-model")
+
+		resp := api.ChatResponse{
+			Model:   "test-model",
+			Message: api.Message{Role: "assistant", Content: "hello"},
+		}
+
+		events := conv.Process(resp)
+
+		var foundTextStart bool
+		for _, e := range events {
+			if e.Event == "content_block_start" {
+				if start, ok := e.Data.(ContentBlockStartEvent); ok {
+					if start.ContentBlock.Type == "text" {
+						foundTextStart = true
+						// Marshal and verify the text field is present
+						data, _ := json.Marshal(start)
+						var result map[string]any
+						json.Unmarshal(data, &result)
+						cb := result["content_block"].(map[string]any)
+						if _, ok := cb["text"]; !ok {
+							t.Error("content_block_start for text should include 'text' field")
+						}
+					}
+				}
+			}
+		}
+
+		if !foundTextStart {
+			t.Error("expected text content_block_start event")
+		}
+	})
+
+	t.Run("thinking block start includes empty thinking", func(t *testing.T) {
+		conv := NewStreamConverter("msg_123", "test-model")
+
+		resp := api.ChatResponse{
+			Model:   "test-model",
+			Message: api.Message{Role: "assistant", Thinking: "let me think..."},
+		}
+
+		events := conv.Process(resp)
+
+		var foundThinkingStart bool
+		for _, e := range events {
+			if e.Event == "content_block_start" {
+				if start, ok := e.Data.(ContentBlockStartEvent); ok {
+					if start.ContentBlock.Type == "thinking" {
+						foundThinkingStart = true
+						data, _ := json.Marshal(start)
+						var result map[string]any
+						json.Unmarshal(data, &result)
+						cb := result["content_block"].(map[string]any)
+						if _, ok := cb["thinking"]; !ok {
+							t.Error("content_block_start for thinking should include 'thinking' field")
+						}
+					}
+				}
+			}
+		}
+
+		if !foundThinkingStart {
+			t.Error("expected thinking content_block_start event")
+		}
+	})
+}

api/client.go

@@ -165,7 +165,7 @@ func (c *Client) do(ctx context.Context, method, path string, reqData, respData
 	return nil
 }
 
-const maxBufferSize = 512 * format.KiloByte
+const maxBufferSize = 8 * format.MegaByte
 
 func (c *Client) stream(ctx context.Context, method, path string, data any, fn func([]byte) error) error {
 	var buf io.Reader

api/types.go

@@ -127,6 +127,20 @@ type GenerateRequest struct {
 	// each with an associated log probability. Only applies when Logprobs is true.
 	// Valid values are 0-20. Default is 0 (only return the selected token's logprob).
 	TopLogprobs int `json:"top_logprobs,omitempty"`
+
+	// Experimental: Image generation fields (may change or be removed)
+
+	// Width is the width of the generated image in pixels.
+	// Only used for image generation models.
+	Width int32 `json:"width,omitempty"`
+
+	// Height is the height of the generated image in pixels.
+	// Only used for image generation models.
+	Height int32 `json:"height,omitempty"`
+
+	// Steps is the number of diffusion steps for image generation.
+	// Only used for image generation models.
+	Steps int32 `json:"steps,omitempty"`
 }
 
 // ChatRequest describes a request sent by [Client.Chat].
@@ -735,7 +749,7 @@ type ShowResponse struct {
 	Messages      []Message          `json:"messages,omitempty"`
 	RemoteModel   string             `json:"remote_model,omitempty"`
 	RemoteHost    string             `json:"remote_host,omitempty"`
-	ModelInfo     map[string]any     `json:"model_info,omitempty"`
+	ModelInfo     map[string]any     `json:"model_info"`
 	ProjectorInfo map[string]any     `json:"projector_info,omitempty"`
 	Tensors       []Tensor           `json:"tensors,omitempty"`
 	Capabilities  []model.Capability `json:"capabilities,omitempty"`
@@ -860,6 +874,20 @@ type GenerateResponse struct {
 	// Logprobs contains log probability information for the generated tokens,
 	// if requested via the Logprobs parameter.
 	Logprobs []Logprob `json:"logprobs,omitempty"`
+
+	// Experimental: Image generation fields (may change or be removed)
+
+	// Image contains a base64-encoded generated image.
+	// Only present for image generation models.
+	Image string `json:"image,omitempty"`
+
+	// Completed is the number of completed steps in image generation.
+	// Only present for image generation models during streaming.
+	Completed int64 `json:"completed,omitempty"`
+
+	// Total is the total number of steps for image generation.
+	// Only present for image generation models during streaming.
+	Total int64 `json:"total,omitempty"`
 }
 
 // ModelDetails provides details about a model.

app/cmd/app/app_darwin.m

@@ -14,6 +14,7 @@ extern NSString *SystemWidePath;
 @interface AppDelegate () <NSWindowDelegate, WKNavigationDelegate, WKUIDelegate>
 @property(strong, nonatomic) NSStatusItem *statusItem;
 @property(assign, nonatomic) BOOL updateAvailable;
+@property(assign, nonatomic) BOOL systemShutdownInProgress;
 @end
 
 @implementation AppDelegate
@@ -40,6 +41,13 @@ bool firstTimeRun,startHidden; // Set in run before initialization
 }
 
 - (void)applicationDidFinishLaunching:(NSNotification *)aNotification {
+    // Register for system shutdown/restart notification so we can allow termination
+    [[[NSWorkspace sharedWorkspace] notificationCenter]
+        addObserver:self
+           selector:@selector(systemWillPowerOff:)
+               name:NSWorkspaceWillPowerOffNotification
+             object:nil];
+
     // if we're in development mode, set the app icon
     NSString *bundlePath = [[NSBundle mainBundle] bundlePath];
     if (![bundlePath hasSuffix:@".app"]) {
@@ -278,7 +286,18 @@ bool firstTimeRun,startHidden; // Set in run before initialization
     [NSApp activateIgnoringOtherApps:YES];
 }
 
+- (void)systemWillPowerOff:(NSNotification *)notification {
+    // Set flag so applicationShouldTerminate: knows to allow termination.
+    // The system will call applicationShouldTerminate: after posting this notification.
+    self.systemShutdownInProgress = YES;
+}
+
 - (NSApplicationTerminateReply)applicationShouldTerminate:(NSApplication *)sender {
+    // Allow termination if the system is shutting down or restarting
+    if (self.systemShutdownInProgress) {
+        return NSTerminateNow;
+    }
+    // Otherwise just hide the app (for Cmd+Q, close button, etc.)
     [NSApp hide:nil];
     [NSApp setActivationPolicy:NSApplicationActivationPolicyAccessory];
     return NSTerminateCancel;

cmd/cmd.go

@@ -46,6 +46,9 @@ import (
 	"github.com/ollama/ollama/types/syncmap"
 	"github.com/ollama/ollama/version"
 	xcmd "github.com/ollama/ollama/x/cmd"
+	"github.com/ollama/ollama/x/create"
+	xcreateclient "github.com/ollama/ollama/x/create/client"
+	"github.com/ollama/ollama/x/imagegen"
 )
 
 const ConnectInstructions = "To sign in, navigate to:\n    %s\n\n"
@@ -91,11 +94,88 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 	p := progress.NewProgress(os.Stderr)
 	defer p.Stop()
 
+	// Validate model name early to fail fast
+	modelName := args[0]
+	name := model.ParseName(modelName)
+	if !name.IsValid() {
+		return fmt.Errorf("invalid model name: %s", modelName)
+	}
+
+	// Check for --experimental flag for safetensors model creation
+	experimental, _ := cmd.Flags().GetBool("experimental")
+	if experimental {
+		// Get Modelfile content - either from -f flag or default to "FROM ."
+		var reader io.Reader
+		filename, err := getModelfileName(cmd)
+		if os.IsNotExist(err) || filename == "" {
+			// No Modelfile specified or found - use default
+			reader = strings.NewReader("FROM .\n")
+		} else if err != nil {
+			return err
+		} else {
+			f, err := os.Open(filename)
+			if err != nil {
+				return err
+			}
+			defer f.Close()
+			reader = f
+		}
+
+		// Parse the Modelfile
+		modelfile, err := parser.ParseFile(reader)
+		if err != nil {
+			return fmt.Errorf("failed to parse Modelfile: %w", err)
+		}
+
+		// Extract FROM path and configuration
+		var modelDir string
+		mfConfig := &xcreateclient.ModelfileConfig{}
+
+		for _, cmd := range modelfile.Commands {
+			switch cmd.Name {
+			case "model":
+				modelDir = cmd.Args
+			case "template":
+				mfConfig.Template = cmd.Args
+			case "system":
+				mfConfig.System = cmd.Args
+			case "license":
+				mfConfig.License = cmd.Args
+			}
+		}
+
+		if modelDir == "" {
+			modelDir = "."
+		}
+
+		// Resolve relative paths based on Modelfile location
+		if !filepath.IsAbs(modelDir) && filename != "" {
+			modelDir = filepath.Join(filepath.Dir(filename), modelDir)
+		}
+
+		quantize, _ := cmd.Flags().GetString("quantize")
+		return xcreateclient.CreateModel(xcreateclient.CreateOptions{
+			ModelName: modelName,
+			ModelDir:  modelDir,
+			Quantize:  quantize,
+			Modelfile: mfConfig,
+		}, p)
+	}
+
 	var reader io.Reader
 
 	filename, err := getModelfileName(cmd)
 	if os.IsNotExist(err) {
 		if filename == "" {
+			// No Modelfile found - check if current directory is an image gen model
+			if create.IsTensorModelDir(".") {
+				quantize, _ := cmd.Flags().GetString("quantize")
+				return xcreateclient.CreateModel(xcreateclient.CreateOptions{
+					ModelName: modelName,
+					ModelDir:  ".",
+					Quantize:  quantize,
+				}, p)
+			}
 			reader = strings.NewReader("FROM .\n")
 		} else {
 			return errModelfileNotFound
@@ -127,7 +207,7 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 	}
 	spinner.Stop()
 
-	req.Model = args[0]
+	req.Model = modelName
 	quantize, _ := cmd.Flags().GetString("quantize")
 	if quantize != "" {
 		req.Quantize = quantize
@@ -457,6 +537,7 @@ func RunHandler(cmd *cobra.Command, args []string) error {
 	}
 
 	name := args[0]
+
 	info, err := func() (*api.ShowResponse, error) {
 		showReq := &api.ShowRequest{Name: name}
 		info, err := client.Show(cmd.Context(), showReq)
@@ -518,8 +599,18 @@ func RunHandler(cmd *cobra.Command, args []string) error {
 		return generateEmbedding(cmd, name, opts.Prompt, opts.KeepAlive, truncate, dimensions)
 	}
 
+	// Check if this is an image generation model
+	if slices.Contains(info.Capabilities, model.CapabilityImage) {
+		if opts.Prompt == "" && !interactive {
+			return errors.New("image generation models require a prompt. Usage: ollama run " + name + " \"your prompt here\"")
+		}
+		return imagegen.RunCLI(cmd, name, opts.Prompt, interactive, opts.KeepAlive)
+	}
+
 	// Check for experimental flag
 	isExperimental, _ := cmd.Flags().GetBool("experimental")
+	yoloMode, _ := cmd.Flags().GetBool("experimental-yolo")
+	enableWebsearch, _ := cmd.Flags().GetBool("experimental-websearch")
 
 	if interactive {
 		if err := loadOrUnloadModel(cmd, &opts); err != nil {
@@ -547,9 +638,9 @@ func RunHandler(cmd *cobra.Command, args []string) error {
 			}
 		}
 
-		// Use experimental agent loop with
+		// Use experimental agent loop with tools
 		if isExperimental {
-			return xcmd.GenerateInteractive(cmd, opts.Model, opts.WordWrap, opts.Options, opts.Think, opts.HideThinking, opts.KeepAlive)
+			return xcmd.GenerateInteractive(cmd, opts.Model, opts.WordWrap, opts.Options, opts.Think, opts.HideThinking, opts.KeepAlive, yoloMode, enableWebsearch)
 		}
 
 		return generateInteractive(cmd, opts)
@@ -655,7 +746,11 @@ func PushHandler(cmd *cobra.Command, args []string) error {
 
 			bar, ok := bars[resp.Digest]
 			if !ok {
-				bar = progress.NewBar(fmt.Sprintf("pushing %s...", resp.Digest[7:19]), resp.Total, resp.Completed)
+				msg := resp.Status
+				if msg == "" {
+					msg = fmt.Sprintf("pushing %s...", resp.Digest[7:19])
+				}
+				bar = progress.NewBar(msg, resp.Total, resp.Completed)
 				bars[resp.Digest] = bar
 				p.Add(resp.Digest, bar)
 			}
@@ -804,11 +899,11 @@ func DeleteHandler(cmd *cobra.Command, args []string) error {
 	for _, arg := range args {
 		// Unload the model if it's running before deletion
 		if err := loadOrUnloadModel(cmd, &runOptions{
-			Model:     args[0],
+			Model:     arg,
 			KeepAlive: &api.Duration{Duration: 0},
 		}); err != nil {
 			if !strings.Contains(strings.ToLower(err.Error()), "not found") {
-				fmt.Fprintf(os.Stderr, "Warning: unable to stop model '%s'\n", args[0])
+				fmt.Fprintf(os.Stderr, "Warning: unable to stop model '%s'\n", arg)
 			}
 		}
 
@@ -1720,15 +1815,22 @@ func NewCLI() *cobra.Command {
 	rootCmd.Flags().BoolP("version", "v", false, "Show version information")
 
 	createCmd := &cobra.Command{
-		Use:     "create MODEL",
-		Short:   "Create a model",
-		Args:    cobra.ExactArgs(1),
-		PreRunE: checkServerHeartbeat,
-		RunE:    CreateHandler,
+		Use:   "create MODEL",
+		Short: "Create a model",
+		Args:  cobra.ExactArgs(1),
+		PreRunE: func(cmd *cobra.Command, args []string) error {
+			// Skip server check for experimental mode (writes directly to disk)
+			if experimental, _ := cmd.Flags().GetBool("experimental"); experimental {
+				return nil
+			}
+			return checkServerHeartbeat(cmd, args)
+		},
+		RunE: CreateHandler,
 	}
 
 	createCmd.Flags().StringP("file", "f", "", "Name of the Modelfile (default \"Modelfile\")")
 	createCmd.Flags().StringP("quantize", "q", "", "Quantize model to this level (e.g. q4_K_M)")
+	createCmd.Flags().Bool("experimental", false, "Enable experimental safetensors model creation")
 
 	showCmd := &cobra.Command{
 		Use:     "show MODEL",
@@ -1764,6 +1866,11 @@ func NewCLI() *cobra.Command {
 	runCmd.Flags().Bool("truncate", false, "For embedding models: truncate inputs exceeding context length (default: true). Set --truncate=false to error instead")
 	runCmd.Flags().Int("dimensions", 0, "Truncate output embeddings to specified dimension (embedding models only)")
 	runCmd.Flags().Bool("experimental", false, "Enable experimental agent loop with tools")
+	runCmd.Flags().Bool("experimental-yolo", false, "Skip all tool approval prompts (use with caution)")
+	runCmd.Flags().Bool("experimental-websearch", false, "Enable web search tool in experimental mode")
+
+	// Image generation flags (width, height, steps, seed, etc.)
+	imagegen.RegisterFlags(runCmd)
 
 	stopCmd := &cobra.Command{
 		Use:     "stop MODEL",
@@ -1878,6 +1985,7 @@ func NewCLI() *cobra.Command {
 	} {
 		switch cmd {
 		case runCmd:
+			imagegen.AppendFlagsDocs(cmd)
 			appendEnvDocs(cmd, []envconfig.EnvVar{envVars["OLLAMA_HOST"], envVars["OLLAMA_NOHISTORY"]})
 		case serveCmd:
 			appendEnvDocs(cmd, []envconfig.EnvVar{

cmd/cmd_test.go

@@ -1547,6 +1547,79 @@ func TestRunOptions_Copy_ThinkValueVariants(t *testing.T) {
 	}
 }
 
+func TestShowInfoImageGen(t *testing.T) {
+	var b bytes.Buffer
+	err := showInfo(&api.ShowResponse{
+		Details: api.ModelDetails{
+			Family:            "ZImagePipeline",
+			ParameterSize:     "10.3B",
+			QuantizationLevel: "FP8",
+		},
+		Capabilities: []model.Capability{model.CapabilityImage},
+		Requires:     "0.14.0",
+	}, false, &b)
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	expect := "  Model\n" +
+		"    architecture    ZImagePipeline    \n" +
+		"    parameters      10.3B             \n" +
+		"    quantization    FP8               \n" +
+		"    requires        0.14.0            \n" +
+		"\n" +
+		"  Capabilities\n" +
+		"    image    \n" +
+		"\n"
+	if diff := cmp.Diff(expect, b.String()); diff != "" {
+		t.Errorf("unexpected output (-want +got):\n%s", diff)
+	}
+}
+
+func TestPushProgressMessage(t *testing.T) {
+	tests := []struct {
+		name    string
+		status  string
+		digest  string
+		wantMsg string
+	}{
+		{
+			name:    "uses status when provided",
+			status:  "uploading model",
+			digest:  "sha256:abc123456789def",
+			wantMsg: "uploading model",
+		},
+		{
+			name:    "falls back to digest when status empty",
+			status:  "",
+			digest:  "sha256:abc123456789def",
+			wantMsg: "pushing abc123456789...",
+		},
+		{
+			name:    "handles short digest gracefully",
+			status:  "",
+			digest:  "sha256:abc",
+			wantMsg: "pushing sha256:abc...",
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			msg := tt.status
+			if msg == "" {
+				if len(tt.digest) >= 19 {
+					msg = fmt.Sprintf("pushing %s...", tt.digest[7:19])
+				} else {
+					msg = fmt.Sprintf("pushing %s...", tt.digest)
+				}
+			}
+			if msg != tt.wantMsg {
+				t.Errorf("got %q, want %q", msg, tt.wantMsg)
+			}
+		})
+	}
+}
+
 func TestRunOptions_Copy_Independence(t *testing.T) {
 	// Test that modifications to original don't affect copy
 	originalThink := &api.ThinkValue{Value: "original"}

cmd/interactive.go

@@ -116,7 +116,7 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 		Prompt:         ">>> ",
 		AltPrompt:      "... ",
 		Placeholder:    "Send a message (/? for help)",
-		AltPlaceholder: `Use """ to end multi-line input`,
+		AltPlaceholder: "Press Enter to send",
 	})
 	if err != nil {
 		return err

convert/convert.go

@@ -6,11 +6,14 @@ import (
 	"errors"
 	"fmt"
 	"io/fs"
+	"iter"
 	"log/slog"
+	"maps"
 	"os"
 	"slices"
 	"strings"
 
+	ofs "github.com/ollama/ollama/fs"
 	"github.com/ollama/ollama/fs/ggml"
 )
 
@@ -18,8 +21,13 @@ type ModelParameters struct {
 	Architectures []string `json:"architectures"`
 	VocabSize     uint32   `json:"vocab_size"`
 
+	// TODO is this needed?
+	ModelType string `json:"model_type"`
+
 	TextModel struct {
-		VocabSize uint32 `json:"vocab_size"`
+		VocabSize  uint32 `json:"vocab_size"`
+		HiddenSize uint32 `json:"hidden_size"`
+		ModelType  string `json:"model_type"`
 	} `json:"text_config"`
 }
 
@@ -33,8 +41,94 @@ type AdapterParameters struct {
 	} `json:"lora_parameters"`
 }
 
-func (ModelParameters) KV(t *Tokenizer) ggml.KV {
-	kv := ggml.KV{
+type KV map[string]any
+
+func (kv KV) Architecture() string {
+	return kv.String("general.architecture", "unknown")
+}
+
+type valueTypes interface {
+	uint8 | int8 | uint16 | int16 |
+		uint32 | int32 | uint64 | int64 |
+		string | float32 | float64 | bool
+}
+
+type arrayValueTypes interface {
+	[]uint8 | []int8 | []uint16 | []int16 |
+		[]uint32 | []int32 | []uint64 | []int64 |
+		[]string | []float32 | []float64 | []bool
+}
+
+func keyValue[T valueTypes | arrayValueTypes](kv KV, key string, defaultValue ...T) (T, bool) {
+	if !strings.HasPrefix(key, "tokenizer.") && !strings.HasPrefix(key, "general.") {
+		key = kv.Architecture() + "." + key
+	}
+
+	if val, ok := kv[key].(T); ok {
+		return val, true
+	}
+	return defaultValue[0], false
+}
+
+func (kv KV) String(key string, defaultValue ...string) string {
+	val, _ := keyValue(kv, key, append(defaultValue, "")...)
+	return val
+}
+
+func (kv KV) Uint(key string, defaultValue ...uint32) uint32 {
+	val, _ := keyValue(kv, key, append(defaultValue, 0)...)
+	return val
+}
+
+func (kv KV) Float(key string, defaultValue ...float32) float32 {
+	val, _ := keyValue(kv, key, append(defaultValue, 0)...)
+	return val
+}
+
+func (kv KV) Bool(key string, defaultValue ...bool) bool {
+	val, _ := keyValue(kv, key, append(defaultValue, false)...)
+	return val
+}
+
+func (kv KV) Strings(key string, defaultValue ...[]string) []string {
+	val, _ := keyValue(kv, key, append(defaultValue, []string{""})...)
+	return val
+}
+
+func (kv KV) Ints(key string, defaultValue ...[]int32) []int32 {
+	val, _ := keyValue(kv, key, append(defaultValue, []int32{0})...)
+	return val
+}
+
+func (kv KV) Uints(key string, defaultValue ...[]uint32) []uint32 {
+	val, _ := keyValue(kv, key, append(defaultValue, []uint32{0})...)
+	return val
+}
+
+func (kv KV) Floats(key string, defaultValue ...[]float32) []float32 {
+	val, _ := keyValue(kv, key, append(defaultValue, []float32{0})...)
+	return val
+}
+
+func (kv KV) Bools(key string, defaultValue ...[]bool) []bool {
+	val, _ := keyValue(kv, key, append(defaultValue, []bool{false})...)
+	return val
+}
+
+func (kv KV) Len() int {
+	return len(kv)
+}
+
+func (kv KV) Keys() iter.Seq[string] {
+	return maps.Keys(kv)
+}
+
+func (kv KV) Value(key string) any {
+	return kv[key]
+}
+
+func (ModelParameters) KV(t *Tokenizer) KV {
+	kv := KV{
 		"general.file_type":            uint32(1),
 		"general.quantization_version": uint32(2),
 		"tokenizer.ggml.pre":           t.Pre,
@@ -63,7 +157,7 @@ func (ModelParameters) KV(t *Tokenizer) ggml.KV {
 	return kv
 }
 
-func (p AdapterParameters) KV() ggml.KV {
+func (p AdapterParameters) KV() KV {
 	var alpha float32
 	if p.LoraParameters.Alpha == 0 {
 		alpha = float32(p.Alpha)
@@ -71,7 +165,7 @@ func (p AdapterParameters) KV() ggml.KV {
 		alpha = p.LoraParameters.Alpha
 	}
 
-	kv := ggml.KV{
+	kv := KV{
 		"adapter.lora.alpha": alpha,
 		"adapter.type":       "lora",
 		"general.file_type":  uint32(1),
@@ -88,9 +182,14 @@ func (ModelParameters) specialTokenTypes() []string {
 	}
 }
 
-type ModelConverter interface {
+type ModelKV interface {
 	// KV maps parameters to LLM key-values
-	KV(*Tokenizer) ggml.KV
+	KV(*Tokenizer) KV
+}
+
+type ModelConverter interface {
+	ModelKV
+
 	// Tensors maps input tensors to LLM tensors. Model specific modifications can be done here.
 	Tensors([]Tensor) []*ggml.Tensor
 	// Replacements returns a list of string pairs to replace in tensor names.
@@ -107,7 +206,7 @@ type moreParser interface {
 
 type AdapterConverter interface {
 	// KV maps parameters to LLM key-values
-	KV(ggml.KV) ggml.KV
+	KV(ofs.Config) KV
 	// Tensors maps input tensors to LLM tensors. Adapter specific modifications can be done here.
 	Tensors([]Tensor) []*ggml.Tensor
 	// Replacements returns a list of string pairs to replace in tensor names.
@@ -115,7 +214,7 @@ type AdapterConverter interface {
 	Replacements() []string
 }
 
-func ConvertAdapter(fsys fs.FS, f *os.File, baseKV ggml.KV) error {
+func ConvertAdapter(fsys fs.FS, f *os.File, baseKV ofs.Config) error {
 	bts, err := fs.ReadFile(fsys, "adapter_config.json")
 	if err != nil {
 		return err
@@ -126,8 +225,8 @@ func ConvertAdapter(fsys fs.FS, f *os.File, baseKV ggml.KV) error {
 		return err
 	}
 
-	arch, ok := baseKV["general.architecture"]
-	if !ok {
+	arch := baseKV.Architecture()
+	if arch == "" {
 		return errors.New("architecture not set for the base model")
 	}
 
@@ -153,23 +252,19 @@ func ConvertAdapter(fsys fs.FS, f *os.File, baseKV ggml.KV) error {
 	return writeFile(f, conv.KV(baseKV), conv.Tensors(ts))
 }
 
-// Convert writes an Ollama compatible model to the provided io.WriteSeeker based on configurations
-// and files it finds in the input path.
-// Supported input model formats include safetensors.
-// Supported input tokenizers files include tokenizer.json (preferred) and tokenizer.model.
-func ConvertModel(fsys fs.FS, f *os.File) error {
+func LoadModelMetadata(fsys fs.FS) (ModelKV, *Tokenizer, error) {
 	bts, err := fs.ReadFile(fsys, "config.json")
 	if err != nil {
-		return err
+		return nil, nil, err
 	}
 
 	var p ModelParameters
 	if err := json.Unmarshal(bts, &p); err != nil {
-		return err
+		return nil, nil, err
 	}
 
 	if len(p.Architectures) < 1 {
-		return errors.New("unknown architecture")
+		return nil, nil, errors.New("unknown architecture")
 	}
 
 	var conv ModelConverter
@@ -216,23 +311,27 @@ func ConvertModel(fsys fs.FS, f *os.File) error {
 		conv = &deepseekocr{}
 	case "DeepseekV3ForCausalLM":
 		conv = &deepseek2Model{}
+	case "Glm4MoeLiteForCausalLM":
+		conv = &glm4MoeLiteModel{}
+	case "Lfm2ForCausalLM":
+		conv = &lfm2Model{}
 	default:
-		return fmt.Errorf("unsupported architecture %q", p.Architectures[0])
+		return nil, nil, fmt.Errorf("unsupported architecture %q", p.Architectures[0])
 	}
 
 	if err := json.Unmarshal(bts, conv); err != nil {
-		return err
+		return nil, nil, err
 	}
 
 	if t, ok := conv.(moreParser); ok {
 		if err := t.parseMore(fsys); err != nil {
-			return err
+			return nil, nil, err
 		}
 	}
 
 	t, err := parseTokenizer(fsys, conv.specialTokenTypes())
 	if err != nil {
-		return err
+		return nil, nil, err
 	}
 
 	vocabSize := int(cmp.Or(p.VocabSize, p.TextModel.VocabSize))
@@ -254,6 +353,19 @@ func ConvertModel(fsys fs.FS, f *os.File) error {
 	default:
 		slog.Debug("vocabulary", "size", len(t.Vocabulary.Tokens))
 	}
+	return conv, t, nil
+}
+
+// Convert writes an Ollama compatible model to the provided io.WriteSeeker based on configurations
+// and files it finds in the input path.
+// Supported input model formats include safetensors.
+// Supported input tokenizers files include tokenizer.json (preferred) and tokenizer.model.
+func ConvertModel(fsys fs.FS, f *os.File) error {
+	kv, t, err := LoadModelMetadata(fsys)
+	if err != nil {
+		return err
+	}
+	conv := kv.(ModelConverter)
 
 	ts, err := parseTensors(fsys, strings.NewReplacer(conv.Replacements()...))
 	if err != nil {
@@ -263,7 +375,7 @@ func ConvertModel(fsys fs.FS, f *os.File) error {
 	return writeFile(f, conv.KV(t), conv.Tensors(ts))
 }
 
-func writeFile(f *os.File, kv ggml.KV, ts []*ggml.Tensor) error {
+func writeFile(f *os.File, kv KV, ts []*ggml.Tensor) error {
 	for i := range ts {
 		ts[i].Shape = slices.Clone(ts[i].Shape)
 		slices.Reverse(ts[i].Shape)

convert/convert_bert.go

@@ -88,7 +88,7 @@ func (p *bertModel) parseMore(fsys fs.FS) error {
 	return nil
 }
 
-func (p *bertModel) KV(t *Tokenizer) ggml.KV {
+func (p *bertModel) KV(t *Tokenizer) KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "bert"
 	kv["bert.attention.causal"] = false

convert/convert_commandr.go

@@ -24,7 +24,7 @@ type commandrModel struct {
 
 var _ ModelConverter = (*commandrModel)(nil)
 
-func (p *commandrModel) KV(t *Tokenizer) ggml.KV {
+func (p *commandrModel) KV(t *Tokenizer) KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "command-r"
 	kv["general.name"] = "command-r"

convert/convert_deepseek2.go

@@ -47,7 +47,7 @@ type deepseek2Model struct {
 	Architecture string
 }
 
-func (p *deepseek2Model) KV(t *Tokenizer) ggml.KV {
+func (p *deepseek2Model) KV(t *Tokenizer) KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "deepseek2"
 	kv["general.type"] = "model"

convert/convert_deepseekocr.go

@@ -41,7 +41,7 @@ type deepseekocr struct {
 	} `json:"vision_config"`
 }
 
-func (m *deepseekocr) KV(t *Tokenizer) ggml.KV {
+func (m *deepseekocr) KV(t *Tokenizer) KV {
 	kv := m.ModelParameters.KV(t)
 	kv["general.architecture"] = "deepseekocr"
 	kv["block_count"] = m.LanguageConfig.HiddenLayers

convert/convert_gemma.go

@@ -23,7 +23,7 @@ type gemmaModel struct {
 
 var _ ModelConverter = (*gemmaModel)(nil)
 
-func (p *gemmaModel) KV(t *Tokenizer) ggml.KV {
+func (p *gemmaModel) KV(t *Tokenizer) KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "gemma"
 	kv["gemma.context_length"] = p.MaxPositionEmbeddings

convert/convert_gemma2.go

@@ -1,7 +1,5 @@
 package convert
 
-import "github.com/ollama/ollama/fs/ggml"
-
 type gemma2Model struct {
 	gemmaModel
 	SlidingWindow         uint32  `json:"sliding_window"`
@@ -9,7 +7,7 @@ type gemma2Model struct {
 	FinalLogitSoftcap     float32 `json:"final_logit_softcapping"`
 }
 
-func (p *gemma2Model) KV(t *Tokenizer) ggml.KV {
+func (p *gemma2Model) KV(t *Tokenizer) KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "gemma2"
 	kv["gemma2.context_length"] = p.MaxPositionEmbeddings

convert/convert_gemma2_adapter.go

@@ -6,6 +6,7 @@ import (
 	"github.com/pdevine/tensor"
 	"github.com/pdevine/tensor/native"
 
+	"github.com/ollama/ollama/fs"
 	"github.com/ollama/ollama/fs/ggml"
 )
 
@@ -15,7 +16,7 @@ type gemma2Adapter struct {
 
 var _ AdapterConverter = (*gemma2Adapter)(nil)
 
-func (p *gemma2Adapter) KV(baseKV ggml.KV) ggml.KV {
+func (p *gemma2Adapter) KV(baseKV fs.Config) KV {
 	kv := p.AdapterParameters.KV()
 	kv["general.architecture"] = "gemma2"
 	return kv

convert/convert_gemma3.go

@@ -3,8 +3,6 @@ package convert
 import (
 	"cmp"
 	"slices"
-
-	"github.com/ollama/ollama/fs/ggml"
 )
 
 type gemma3Model struct {
@@ -55,7 +53,7 @@ const (
 	gemma27BLayerCount = 62
 )
 
-func (p *gemma3Model) KV(t *Tokenizer) ggml.KV {
+func (p *gemma3Model) KV(t *Tokenizer) KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "gemma3"
 

convert/convert_gemma3n.go

@@ -38,7 +38,7 @@ type gemma3nModel struct {
 	VisionModel struct{} `json:"vision_config"`
 }
 
-func (m *gemma3nModel) KV(t *Tokenizer) ggml.KV {
+func (m *gemma3nModel) KV(t *Tokenizer) KV {
 	kv := m.ModelParameters.KV(t)
 	kv["general.architecture"] = "gemma3n"
 	kv["gemma3n.activation_sparsity_scale"] = slices.Collect(func(yield func(float32) bool) {

convert/convert_glm4moelite.go

@@ -0,0 +1,150 @@
+package convert
+
+import (
+	"cmp"
+	"fmt"
+	"log/slog"
+	"regexp"
+	"strconv"
+
+	"github.com/ollama/ollama/fs/ggml"
+)
+
+type glm4MoeLiteModel struct {
+	ModelParameters
+	MaxPositionEmbeddings uint32  `json:"max_position_embeddings"`
+	HiddenSize            uint32  `json:"hidden_size"`
+	HiddenLayers          uint32  `json:"num_hidden_layers"`
+	IntermediateSize      uint32  `json:"intermediate_size"`
+	NumAttentionHeads     uint32  `json:"num_attention_heads"`
+	NumKeyValueHeads      uint32  `json:"num_key_value_heads"`
+	RMSNormEPS            float32 `json:"rms_norm_eps"`
+
+	RopeTheta     float32 `json:"rope_theta"`
+	QKNopeHeadDim uint32  `json:"qk_nope_head_dim"`
+	QKRopeHeadDim uint32  `json:"qk_rope_head_dim"`
+	KVLoraRank    uint32  `json:"kv_lora_rank"`
+	QLoraRank     uint32  `json:"q_lora_rank"`
+	VHeadDim      uint32  `json:"v_head_dim"`
+
+	ExpertCount            uint32  `json:"n_routed_experts"`
+	ExpertSharedCount      uint32  `json:"n_shared_experts"`
+	ExpertIntermediateSize uint32  `json:"moe_intermediate_size"`
+	ExpertUsedCount        uint32  `json:"num_experts_per_tok"`
+	ExpertWeightsNorm      bool    `json:"norm_topk_prob"`
+	ExpertWeightsScale     float32 `json:"routed_scaling_factor"`
+
+	LeadingDenseBlockCount uint32 `json:"first_k_dense_replace"`
+}
+
+func (p *glm4MoeLiteModel) KV(t *Tokenizer) KV {
+	kv := p.ModelParameters.KV(t)
+	kv["general.architecture"] = "glm4moelite"
+	kv["general.type"] = "model"
+	kv["glm4moelite.block_count"] = p.HiddenLayers
+
+	numHeads := p.NumAttentionHeads
+	numKVHeads := p.NumKeyValueHeads
+
+	kv["glm4moelite.attention.head_count"] = numHeads
+	kv["glm4moelite.attention.head_count_kv"] = numKVHeads
+	kv["glm4moelite.attention.key_length"] = p.QKNopeHeadDim + p.QKRopeHeadDim
+	kv["glm4moelite.attention.kv_lora_rank"] = p.KVLoraRank
+	kv["glm4moelite.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
+	kv["glm4moelite.attention.q_lora_rank"] = p.QLoraRank
+	kv["glm4moelite.attention.value_length"] = p.VHeadDim
+	kv["glm4moelite.context_length"] = p.MaxPositionEmbeddings
+	kv["glm4moelite.embedding_length"] = p.HiddenSize
+	kv["glm4moelite.expert_count"] = p.ExpertCount
+	kv["glm4moelite.expert_feed_forward_length"] = p.ExpertIntermediateSize
+	kv["glm4moelite.expert_shared_count"] = p.ExpertSharedCount
+
+	kv["glm4moelite.expert_gating_func"] = uint32(2)
+	kv["glm4moelite.expert_used_count"] = p.ExpertUsedCount
+	kv["glm4moelite.expert_weights_norm"] = p.ExpertWeightsNorm
+	kv["glm4moelite.expert_weights_scale"] = p.ExpertWeightsScale
+	kv["glm4moelite.feed_forward_length"] = p.IntermediateSize
+	kv["glm4moelite.leading_dense_block_count"] = p.LeadingDenseBlockCount
+
+	kv["glm4moelite.rope.dimension_count"] = p.QKRopeHeadDim
+	kv["glm4moelite.rope.freq_base"] = cmp.Or(p.RopeTheta, float32(1000000.0))
+
+	kv["tokenizer.ggml.pre"] = "glm4"
+
+	return kv
+}
+
+func (p *glm4MoeLiteModel) Replacements() []string {
+	return []string{
+		"lm_head", "output",
+		"model.embed_tokens", "token_embd",
+		"model.norm", "output_norm",
+		"model.layers", "blk",
+		"input_layernorm", "attn_norm",
+		"self_attn.kv_a_proj_with_mqa", "attn_kv_a_mqa",
+		"self_attn.kv_a_layernorm", "attn_kv_a_norm",
+		"self_attn.kv_b_proj", "attn_kv_b",
+		"self_attn.q_a_proj", "attn_q_a",
+		"self_attn.q_a_layernorm", "attn_q_a_norm",
+		"self_attn.q_b_proj", "attn_q_b",
+		"self_attn.o_proj", "attn_output",
+		"post_attention_layernorm", "ffn_norm",
+		"mlp.shared_experts.down_proj", "ffn_down_shexp",
+		"mlp.shared_experts.gate_proj", "ffn_gate_shexp",
+		"mlp.shared_experts.up_proj", "ffn_up_shexp",
+		"mlp.gate_proj", "ffn_gate",
+		"mlp.down_proj", "ffn_down",
+		"mlp.up_proj", "ffn_up",
+		"mlp.gate.e_score_correction_bias", "exp_probs_b.bias",
+		"mlp.gate", "ffn_gate_inp",
+	}
+}
+
+func (p *glm4MoeLiteModel) Tensors(s []Tensor) (out []*ggml.Tensor) {
+	merges := make([]merge, p.HiddenLayers*3)
+	for i := range p.HiddenLayers {
+		merges[i*3+0] = merge{
+			fmt.Sprintf("blk.%d.mlp.experts.*.gate_proj.weight", i),
+			fmt.Sprintf("blk.%d.ffn_gate_exps.weight", i),
+		}
+		merges[i*3+1] = merge{
+			fmt.Sprintf("blk.%d.mlp.experts.*.up_proj.weight", i),
+			fmt.Sprintf("blk.%d.ffn_up_exps.weight", i),
+		}
+		merges[i*3+2] = merge{
+			fmt.Sprintf("blk.%d.mlp.experts.*.down_proj.weight", i),
+			fmt.Sprintf("blk.%d.ffn_down_exps.weight", i),
+		}
+	}
+
+	skipLayer := func(n string, minValue uint32) bool {
+		re := regexp.MustCompile(`^blk\.(\d+)`)
+		matches := re.FindStringSubmatch(n)
+		if matches == nil {
+			return false
+		}
+
+		blkNum, err := strconv.Atoi(matches[1])
+		if err != nil {
+			return false
+		}
+
+		return uint32(blkNum) >= minValue
+	}
+
+	out, s = mergeTensors(s, merges...)
+	for _, t := range s {
+		// skip any additional layers (such as the Multi-Token Prediction layer)
+		if skipLayer(t.Name(), p.HiddenLayers) {
+			slog.Debug("skipping layer", "name", t.Name())
+			continue
+		}
+		out = append(out, &ggml.Tensor{
+			Name:     t.Name(),
+			Kind:     t.Kind(),
+			Shape:    t.Shape(),
+			WriterTo: t,
+		})
+	}
+	return out
+}

convert/convert_gptoss.go

@@ -37,7 +37,7 @@ type gptossModel struct {
 
 var _ ModelConverter = (*gptossModel)(nil)
 
-func (m *gptossModel) KV(t *Tokenizer) ggml.KV {
+func (m *gptossModel) KV(t *Tokenizer) KV {
 	kv := m.ModelParameters.KV(t)
 	kv["general.architecture"] = "gptoss"
 	kv["general.file_type"] = uint32(4)

convert/convert_lfm2.go

@@ -0,0 +1,133 @@
+package convert
+
+import (
+	"encoding/binary"
+	"io"
+	"slices"
+	"strings"
+
+	"github.com/d4l3k/go-bfloat16"
+	"github.com/ollama/ollama/fs/ggml"
+)
+
+// bf16ToF32 wraps a tensor and converts BF16 data to F32 on write
+type bf16ToF32 struct {
+	Tensor
+}
+
+func (c bf16ToF32) WriteTo(w io.Writer) (int64, error) {
+	// Read BF16 data from original tensor
+	var buf strings.Builder
+	if _, err := c.Tensor.WriteTo(&buf); err != nil {
+		return 0, err
+	}
+	bf16Data := []byte(buf.String())
+
+	// Convert BF16 to F32
+	f32s := bfloat16.DecodeFloat32(bf16Data)
+
+	// Write F32 data
+	if err := binary.Write(w, binary.LittleEndian, f32s); err != nil {
+		return 0, err
+	}
+	return int64(len(f32s) * 4), nil
+}
+
+type lfm2Model struct {
+	ModelParameters
+	HiddenSize            uint32   `json:"hidden_size"`
+	NumHiddenLayers       uint32   `json:"num_hidden_layers"`
+	MaxPositionEmbeddings uint32   `json:"max_position_embeddings"`
+	IntermediateSize      uint32   `json:"intermediate_size"`
+	NumAttentionHeads     uint32   `json:"num_attention_heads"`
+	NumKeyValueHeads      uint32   `json:"num_key_value_heads"`
+	RopeTheta             float32  `json:"rope_theta"`
+	NormEps               float32  `json:"norm_eps"`
+	ConvLCache            uint32   `json:"conv_L_cache"`
+	LayerTypes            []string `json:"layer_types"`
+	TieEmbedding          bool     `json:"tie_embedding"`
+}
+
+var _ ModelConverter = (*lfm2Model)(nil)
+
+func (p *lfm2Model) KV(t *Tokenizer) KV {
+	kv := p.ModelParameters.KV(t)
+	kv["general.architecture"] = "lfm2"
+	kv["lfm2.vocab_size"] = p.VocabSize
+	kv["lfm2.block_count"] = p.NumHiddenLayers
+	kv["lfm2.embedding_length"] = p.HiddenSize
+	kv["lfm2.feed_forward_length"] = p.IntermediateSize
+	kv["lfm2.context_length"] = p.MaxPositionEmbeddings
+
+	// Build per-layer KV head count array based on layer_types
+	// (0 = shortconv layer, non-zero = attention layer with that many KV heads)
+	kvHeadCounts := make([]uint32, p.NumHiddenLayers)
+	for i := range p.NumHiddenLayers {
+		if int(i) < len(p.LayerTypes) && p.LayerTypes[i] == "full_attention" {
+			kvHeadCounts[i] = p.NumKeyValueHeads
+		}
+	}
+
+	kv["lfm2.attention.head_count"] = p.NumAttentionHeads
+	kv["lfm2.attention.head_count_kv"] = kvHeadCounts
+	kv["lfm2.attention.key_length"] = p.HiddenSize / p.NumAttentionHeads
+	kv["lfm2.attention.value_length"] = p.HiddenSize / p.NumAttentionHeads
+	kv["lfm2.attention.layer_norm_rms_epsilon"] = p.NormEps
+	kv["lfm2.rope.freq_base"] = p.RopeTheta
+	kv["lfm2.shortconv.l_cache"] = p.ConvLCache
+
+	return kv
+}
+
+func (p *lfm2Model) Tensors(ts []Tensor) []*ggml.Tensor {
+	var out []*ggml.Tensor
+
+	for _, t := range ts {
+		shape := t.Shape()
+		kind := t.Kind()
+		var writer io.WriterTo = t
+
+		// Squeeze conv weights: [D, 1, K] -> [D, K] and convert to F32
+		if strings.HasSuffix(t.Name(), "shortconv.conv.weight") {
+			if len(shape) == 3 && shape[1] == 1 {
+				shape = []uint64{shape[0], shape[2]}
+			}
+			// Convert BF16 to F32 for accuracy (small kernel, runtime casts to F32 anyway)
+			if kind == tensorKindBF16 {
+				kind = tensorKindFP32
+				writer = bf16ToF32{t}
+			}
+		}
+
+		out = append(out, &ggml.Tensor{
+			Name:     t.Name(),
+			Kind:     kind,
+			Shape:    slices.Clone(shape),
+			WriterTo: writer,
+		})
+	}
+
+	return out
+}
+
+func (p *lfm2Model) Replacements() []string {
+	return []string{
+		"model.embed_tokens", "token_embd",
+		"model.embedding_norm", "output_norm",
+		"model.layers", "blk",
+		"operator_norm", "attn_norm",
+		"self_attn.q_proj", "attn_q",
+		"self_attn.k_proj", "attn_k",
+		"self_attn.v_proj", "attn_v",
+		"self_attn.out_proj", "attn_output",
+		"self_attn.q_layernorm", "attn_q_norm",
+		"self_attn.k_layernorm", "attn_k_norm",
+		"conv.conv", "shortconv.conv",
+		"conv.in_proj", "shortconv.in_proj",
+		"conv.out_proj", "shortconv.out_proj",
+		"feed_forward.w1", "ffn_gate",
+		"feed_forward.w2", "ffn_down",
+		"feed_forward.w3", "ffn_up",
+		"ffn_norm", "ffn_norm",
+	}
+}

convert/convert_llama.go

@@ -48,7 +48,7 @@ type llamaModel struct {
 
 var _ ModelConverter = (*llamaModel)(nil)
 
-func (p *llamaModel) KV(t *Tokenizer) ggml.KV {
+func (p *llamaModel) KV(t *Tokenizer) KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "llama"
 	kv["llama.vocab_size"] = p.VocabSize

convert/convert_llama4.go

@@ -35,7 +35,7 @@ type llama4Model struct {
 }
 
 // KV implements ModelConverter.
-func (p *llama4Model) KV(t *Tokenizer) ggml.KV {
+func (p *llama4Model) KV(t *Tokenizer) KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "llama4"
 

convert/convert_llama_adapter.go

@@ -7,6 +7,7 @@ import (
 	"github.com/pdevine/tensor"
 	"github.com/pdevine/tensor/native"
 
+	"github.com/ollama/ollama/fs"
 	"github.com/ollama/ollama/fs/ggml"
 )
 
@@ -18,13 +19,13 @@ type llamaAdapter struct {
 
 var _ AdapterConverter = (*llamaAdapter)(nil)
 
-func (p *llamaAdapter) KV(baseKV ggml.KV) ggml.KV {
+func (p *llamaAdapter) KV(baseKV fs.Config) KV {
 	kv := p.AdapterParameters.KV()
 	kv["general.architecture"] = "llama"
-	kv["llama.attention.head_count"] = baseKV["llama.attention.head_count"]
-	kv["llama.attention.head_count_kv"] = baseKV["llama.attention.head_count_kv"]
+	kv["llama.attention.head_count"] = baseKV.Value("llama.attention.head_count")
+	kv["llama.attention.head_count_kv"] = baseKV.Value("llama.attention.head_count_kv")
 
-	p.NumAttentionHeads = baseKV["llama.attention.head_count"].(uint32)
+	p.NumAttentionHeads = baseKV.Value("llama.attention.head_count").(uint32)
 
 	return kv
 }

convert/convert_mistral.go

@@ -60,7 +60,7 @@ type mistral3Model struct {
 	ProjectorHiddenAct      string `json:"projector_hidden_act"`
 }
 
-func (p *mistral3Model) KV(t *Tokenizer) ggml.KV {
+func (p *mistral3Model) KV(t *Tokenizer) KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "mistral3"
 	kv["mistral3.vocab_size"] = p.TextModel.VocabSize

convert/convert_mistral_causal.go

@@ -39,7 +39,7 @@ type mistral3CausalModel struct {
 	} `json:"rope_parameters"`
 }
 
-func (p *mistral3CausalModel) KV(t *Tokenizer) ggml.KV {
+func (p *mistral3CausalModel) KV(t *Tokenizer) KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "mistral3"
 	kv["mistral3.vocab_size"] = p.VocabSize

convert/convert_mixtral.go

@@ -12,7 +12,7 @@ type mixtralModel struct {
 	NumExpertsPerToken uint32 `json:"num_experts_per_tok"`
 }
 
-func (p *mixtralModel) KV(t *Tokenizer) ggml.KV {
+func (p *mixtralModel) KV(t *Tokenizer) KV {
 	kv := p.llamaModel.KV(t)
 
 	if p.NumLocalExperts > 0 {

convert/convert_mllama.go

@@ -34,7 +34,7 @@ type mllamaModel struct {
 	} `json:"vision_config"`
 }
 
-func (m *mllamaModel) KV(t *Tokenizer) ggml.KV {
+func (m *mllamaModel) KV(t *Tokenizer) KV {
 	kv := m.ModelParameters.KV(t)
 	kv["general.architecture"] = "mllama"
 

convert/convert_nomicbert.go

@@ -87,7 +87,7 @@ func (p *nomicbertModel) parseMore(fsys fs.FS) error {
 	return nil
 }
 
-func (p *nomicbertModel) KV(t *Tokenizer) ggml.KV {
+func (p *nomicbertModel) KV(t *Tokenizer) KV {
 	kv := p.ModelParameters.KV(t)
 
 	// Determine architecture based on MoE parameters (following qwen3 pattern)

convert/convert_olmo.go

@@ -34,7 +34,7 @@ type olmoModel struct {
 
 var _ ModelConverter = (*olmoModel)(nil)
 
-func (p *olmoModel) KV(t *Tokenizer) ggml.KV {
+func (p *olmoModel) KV(t *Tokenizer) KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "olmo3"
 	kv["olmo3.block_count"] = p.NumHiddenLayers

convert/convert_phi3.go

@@ -37,7 +37,7 @@ type phi3Model struct {
 
 var _ ModelConverter = (*phi3Model)(nil)
 
-func (p *phi3Model) KV(t *Tokenizer) ggml.KV {
+func (p *phi3Model) KV(t *Tokenizer) KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "phi3"
 	kv["phi3.context_length"] = p.MaxPositionEmbeddings

convert/convert_qwen2.go

@@ -22,7 +22,7 @@ type qwen2Model struct {
 
 var _ ModelConverter = (*qwen2Model)(nil)
 
-func (q *qwen2Model) KV(t *Tokenizer) ggml.KV {
+func (q *qwen2Model) KV(t *Tokenizer) KV {
 	kv := q.ModelParameters.KV(t)
 	kv["general.architecture"] = "qwen2"
 	kv["qwen2.block_count"] = q.HiddenLayers

convert/convert_qwen25vl.go

@@ -29,7 +29,7 @@ type qwen25VLModel struct {
 
 var _ ModelConverter = (*qwen25VLModel)(nil)
 
-func (q *qwen25VLModel) KV(t *Tokenizer) ggml.KV {
+func (q *qwen25VLModel) KV(t *Tokenizer) KV {
 	kv := q.ModelParameters.KV(t)
 	kv["general.architecture"] = "qwen25vl"
 

convert/convert_qwen3.go

@@ -32,7 +32,7 @@ type qwen3Model struct {
 }
 
 // KV implements ModelConverter.
-func (q *qwen3Model) KV(t *Tokenizer) ggml.KV {
+func (q *qwen3Model) KV(t *Tokenizer) KV {
 	arch := "qwen3"
 	if q.NumExperts > 0 {
 		arch += "moe"

convert/convert_qwen3vl.go

@@ -45,7 +45,7 @@ func (m *qwen3VLModel) parseMore(fsys fs.FS) error {
 	return json.Unmarshal(bts, &m.VisionModel)
 }
 
-func (m *qwen3VLModel) KV(t *Tokenizer) ggml.KV {
+func (m *qwen3VLModel) KV(t *Tokenizer) KV {
 	kv := m.qwen3Model.KV(t)
 
 	arch := "qwen3vl"

convert/convert_test.go

@@ -19,6 +19,7 @@ import (
 	"testing"
 
 	"github.com/google/go-cmp/cmp"
+	fsc "github.com/ollama/ollama/fs"
 	"github.com/ollama/ollama/fs/ggml"
 )
 
@@ -28,7 +29,7 @@ type tensorData struct {
 	Shape   []int  `json:"shape"`
 }
 
-func convertFull(t *testing.T, fsys fs.FS) (*os.File, ggml.KV, ggml.Tensors) {
+func convertFull(t *testing.T, fsys fs.FS) (*os.File, fsc.Config, ggml.Tensors) {
 	t.Helper()
 
 	f, err := os.CreateTemp(t.TempDir(), "f16")
@@ -59,9 +60,10 @@ func convertFull(t *testing.T, fsys fs.FS) (*os.File, ggml.KV, ggml.Tensors) {
 	return r, m.KV(), m.Tensors()
 }
 
-func generateResultsJSON(t *testing.T, f *os.File, kv ggml.KV, tensors ggml.Tensors) map[string]string {
+func generateResultsJSON(t *testing.T, f *os.File, kv fsc.Config, tensors ggml.Tensors) map[string]string {
 	actual := make(map[string]string)
-	for k, v := range kv {
+	for k := range kv.Keys() {
+		v := kv.Value(k)
 		if s, ok := v.(json.Marshaler); !ok {
 			actual[k] = fmt.Sprintf("%v", v)
 		} else {
@@ -277,7 +279,7 @@ func generateSafetensorTestData(t *testing.T, tempDir string, tensorData map[str
 func TestConvertAdapter(t *testing.T) {
 	type AdapterCase struct {
 		Name     string
-		BaseKV   map[string]any
+		BaseKV   KV
 		Expected map[string]string
 	}
 

docs/README.md

@@ -14,6 +14,7 @@
 * [API Reference](https://docs.ollama.com/api)
 * [Modelfile Reference](https://docs.ollama.com/modelfile)
 * [OpenAI Compatibility](https://docs.ollama.com/api/openai-compatibility)
+* [Anthropic Compatibility](./api/anthropic-compatibility.mdx)
 
 ### Resources
 

docs/api.md

@@ -16,6 +16,7 @@
 - [Generate Embeddings](#generate-embeddings)
 - [List Running Models](#list-running-models)
 - [Version](#version)
+- [Experimental: Image Generation](#image-generation-experimental)
 
 ## Conventions
 
@@ -58,6 +59,15 @@ Advanced parameters (optional):
 - `keep_alive`: controls how long the model will stay loaded into memory following the request (default: `5m`)
 - `context` (deprecated): the context parameter returned from a previous request to `/generate`, this can be used to keep a short conversational memory
 
+Experimental image generation parameters (for image generation models only):
+
+> [!WARNING]
+> These parameters are experimental and may change in future versions.
+
+- `width`: width of the generated image in pixels
+- `height`: height of the generated image in pixels
+- `steps`: number of diffusion steps
+
 #### Structured outputs
 
 Structured outputs are supported by providing a JSON schema in the `format` parameter. The model will generate a response that matches the schema. See the [structured outputs](#request-structured-outputs) example below.
@@ -1867,3 +1877,55 @@ curl http://localhost:11434/api/version
   "version": "0.5.1"
 }
 ```
+
+## Experimental Features
+
+### Image Generation (Experimental)
+
+> [!WARNING]
+> Image generation is experimental and may change in future versions.
+
+Image generation is now supported through the standard `/api/generate` endpoint when using image generation models. The API automatically detects when an image generation model is being used.
+
+See the [Generate a completion](#generate-a-completion) section for the full API documentation. The experimental image generation parameters (`width`, `height`, `steps`) are documented there.
+
+#### Example
+
+##### Request
+
+```shell
+curl http://localhost:11434/api/generate -d '{
+  "model": "x/z-image-turbo",
+  "prompt": "a sunset over mountains",
+  "width": 1024,
+  "height": 768
+}'
+```
+
+##### Response (streaming)
+
+Progress updates during generation:
+
+```json
+{
+  "model": "x/z-image-turbo",
+  "created_at": "2024-01-15T10:30:00.000000Z",
+  "completed": 5,
+  "total": 20,
+  "done": false
+}
+```
+
+##### Final Response
+
+```json
+{
+  "model": "x/z-image-turbo",
+  "created_at": "2024-01-15T10:30:15.000000Z",
+  "image": "iVBORw0KGgoAAAANSUhEUg...",
+  "done": true,
+  "done_reason": "stop",
+  "total_duration": 15000000000,
+  "load_duration": 2000000000
+}
+```

docs/api/anthropic-compatibility.mdx

@@ -0,0 +1,408 @@
+---
+title: Anthropic compatibility
+---
+
+Ollama provides compatibility with the [Anthropic Messages API](https://docs.anthropic.com/en/api/messages) to help connect existing applications to Ollama, including tools like Claude Code.
+
+## Recommended models
+
+For coding use cases, models like `glm-4.7:cloud`, `minimax-m2.1:cloud`, and `qwen3-coder` are recommended.
+
+Pull a model before use:
+```shell
+ollama pull qwen3-coder
+ollama pull glm-4.7:cloud
+```
+
+## Usage
+
+### Environment variables
+
+To use Ollama with tools that expect the Anthropic API (like Claude Code), set these environment variables:
+
+```shell
+export ANTHROPIC_AUTH_TOKEN=ollama  # required but ignored
+export ANTHROPIC_BASE_URL=http://localhost:11434
+export ANTHROPIC_API_KEY=ollama  # required but ignored
+```
+
+### Simple `/v1/messages` example
+
+<CodeGroup dropdown>
+
+```python basic.py
+import anthropic
+
+client = anthropic.Anthropic(
+    base_url='http://localhost:11434',
+    api_key='ollama',  # required but ignored
+)
+
+message = client.messages.create(
+    model='qwen3-coder',
+    max_tokens=1024,
+    messages=[
+        {'role': 'user', 'content': 'Hello, how are you?'}
+    ]
+)
+print(message.content[0].text)
+```
+
+```javascript basic.js
+import Anthropic from "@anthropic-ai/sdk";
+
+const anthropic = new Anthropic({
+  baseURL: "http://localhost:11434",
+  apiKey: "ollama", // required but ignored
+});
+
+const message = await anthropic.messages.create({
+  model: "qwen3-coder",
+  max_tokens: 1024,
+  messages: [{ role: "user", content: "Hello, how are you?" }],
+});
+
+console.log(message.content[0].text);
+```
+
+```shell basic.sh
+curl -X POST http://localhost:11434/v1/messages \
+-H "Content-Type: application/json" \
+-H "x-api-key: ollama" \
+-H "anthropic-version: 2023-06-01" \
+-d '{
+  "model": "qwen3-coder",
+  "max_tokens": 1024,
+  "messages": [{ "role": "user", "content": "Hello, how are you?" }]
+}'
+```
+
+</CodeGroup>
+
+### Streaming example
+
+<CodeGroup dropdown>
+
+```python streaming.py
+import anthropic
+
+client = anthropic.Anthropic(
+    base_url='http://localhost:11434',
+    api_key='ollama',
+)
+
+with client.messages.stream(
+    model='qwen3-coder',
+    max_tokens=1024,
+    messages=[{'role': 'user', 'content': 'Count from 1 to 10'}]
+) as stream:
+    for text in stream.text_stream:
+        print(text, end='', flush=True)
+```
+
+```javascript streaming.js
+import Anthropic from "@anthropic-ai/sdk";
+
+const anthropic = new Anthropic({
+  baseURL: "http://localhost:11434",
+  apiKey: "ollama",
+});
+
+const stream = await anthropic.messages.stream({
+  model: "qwen3-coder",
+  max_tokens: 1024,
+  messages: [{ role: "user", content: "Count from 1 to 10" }],
+});
+
+for await (const event of stream) {
+  if (
+    event.type === "content_block_delta" &&
+    event.delta.type === "text_delta"
+  ) {
+    process.stdout.write(event.delta.text);
+  }
+}
+```
+
+```shell streaming.sh
+curl -X POST http://localhost:11434/v1/messages \
+-H "Content-Type: application/json" \
+-d '{
+  "model": "qwen3-coder",
+  "max_tokens": 1024,
+  "stream": true,
+  "messages": [{ "role": "user", "content": "Count from 1 to 10" }]
+}'
+```
+
+</CodeGroup>
+
+### Tool calling example
+
+<CodeGroup dropdown>
+
+```python tools.py
+import anthropic
+
+client = anthropic.Anthropic(
+    base_url='http://localhost:11434',
+    api_key='ollama',
+)
+
+message = client.messages.create(
+    model='qwen3-coder',
+    max_tokens=1024,
+    tools=[
+        {
+            'name': 'get_weather',
+            'description': 'Get the current weather in a location',
+            'input_schema': {
+                'type': 'object',
+                'properties': {
+                    'location': {
+                        'type': 'string',
+                        'description': 'The city and state, e.g. San Francisco, CA'
+                    }
+                },
+                'required': ['location']
+            }
+        }
+    ],
+    messages=[{'role': 'user', 'content': "What's the weather in San Francisco?"}]
+)
+
+for block in message.content:
+    if block.type == 'tool_use':
+        print(f'Tool: {block.name}')
+        print(f'Input: {block.input}')
+```
+
+```javascript tools.js
+import Anthropic from "@anthropic-ai/sdk";
+
+const anthropic = new Anthropic({
+  baseURL: "http://localhost:11434",
+  apiKey: "ollama",
+});
+
+const message = await anthropic.messages.create({
+  model: "qwen3-coder",
+  max_tokens: 1024,
+  tools: [
+    {
+      name: "get_weather",
+      description: "Get the current weather in a location",
+      input_schema: {
+        type: "object",
+        properties: {
+          location: {
+            type: "string",
+            description: "The city and state, e.g. San Francisco, CA",
+          },
+        },
+        required: ["location"],
+      },
+    },
+  ],
+  messages: [{ role: "user", content: "What's the weather in San Francisco?" }],
+});
+
+for (const block of message.content) {
+  if (block.type === "tool_use") {
+    console.log("Tool:", block.name);
+    console.log("Input:", block.input);
+  }
+}
+```
+
+```shell tools.sh
+curl -X POST http://localhost:11434/v1/messages \
+-H "Content-Type: application/json" \
+-d '{
+  "model": "qwen3-coder",
+  "max_tokens": 1024,
+  "tools": [
+    {
+      "name": "get_weather",
+      "description": "Get the current weather in a location",
+      "input_schema": {
+        "type": "object",
+        "properties": {
+          "location": {
+            "type": "string",
+            "description": "The city and state"
+          }
+        },
+        "required": ["location"]
+      }
+    }
+  ],
+  "messages": [{ "role": "user", "content": "What is the weather in San Francisco?" }]
+}'
+```
+
+</CodeGroup>
+
+## Using with Claude Code
+
+[Claude Code](https://code.claude.com/docs/en/overview) can be configured to use Ollama as its backend:
+
+```shell
+ANTHROPIC_AUTH_TOKEN=ollama ANTHROPIC_BASE_URL=http://localhost:11434 ANTHROPIC_API_KEY=ollama claude --model qwen3-coder
+```
+
+Or set the environment variables in your shell profile:
+
+```shell
+export ANTHROPIC_AUTH_TOKEN=ollama
+export ANTHROPIC_BASE_URL=http://localhost:11434
+export ANTHROPIC_API_KEY=ollama
+```
+
+Then run Claude Code with any Ollama model:
+
+```shell
+# Local models
+claude --model qwen3-coder
+claude --model gpt-oss:20b
+
+# Cloud models
+claude --model glm-4.7:cloud
+claude --model minimax-m2.1:cloud
+```
+
+## Endpoints
+
+### `/v1/messages`
+
+#### Supported features
+
+- [x] Messages
+- [x] Streaming
+- [x] System prompts
+- [x] Multi-turn conversations
+- [x] Vision (images)
+- [x] Tools (function calling)
+- [x] Tool results
+- [x] Thinking/extended thinking
+
+#### Supported request fields
+
+- [x] `model`
+- [x] `max_tokens`
+- [x] `messages`
+  - [x] Text `content`
+  - [x] Image `content` (base64)
+  - [x] Array of content blocks
+  - [x] `tool_use` blocks
+  - [x] `tool_result` blocks
+  - [x] `thinking` blocks
+- [x] `system` (string or array)
+- [x] `stream`
+- [x] `temperature`
+- [x] `top_p`
+- [x] `top_k`
+- [x] `stop_sequences`
+- [x] `tools`
+- [x] `thinking`
+- [ ] `tool_choice`
+- [ ] `metadata`
+
+#### Supported response fields
+
+- [x] `id`
+- [x] `type`
+- [x] `role`
+- [x] `model`
+- [x] `content` (text, tool_use, thinking blocks)
+- [x] `stop_reason` (end_turn, max_tokens, tool_use)
+- [x] `usage` (input_tokens, output_tokens)
+
+#### Streaming events
+
+- [x] `message_start`
+- [x] `content_block_start`
+- [x] `content_block_delta` (text_delta, input_json_delta, thinking_delta)
+- [x] `content_block_stop`
+- [x] `message_delta`
+- [x] `message_stop`
+- [x] `ping`
+- [x] `error`
+
+## Models
+
+Ollama supports both local and cloud models.
+
+### Local models
+
+Pull a local model before use:
+
+```shell
+ollama pull qwen3-coder
+```
+
+Recommended local models:
+- `qwen3-coder` - Excellent for coding tasks
+- `gpt-oss:20b` - Strong general-purpose model
+
+### Cloud models
+
+Cloud models are available immediately without pulling:
+
+- `glm-4.7:cloud` - High-performance cloud model
+- `minimax-m2.1:cloud` - Fast cloud model
+
+### Default model names
+
+For tooling that relies on default Anthropic model names such as `claude-3-5-sonnet`, use `ollama cp` to copy an existing model name:
+
+```shell
+ollama cp qwen3-coder claude-3-5-sonnet
+```
+
+Afterwards, this new model name can be specified in the `model` field:
+
+```shell
+curl http://localhost:11434/v1/messages \
+    -H "Content-Type: application/json" \
+    -d '{
+        "model": "claude-3-5-sonnet",
+        "max_tokens": 1024,
+        "messages": [
+            {
+                "role": "user",
+                "content": "Hello!"
+            }
+        ]
+    }'
+```
+
+## Differences from the Anthropic API
+
+### Behavior differences
+
+- API key is accepted but not validated
+- `anthropic-version` header is accepted but not used
+- Token counts are approximations based on the underlying model's tokenizer
+
+### Not supported
+
+The following Anthropic API features are not currently supported:
+
+| Feature | Description |
+|---------|-------------|
+| `/v1/messages/count_tokens` | Token counting endpoint |
+| `tool_choice` | Forcing specific tool use or disabling tools |
+| `metadata` | Request metadata (user_id) |
+| Prompt caching | `cache_control` blocks for caching prefixes |
+| Batches API | `/v1/messages/batches` for async batch processing |
+| Citations | `citations` content blocks |
+| PDF support | `document` content blocks with PDF files |
+| Server-sent errors | `error` events during streaming (errors return HTTP status) |
+
+### Partial support
+
+| Feature | Status |
+|---------|--------|
+| Image content | Base64 images supported; URL images not supported |
+| Extended thinking | Basic support; `budget_tokens` accepted but not enforced |

docs/api/openai-compatibility.mdx

@@ -275,6 +275,73 @@ curl -X POST http://localhost:11434/v1/chat/completions \
 - [x] `dimensions`
 - [ ] `user`
 
+### `/v1/images/generations` (experimental)
+
+> Note: This endpoint is experimental and may change or be removed in future versions.
+
+Generate images using image generation models.
+
+<CodeGroup dropdown>
+
+```python images.py
+from openai import OpenAI
+
+client = OpenAI(
+    base_url='http://localhost:11434/v1/',
+    api_key='ollama',  # required but ignored
+)
+
+response = client.images.generate(
+    model='x/z-image-turbo',
+    prompt='A cute robot learning to paint',
+    size='1024x1024',
+    response_format='b64_json',
+)
+print(response.data[0].b64_json[:50] + '...')
+```
+
+```javascript images.js
+import OpenAI from "openai";
+
+const openai = new OpenAI({
+  baseURL: "http://localhost:11434/v1/",
+  apiKey: "ollama", // required but ignored
+});
+
+const response = await openai.images.generate({
+  model: "x/z-image-turbo",
+  prompt: "A cute robot learning to paint",
+  size: "1024x1024",
+  response_format: "b64_json",
+});
+
+console.log(response.data[0].b64_json.slice(0, 50) + "...");
+```
+
+```shell images.sh
+curl -X POST http://localhost:11434/v1/images/generations \
+-H "Content-Type: application/json" \
+-d '{
+  "model": "x/z-image-turbo",
+  "prompt": "A cute robot learning to paint",
+  "size": "1024x1024",
+  "response_format": "b64_json"
+}'
+```
+
+</CodeGroup>
+
+#### Supported request fields
+
+- [x] `model`
+- [x] `prompt`
+- [x] `size` (e.g. "1024x1024")
+- [x] `response_format` (only `b64_json` supported)
+- [ ] `n`
+- [ ] `quality`
+- [ ] `style`
+- [ ] `user`
+
 ### `/v1/responses`
 
 > Note: Added in Ollama v0.13.3

docs/capabilities/web-search.mdx

@@ -110,7 +110,7 @@ More Ollama [Python example](https://github.com/ollama/ollama-python/blob/main/e
 import { Ollama } from "ollama";
 
 const client = new Ollama();
-const results = await client.webSearch({ query: "what is ollama?" });
+const results = await client.webSearch("what is ollama?");
 console.log(JSON.stringify(results, null, 2));
 ```
 
@@ -213,7 +213,7 @@ models](https://ollama.com/models)\n\nAvailable for macOS, Windows, and Linux',
 import { Ollama } from "ollama";
 
 const client = new Ollama();
-const fetchResult = await client.webFetch({ url: "https://ollama.com" });
+const fetchResult = await client.webFetch("https://ollama.com");
 console.log(JSON.stringify(fetchResult, null, 2));
 ```
 

docs/docs.json

@@ -32,7 +32,9 @@
     "codeblocks": "system"
   },
   "contextual": {
-    "options": ["copy"]
+    "options": [
+      "copy"
+    ]
   },
   "navbar": {
     "links": [
@@ -52,7 +54,9 @@
       "display": "simple"
     },
     "examples": {
-      "languages": ["curl"]
+      "languages": [
+        "curl"
+      ]
     }
   },
   "redirects": [
@@ -97,6 +101,7 @@
           {
             "group": "Integrations",
             "pages": [
+              "/integrations/claude-code",
               "/integrations/vscode",
               "/integrations/jetbrains",
               "/integrations/codex",
@@ -106,7 +111,9 @@
               "/integrations/zed",
               "/integrations/roo-code",
               "/integrations/n8n",
-              "/integrations/xcode"
+              "/integrations/xcode",
+              "/integrations/onyx",
+              "/integrations/marimo"
             ]
           },
           {
@@ -139,7 +146,8 @@
               "/api/streaming",
               "/api/usage",
               "/api/errors",
-              "/api/openai-compatibility"
+              "/api/openai-compatibility",
+              "/api/anthropic-compatibility"
             ]
           },
           {

docs/faq.mdx

@@ -22,7 +22,7 @@ Please refer to the [GPU docs](./gpu).
 
 ## How can I specify the context window size?
 
-By default, Ollama uses a context window size of 2048 tokens.
+By default, Ollama uses a context window size of 4096 tokens.
 
 This can be overridden with the `OLLAMA_CONTEXT_LENGTH` environment variable. For example, to set the default context window to 8K, use:
 

docs/integrations/claude-code.mdx

@@ -0,0 +1,78 @@
+---
+title: Claude Code
+---
+
+Claude Code is Anthropic's agentic coding tool that can read, modify, and execute code in your working directory. 
+
+Open models can be used with Claude Code through Ollama's Anthropic-compatible API, enabling you to use models such as `qwen3-coder`, `gpt-oss:20b`, or other models.
+
+![Claude Code with Ollama](https://files.ollama.com/claude-code.png)
+
+## Install
+
+Install [Claude Code](https://code.claude.com/docs/en/overview):
+
+<CodeGroup>
+
+```shell macOS / Linux
+curl -fsSL https://claude.ai/install.sh | bash
+```
+
+```powershell Windows
+irm https://claude.ai/install.ps1 | iex
+```
+
+</CodeGroup>
+
+## Usage with Ollama
+
+Claude Code connects to Ollama using the Anthropic-compatible API.
+
+1. Set the environment variables:
+
+```shell
+export ANTHROPIC_AUTH_TOKEN=ollama
+export ANTHROPIC_BASE_URL=http://localhost:11434
+```
+
+2. Run Claude Code with an Ollama model:
+
+```shell
+claude --model gpt-oss:20b
+```
+
+Or run with environment variables inline:
+
+```shell
+ANTHROPIC_AUTH_TOKEN=ollama ANTHROPIC_BASE_URL=http://localhost:11434 claude --model gpt-oss:20b
+```
+
+**Note:** Claude Code requires a large context window. We recommend at least 32K tokens. See the [context length documentation](/context-length) for how to adjust context length in Ollama.
+
+## Connecting to ollama.com
+
+1. Create an [API key](https://ollama.com/settings/keys) on ollama.com
+2. Set the environment variables:
+
+```shell
+export ANTHROPIC_BASE_URL=https://ollama.com
+export ANTHROPIC_API_KEY=<your-api-key>
+```
+
+3. Run Claude Code with a cloud model:
+
+```shell
+claude --model glm-4.7:cloud
+```
+
+## Recommended Models
+
+### Cloud models
+- `glm-4.7:cloud` - High-performance cloud model
+- `minimax-m2.1:cloud` - Fast cloud model
+- `qwen3-coder:480b` - Large coding model
+
+### Local models
+- `qwen3-coder` - Excellent for coding tasks
+- `gpt-oss:20b` - Strong general-purpose model
+- `gpt-oss:120b` - Larger general-purpose model for more complex tasks

docs/integrations/marimo.mdx

@@ -0,0 +1,73 @@
+---
+title: marimo
+---
+
+## Install
+
+Install [marimo](https://marimo.io). You can use `pip` or `uv` for this. You 
+can also use `uv` to create a sandboxed environment for marimo by running:
+
+```
+uvx marimo edit --sandbox notebook.py
+```
+
+## Usage with Ollama
+
+1. In marimo, go to the user settings and go to the AI tab. From here
+you can find and configure Ollama as an AI provider. For local use you
+would typically point the base url to `http://localhost:11434/v1`.
+
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/marimo-settings.png" 
+    alt="Ollama settings in marimo"
+    width="50%"
+  />
+</div>
+
+2. Once the AI provider is set up, you can turn on/off specific AI models you'd like to access. 
+
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/marimo-models.png" 
+    alt="Selecting an Ollama model"
+    width="50%"
+  />
+</div>
+
+3. You can also add a model to the list of available models by scrolling to the bottom and using the UI there. 
+
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/marimo-add-model.png" 
+    alt="Adding a new Ollama model"
+    width="50%"
+  />
+</div>
+
+4. Once configured, you can now use Ollama for AI chats in marimo.
+
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/marimo-chat.png" 
+    alt="Configure code completion"
+    width="50%"
+  />
+</div>
+
+4. Alternatively, you can now use Ollama for **inline code completion** in marimo. This can be configured in the "AI Features" tab. 
+
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/marimo-code-completion.png" 
+    alt="Configure code completion"
+    width="50%"
+  />
+</div>
+
+
+## Connecting to ollama.com
+
+1. Sign in to ollama cloud via `ollama signin` 
+2. In the ollama model settings add a model that ollama hosts, like `gpt-oss:120b`.
+3. You can now refer to this model in marimo!

docs/integrations/onyx.mdx

@@ -0,0 +1,63 @@
+---
+title: Onyx
+---
+
+## Overview
+[Onyx](http://onyx.app/) is a self-hostable Chat UI that integrates with all Ollama models. Features include:
+- Creating custom Agents
+- Web search
+- Deep Research
+- RAG over uploaded documents and connected apps
+- Connectors to applications like Google Drive, Email, Slack, etc.
+- MCP and OpenAPI Actions support
+- Image generation
+- User/Groups management, RBAC, SSO, etc.
+
+Onyx can be deployed for single users or large organizations.
+
+## Install Onyx
+
+Deploy Onyx with the [quickstart guide](https://docs.onyx.app/deployment/getting_started/quickstart).
+
+<Info>
+Resourcing/scaling docs [here](https://docs.onyx.app/deployment/getting_started/resourcing).
+</Info>
+
+## Usage with Ollama 
+
+1. Login to your Onyx deployment (create an account first).
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/onyx-login.png" 
+    alt="Onyx Login Page"
+    width="75%"
+  />
+</div>
+2. In the set-up process select `Ollama` as the LLM provider.
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/onyx-ollama-llm.png" 
+    alt="Onyx Set Up Form"
+    width="75%"
+  />
+</div>
+3. Provide your **Ollama API URL** and select your models.
+<Note>If you're running Onyx in Docker, to access your computer's local network use `http://host.docker.internal` instead of `http://127.0.0.1`.</Note>
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/onyx-ollama-form.png" 
+    alt="Selecting Ollama Models"
+    width="75%"
+  />
+</div>
+
+You can also easily connect up Onyx Cloud with the `Ollama Cloud` tab of the setup.
+
+## Send your first query
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/onyx-query.png" 
+    alt="Onyx Query Example"
+    width="75%"
+  />
+</div>

docs/linux.mdx

@@ -20,8 +20,8 @@ curl -fsSL https://ollama.com/install.sh | sh
 Download and extract the package:
 
 ```shell
-curl -fsSL https://ollama.com/download/ollama-linux-amd64.tgz \
-    | sudo tar zx -C /usr
+curl -fsSL https://ollama.com/download/ollama-linux-amd64.tar.zst \
+    | sudo tar x -C /usr
 ```
 
 Start Ollama:
@@ -41,8 +41,8 @@ ollama -v
 If you have an AMD GPU, also download and extract the additional ROCm package:
 
 ```shell
-curl -fsSL https://ollama.com/download/ollama-linux-amd64-rocm.tgz \
-    | sudo tar zx -C /usr
+curl -fsSL https://ollama.com/download/ollama-linux-amd64-rocm.tar.zst \
+    | sudo tar x -C /usr
 ```
 
 ### ARM64 install
@@ -50,8 +50,8 @@ curl -fsSL https://ollama.com/download/ollama-linux-amd64-rocm.tgz \
 Download and extract the ARM64-specific package:
 
 ```shell
-curl -fsSL https://ollama.com/download/ollama-linux-arm64.tgz \
-    | sudo tar zx -C /usr
+curl -fsSL https://ollama.com/download/ollama-linux-arm64.tar.zst \
+    | sudo tar x -C /usr
 ```
 
 ### Adding Ollama as a startup service (recommended)
@@ -146,8 +146,8 @@ curl -fsSL https://ollama.com/install.sh | sh
 Or by re-downloading Ollama:
 
 ```shell
-curl -fsSL https://ollama.com/download/ollama-linux-amd64.tgz \
-    | sudo tar zx -C /usr
+curl -fsSL https://ollama.com/download/ollama-linux-amd64.tar.zst \
+    | sudo tar x -C /usr
 ```
 
 ## Installing specific versions

docs/troubleshooting.md

@@ -1,3 +0,0 @@
-# Troubleshooting
-
-For troubleshooting, see [https://docs.ollama.com/troubleshooting](https://docs.ollama.com/troubleshooting)

fs/config.go

@@ -1,5 +1,7 @@
 package fs
 
+import "iter"
+
 type Config interface {
 	Architecture() string
 	String(string, ...string) string
@@ -11,4 +13,8 @@ type Config interface {
 	Ints(string, ...[]int32) []int32
 	Floats(string, ...[]float32) []float32
 	Bools(string, ...[]bool) []bool
+
+	Len() int
+	Keys() iter.Seq[string]
+	Value(key string) any
 }

fs/ggml/ggml.go

@@ -6,7 +6,9 @@ import (
 	"errors"
 	"fmt"
 	"io"
+	"iter"
 	"log/slog"
+	"maps"
 	"math"
 	"slices"
 	"strings"
@@ -239,6 +241,18 @@ func (kv KV) Bools(key string, defaultValue ...[]bool) []bool {
 	return val.values
 }
 
+func (kv KV) Len() int {
+	return len(kv)
+}
+
+func (kv KV) Keys() iter.Seq[string] {
+	return maps.Keys(kv)
+}
+
+func (kv KV) Value(key string) any {
+	return kv[key]
+}
+
 func (kv KV) OllamaEngineRequired() bool {
 	return slices.Contains([]string{
 		"bert",
@@ -255,6 +269,8 @@ func (kv KV) OllamaEngineRequired() bool {
 		"qwen25vl",
 		"qwen3", "qwen3moe",
 		"qwen3vl", "qwen3vlmoe",
+		"glm4moelite",
+		"lfm2",
 	}, kv.Architecture())
 }
 
@@ -842,7 +858,9 @@ func (f GGML) FlashAttention() bool {
 	return slices.Contains([]string{
 		"bert",
 		"gemma3",
+		"glm4moelite",
 		"gptoss", "gpt-oss",
+		"lfm2",
 		"mistral3",
 		"olmo3",
 		"qwen3", "qwen3moe",

fs/ggml/gguf.go

@@ -8,12 +8,12 @@ import (
 	"fmt"
 	"io"
 	"log/slog"
-	"maps"
 	"os"
 	"runtime"
 	"slices"
 	"strings"
 
+	"github.com/ollama/ollama/fs"
 	"golang.org/x/sync/errgroup"
 )
 
@@ -508,7 +508,7 @@ func writeGGUFArray[S ~[]E, E any](w io.Writer, t uint32, s S) error {
 	return binary.Write(w, binary.LittleEndian, s)
 }
 
-func WriteGGUF(f *os.File, kv KV, ts []*Tensor) error {
+func WriteGGUF(f *os.File, kv fs.Config, ts []*Tensor) error {
 	arch := kv.String("general.architecture")
 	if arch == "" {
 		return fmt.Errorf("architecture not set")
@@ -526,12 +526,12 @@ func WriteGGUF(f *os.File, kv KV, ts []*Tensor) error {
 		return err
 	}
 
-	if err := binary.Write(f, binary.LittleEndian, uint64(len(kv))); err != nil {
+	if err := binary.Write(f, binary.LittleEndian, uint64(kv.Len())); err != nil {
 		return err
 	}
 
-	for _, key := range slices.Sorted(maps.Keys(kv)) {
-		if err := ggufWriteKV(f, arch, key, kv[key]); err != nil {
+	for _, key := range slices.Sorted(kv.Keys()) {
+		if err := ggufWriteKV(f, arch, key, kv.Value(key)); err != nil {
 			return err
 		}
 	}

integration/imagegen_test.go

@@ -0,0 +1,174 @@
+//go:build integration
+
+package integration
+
+import (
+	"bytes"
+	"context"
+	"encoding/base64"
+	"encoding/json"
+	"fmt"
+	"net/http"
+	"strings"
+	"testing"
+	"time"
+
+	"github.com/ollama/ollama/api"
+	imagegenapi "github.com/ollama/ollama/x/imagegen/api"
+)
+
+func TestImageGeneration(t *testing.T) {
+	skipUnderMinVRAM(t, 8)
+
+	type testCase struct {
+		imageGenModel string
+		visionModel   string
+		prompt        string
+		expectedWords []string
+	}
+
+	testCases := []testCase{
+		{
+			imageGenModel: "jmorgan/z-image-turbo",
+			visionModel:   "llama3.2-vision",
+			prompt:        "A cartoon style llama flying like a superhero through the air with clouds in the background",
+			expectedWords: []string{"llama", "flying", "cartoon", "cloud", "sky", "superhero", "air", "animal", "camelid"},
+		},
+	}
+
+	for _, tc := range testCases {
+		t.Run(fmt.Sprintf("%s->%s", tc.imageGenModel, tc.visionModel), func(t *testing.T) {
+			ctx, cancel := context.WithTimeout(context.Background(), 10*time.Minute)
+			defer cancel()
+
+			client, testEndpoint, cleanup := InitServerConnection(ctx, t)
+			defer cleanup()
+
+			// Pull both models
+			if err := PullIfMissing(ctx, client, tc.imageGenModel); err != nil {
+				t.Fatalf("failed to pull image gen model: %v", err)
+			}
+			if err := PullIfMissing(ctx, client, tc.visionModel); err != nil {
+				t.Fatalf("failed to pull vision model: %v", err)
+			}
+
+			// Generate the image
+			t.Logf("Generating image with prompt: %s", tc.prompt)
+			imageBase64, err := generateImage(ctx, testEndpoint, tc.imageGenModel, tc.prompt)
+			if err != nil {
+				if strings.Contains(err.Error(), "image generation not available") {
+					t.Skip("Target system does not support image generation")
+				} else if strings.Contains(err.Error(), "executable file not found in") { // Windows pattern, not yet supported
+					t.Skip("Windows does not support image generation yet")
+				} else if strings.Contains(err.Error(), "CUDA driver version is insufficient") {
+					t.Skip("Driver is too old")
+				} else if strings.Contains(err.Error(), "insufficient memory for image generation") {
+					t.Skip("insufficient memory for image generation")
+				} else if strings.Contains(err.Error(), "error while loading shared libraries: libcuda.so.1") { // AMD GPU or CPU
+					t.Skip("CUDA GPU is not available")
+				} else if strings.Contains(err.Error(), "ollama-mlx: no such file or directory") {
+					// most likely linux arm - not supported yet
+					t.Skip("unsupported architecture")
+				}
+				t.Fatalf("failed to generate image: %v", err)
+			}
+
+			imageData, err := base64.StdEncoding.DecodeString(imageBase64)
+			if err != nil {
+				t.Fatalf("failed to decode image: %v", err)
+			}
+			t.Logf("Generated image: %d bytes", len(imageData))
+
+			// Preload vision model and check GPU loading
+			err = client.Generate(ctx, &api.GenerateRequest{Model: tc.visionModel}, func(response api.GenerateResponse) error { return nil })
+			if err != nil {
+				t.Fatalf("failed to load vision model: %v", err)
+			}
+
+			// Use vision model to describe the image
+			chatReq := api.ChatRequest{
+				Model: tc.visionModel,
+				Messages: []api.Message{
+					{
+						Role:    "user",
+						Content: "Describe this image in detail. What is shown? What style is it? What is the main subject doing?",
+						Images:  []api.ImageData{imageData},
+					},
+				},
+				Stream: &stream,
+				Options: map[string]any{
+					"seed":        42,
+					"temperature": 0.0,
+				},
+			}
+
+			// Verify the vision model's response contains expected keywords
+			response := DoChat(ctx, t, client, chatReq, tc.expectedWords, 240*time.Second, 30*time.Second)
+			if response != nil {
+				t.Logf("Vision model response: %s", response.Content)
+
+				// Additional detailed check for keywords
+				content := strings.ToLower(response.Content)
+				foundWords := []string{}
+				missingWords := []string{}
+				for _, word := range tc.expectedWords {
+					if strings.Contains(content, word) {
+						foundWords = append(foundWords, word)
+					} else {
+						missingWords = append(missingWords, word)
+					}
+				}
+				t.Logf("Found keywords: %v", foundWords)
+				if len(missingWords) > 0 {
+					t.Logf("Missing keywords (at least one was found so test passed): %v", missingWords)
+				}
+			}
+		})
+	}
+}
+
+// generateImage calls the OpenAI-compatible image generation API and returns the base64 image data
+func generateImage(ctx context.Context, endpoint, model, prompt string) (string, error) {
+	reqBody := imagegenapi.ImageGenerationRequest{
+		Model:          model,
+		Prompt:         prompt,
+		N:              1,
+		Size:           "512x512",
+		ResponseFormat: "b64_json",
+	}
+
+	jsonBody, err := json.Marshal(reqBody)
+	if err != nil {
+		return "", fmt.Errorf("failed to marshal request: %w", err)
+	}
+
+	url := fmt.Sprintf("http://%s/v1/images/generations", endpoint)
+	req, err := http.NewRequestWithContext(ctx, "POST", url, bytes.NewReader(jsonBody))
+	if err != nil {
+		return "", fmt.Errorf("failed to create request: %w", err)
+	}
+	req.Header.Set("Content-Type", "application/json")
+
+	resp, err := http.DefaultClient.Do(req)
+	if err != nil {
+		return "", fmt.Errorf("failed to send request: %w", err)
+	}
+	defer resp.Body.Close()
+
+	if resp.StatusCode != http.StatusOK {
+		var buf bytes.Buffer
+		buf.ReadFrom(resp.Body)
+		return "", fmt.Errorf("unexpected status code %d: %s", resp.StatusCode, buf.String())
+	}
+
+	var genResp imagegenapi.ImageGenerationResponse
+	if err := json.NewDecoder(resp.Body).Decode(&genResp); err != nil {
+		return "", fmt.Errorf("failed to decode response: %w", err)
+	}
+
+	if len(genResp.Data) == 0 {
+		return "", fmt.Errorf("no image data in response")
+	}
+
+	return genResp.Data[0].B64JSON, nil
+}

integration/tools_test.go

@@ -131,7 +131,7 @@ func TestAPIToolCalling(t *testing.T) {
 					t.Errorf("unexpected tool called: got %q want %q", lastToolCall.Function.Name, "get_weather")
 				}
 
-				if _, ok := lastToolCall.Function.Arguments["location"]; !ok {
+				if _, ok := lastToolCall.Function.Arguments.Get("location"); !ok {
 					t.Errorf("expected tool arguments to include 'location', got: %s", lastToolCall.Function.Arguments.String())
 				}
 			case <-ctx.Done():

llm/server.go

@@ -1464,6 +1464,12 @@ type CompletionRequest struct {
 
 	// TopLogprobs specifies the number of most likely alternative tokens to return (0-20)
 	TopLogprobs int
+
+	// Image generation fields
+	Width  int32 `json:"width,omitempty"`
+	Height int32 `json:"height,omitempty"`
+	Steps  int32 `json:"steps,omitempty"`
+	Seed   int64 `json:"seed,omitempty"`
 }
 
 // DoneReason represents the reason why a completion response is done
@@ -1512,6 +1518,15 @@ type CompletionResponse struct {
 
 	// Logprobs contains log probability information if requested
 	Logprobs []Logprob `json:"logprobs,omitempty"`
+
+	// Image contains base64-encoded image data for image generation
+	Image string `json:"image,omitempty"`
+
+	// Step is the current step in image generation
+	Step int `json:"step,omitempty"`
+
+	// TotalSteps is the total number of steps for image generation
+	TotalSteps int `json:"total_steps,omitempty"`
 }
 
 func (s *llmServer) Completion(ctx context.Context, req CompletionRequest, fn func(CompletionResponse)) error {

middleware/anthropic.go

@@ -0,0 +1,152 @@
+package middleware
+
+import (
+	"bytes"
+	"encoding/json"
+	"fmt"
+	"io"
+	"net/http"
+
+	"github.com/gin-gonic/gin"
+
+	"github.com/ollama/ollama/anthropic"
+	"github.com/ollama/ollama/api"
+)
+
+// AnthropicWriter wraps the response writer to transform Ollama responses to Anthropic format
+type AnthropicWriter struct {
+	BaseWriter
+	stream    bool
+	id        string
+	model     string
+	converter *anthropic.StreamConverter
+}
+
+func (w *AnthropicWriter) writeError(data []byte) (int, error) {
+	var errData struct {
+		Error string `json:"error"`
+	}
+	if err := json.Unmarshal(data, &errData); err != nil {
+		return 0, err
+	}
+
+	w.ResponseWriter.Header().Set("Content-Type", "application/json")
+	err := json.NewEncoder(w.ResponseWriter).Encode(anthropic.NewError(w.ResponseWriter.Status(), errData.Error))
+	if err != nil {
+		return 0, err
+	}
+
+	return len(data), nil
+}
+
+func (w *AnthropicWriter) writeEvent(eventType string, data any) error {
+	d, err := json.Marshal(data)
+	if err != nil {
+		return err
+	}
+	_, err = w.ResponseWriter.Write([]byte(fmt.Sprintf("event: %s\ndata: %s\n\n", eventType, d)))
+	if err != nil {
+		return err
+	}
+	if f, ok := w.ResponseWriter.(http.Flusher); ok {
+		f.Flush()
+	}
+	return nil
+}
+
+func (w *AnthropicWriter) writeResponse(data []byte) (int, error) {
+	var chatResponse api.ChatResponse
+	err := json.Unmarshal(data, &chatResponse)
+	if err != nil {
+		return 0, err
+	}
+
+	if w.stream {
+		w.ResponseWriter.Header().Set("Content-Type", "text/event-stream")
+
+		events := w.converter.Process(chatResponse)
+		for _, event := range events {
+			if err := w.writeEvent(event.Event, event.Data); err != nil {
+				return 0, err
+			}
+		}
+		return len(data), nil
+	}
+
+	w.ResponseWriter.Header().Set("Content-Type", "application/json")
+	response := anthropic.ToMessagesResponse(w.id, chatResponse)
+	return len(data), json.NewEncoder(w.ResponseWriter).Encode(response)
+}
+
+func (w *AnthropicWriter) Write(data []byte) (int, error) {
+	code := w.ResponseWriter.Status()
+	if code != http.StatusOK {
+		return w.writeError(data)
+	}
+
+	return w.writeResponse(data)
+}
+
+// AnthropicMessagesMiddleware handles Anthropic Messages API requests
+func AnthropicMessagesMiddleware() gin.HandlerFunc {
+	return func(c *gin.Context) {
+		var req anthropic.MessagesRequest
+		err := c.ShouldBindJSON(&req)
+		if err != nil {
+			c.AbortWithStatusJSON(http.StatusBadRequest, anthropic.NewError(http.StatusBadRequest, err.Error()))
+			return
+		}
+
+		if req.Model == "" {
+			c.AbortWithStatusJSON(http.StatusBadRequest, anthropic.NewError(http.StatusBadRequest, "model is required"))
+			return
+		}
+
+		if req.MaxTokens <= 0 {
+			c.AbortWithStatusJSON(http.StatusBadRequest, anthropic.NewError(http.StatusBadRequest, "max_tokens is required and must be positive"))
+			return
+		}
+
+		if len(req.Messages) == 0 {
+			c.AbortWithStatusJSON(http.StatusBadRequest, anthropic.NewError(http.StatusBadRequest, "messages is required"))
+			return
+		}
+
+		chatReq, err := anthropic.FromMessagesRequest(req)
+		if err != nil {
+			c.AbortWithStatusJSON(http.StatusBadRequest, anthropic.NewError(http.StatusBadRequest, err.Error()))
+			return
+		}
+
+		// Set think to nil when being used with Anthropic API to connect to tools like claude code
+		c.Set("relax_thinking", true)
+
+		var b bytes.Buffer
+		if err := json.NewEncoder(&b).Encode(chatReq); err != nil {
+			c.AbortWithStatusJSON(http.StatusInternalServerError, anthropic.NewError(http.StatusInternalServerError, err.Error()))
+			return
+		}
+
+		c.Request.Body = io.NopCloser(&b)
+
+		messageID := anthropic.GenerateMessageID()
+
+		w := &AnthropicWriter{
+			BaseWriter: BaseWriter{ResponseWriter: c.Writer},
+			stream:     req.Stream,
+			id:         messageID,
+			model:      req.Model,
+			converter:  anthropic.NewStreamConverter(messageID, req.Model),
+		}
+
+		if req.Stream {
+			c.Writer.Header().Set("Content-Type", "text/event-stream")
+			c.Writer.Header().Set("Cache-Control", "no-cache")
+			c.Writer.Header().Set("Connection", "keep-alive")
+		}
+
+		c.Writer = w
+
+		c.Next()
+	}
+}

middleware/anthropic_test.go

@@ -0,0 +1,607 @@
+package middleware
+
+import (
+	"bytes"
+	"encoding/json"
+	"io"
+	"net/http"
+	"net/http/httptest"
+	"strings"
+	"testing"
+
+	"github.com/gin-gonic/gin"
+	"github.com/google/go-cmp/cmp"
+	"github.com/google/go-cmp/cmp/cmpopts"
+
+	"github.com/ollama/ollama/anthropic"
+	"github.com/ollama/ollama/api"
+)
+
+func captureAnthropicRequest(capturedRequest any) gin.HandlerFunc {
+	return func(c *gin.Context) {
+		bodyBytes, _ := io.ReadAll(c.Request.Body)
+		c.Request.Body = io.NopCloser(bytes.NewReader(bodyBytes))
+		_ = json.Unmarshal(bodyBytes, capturedRequest)
+		c.Next()
+	}
+}
+
+// testProps creates ToolPropertiesMap from a map (convenience function for tests)
+func testProps(m map[string]api.ToolProperty) *api.ToolPropertiesMap {
+	props := api.NewToolPropertiesMap()
+	for k, v := range m {
+		props.Set(k, v)
+	}
+	return props
+}
+
+func TestAnthropicMessagesMiddleware(t *testing.T) {
+	type testCase struct {
+		name string
+		body string
+		req  api.ChatRequest
+		err  anthropic.ErrorResponse
+	}
+
+	var capturedRequest *api.ChatRequest
+	stream := true
+
+	testCases := []testCase{
+		{
+			name: "basic message",
+			body: `{
+				"model": "test-model",
+				"max_tokens": 1024,
+				"messages": [
+					{"role": "user", "content": "Hello"}
+				]
+			}`,
+			req: api.ChatRequest{
+				Model: "test-model",
+				Messages: []api.Message{
+					{Role: "user", Content: "Hello"},
+				},
+				Options: map[string]any{"num_predict": 1024},
+				Stream:  &False,
+			},
+		},
+		{
+			name: "with system prompt",
+			body: `{
+				"model": "test-model",
+				"max_tokens": 1024,
+				"system": "You are helpful.",
+				"messages": [
+					{"role": "user", "content": "Hello"}
+				]
+			}`,
+			req: api.ChatRequest{
+				Model: "test-model",
+				Messages: []api.Message{
+					{Role: "system", Content: "You are helpful."},
+					{Role: "user", Content: "Hello"},
+				},
+				Options: map[string]any{"num_predict": 1024},
+				Stream:  &False,
+			},
+		},
+		{
+			name: "with options",
+			body: `{
+				"model": "test-model",
+				"max_tokens": 2048,
+				"temperature": 0.7,
+				"top_p": 0.9,
+				"top_k": 40,
+				"stop_sequences": ["\n", "END"],
+				"messages": [
+					{"role": "user", "content": "Hello"}
+				]
+			}`,
+			req: api.ChatRequest{
+				Model: "test-model",
+				Messages: []api.Message{
+					{Role: "user", Content: "Hello"},
+				},
+				Options: map[string]any{
+					"num_predict": 2048,
+					"temperature": 0.7,
+					"top_p":       0.9,
+					"top_k":       40,
+					"stop":        []string{"\n", "END"},
+				},
+				Stream: &False,
+			},
+		},
+		{
+			name: "streaming",
+			body: `{
+				"model": "test-model",
+				"max_tokens": 1024,
+				"stream": true,
+				"messages": [
+					{"role": "user", "content": "Hello"}
+				]
+			}`,
+			req: api.ChatRequest{
+				Model: "test-model",
+				Messages: []api.Message{
+					{Role: "user", Content: "Hello"},
+				},
+				Options: map[string]any{"num_predict": 1024},
+				Stream:  &stream,
+			},
+		},
+		{
+			name: "with tools",
+			body: `{
+				"model": "test-model",
+				"max_tokens": 1024,
+				"messages": [
+					{"role": "user", "content": "What's the weather?"}
+				],
+				"tools": [{
+					"name": "get_weather",
+					"description": "Get current weather",
+					"input_schema": {
+						"type": "object",
+						"properties": {
+							"location": {"type": "string"}
+						},
+						"required": ["location"]
+					}
+				}]
+			}`,
+			req: api.ChatRequest{
+				Model: "test-model",
+				Messages: []api.Message{
+					{Role: "user", Content: "What's the weather?"},
+				},
+				Tools: []api.Tool{
+					{
+						Type: "function",
+						Function: api.ToolFunction{
+							Name:        "get_weather",
+							Description: "Get current weather",
+							Parameters: api.ToolFunctionParameters{
+								Type:     "object",
+								Required: []string{"location"},
+								Properties: testProps(map[string]api.ToolProperty{
+									"location": {Type: api.PropertyType{"string"}},
+								}),
+							},
+						},
+					},
+				},
+				Options: map[string]any{"num_predict": 1024},
+				Stream:  &False,
+			},
+		},
+		{
+			name: "with tool result",
+			body: `{
+				"model": "test-model",
+				"max_tokens": 1024,
+				"messages": [
+					{"role": "user", "content": "What's the weather?"},
+					{"role": "assistant", "content": [
+						{"type": "tool_use", "id": "call_123", "name": "get_weather", "input": {"location": "Paris"}}
+					]},
+					{"role": "user", "content": [
+						{"type": "tool_result", "tool_use_id": "call_123", "content": "Sunny, 22°C"}
+					]}
+				]
+			}`,
+			req: api.ChatRequest{
+				Model: "test-model",
+				Messages: []api.Message{
+					{Role: "user", Content: "What's the weather?"},
+					{
+						Role: "assistant",
+						ToolCalls: []api.ToolCall{
+							{
+								ID: "call_123",
+								Function: api.ToolCallFunction{
+									Name:      "get_weather",
+									Arguments: testArgs(map[string]any{"location": "Paris"}),
+								},
+							},
+						},
+					},
+					{Role: "tool", Content: "Sunny, 22°C", ToolCallID: "call_123"},
+				},
+				Options: map[string]any{"num_predict": 1024},
+				Stream:  &False,
+			},
+		},
+		{
+			name: "with thinking enabled",
+			body: `{
+				"model": "test-model",
+				"max_tokens": 1024,
+				"thinking": {"type": "enabled", "budget_tokens": 1000},
+				"messages": [
+					{"role": "user", "content": "Hello"}
+				]
+			}`,
+			req: api.ChatRequest{
+				Model: "test-model",
+				Messages: []api.Message{
+					{Role: "user", Content: "Hello"},
+				},
+				Options: map[string]any{"num_predict": 1024},
+				Stream:  &False,
+				Think:   &api.ThinkValue{Value: true},
+			},
+		},
+		{
+			name: "missing model error",
+			body: `{
+				"max_tokens": 1024,
+				"messages": [
+					{"role": "user", "content": "Hello"}
+				]
+			}`,
+			err: anthropic.ErrorResponse{
+				Type: "error",
+				Error: anthropic.Error{
+					Type:    "invalid_request_error",
+					Message: "model is required",
+				},
+			},
+		},
+		{
+			name: "missing max_tokens error",
+			body: `{
+				"model": "test-model",
+				"messages": [
+					{"role": "user", "content": "Hello"}
+				]
+			}`,
+			err: anthropic.ErrorResponse{
+				Type: "error",
+				Error: anthropic.Error{
+					Type:    "invalid_request_error",
+					Message: "max_tokens is required and must be positive",
+				},
+			},
+		},
+		{
+			name: "missing messages error",
+			body: `{
+				"model": "test-model",
+				"max_tokens": 1024
+			}`,
+			err: anthropic.ErrorResponse{
+				Type: "error",
+				Error: anthropic.Error{
+					Type:    "invalid_request_error",
+					Message: "messages is required",
+				},
+			},
+		},
+		{
+			name: "tool_use missing id error",
+			body: `{
+				"model": "test-model",
+				"max_tokens": 1024,
+				"messages": [
+					{"role": "assistant", "content": [
+						{"type": "tool_use", "name": "test"}
+					]}
+				]
+			}`,
+			err: anthropic.ErrorResponse{
+				Type: "error",
+				Error: anthropic.Error{
+					Type:    "invalid_request_error",
+					Message: "tool_use block missing required 'id' field",
+				},
+			},
+		},
+	}
+
+	endpoint := func(c *gin.Context) {
+		c.Status(http.StatusOK)
+	}
+
+	gin.SetMode(gin.TestMode)
+	router := gin.New()
+	router.Use(AnthropicMessagesMiddleware(), captureAnthropicRequest(&capturedRequest))
+	router.Handle(http.MethodPost, "/v1/messages", endpoint)
+
+	for _, tc := range testCases {
+		t.Run(tc.name, func(t *testing.T) {
+			req, _ := http.NewRequest(http.MethodPost, "/v1/messages", strings.NewReader(tc.body))
+			req.Header.Set("Content-Type", "application/json")
+
+			defer func() { capturedRequest = nil }()
+
+			resp := httptest.NewRecorder()
+			router.ServeHTTP(resp, req)
+
+			if tc.err.Type != "" {
+				// Expect error
+				if resp.Code == http.StatusOK {
+					t.Fatalf("expected error response, got 200 OK")
+				}
+				var errResp anthropic.ErrorResponse
+				if err := json.Unmarshal(resp.Body.Bytes(), &errResp); err != nil {
+					t.Fatalf("failed to unmarshal error: %v", err)
+				}
+				if errResp.Type != tc.err.Type {
+					t.Errorf("expected error type %q, got %q", tc.err.Type, errResp.Type)
+				}
+				if errResp.Error.Type != tc.err.Error.Type {
+					t.Errorf("expected error.type %q, got %q", tc.err.Error.Type, errResp.Error.Type)
+				}
+				if errResp.Error.Message != tc.err.Error.Message {
+					t.Errorf("expected error.message %q, got %q", tc.err.Error.Message, errResp.Error.Message)
+				}
+				return
+			}
+
+			if resp.Code != http.StatusOK {
+				t.Fatalf("unexpected status code: %d, body: %s", resp.Code, resp.Body.String())
+			}
+
+			if capturedRequest == nil {
+				t.Fatal("request was not captured")
+			}
+
+			// Compare relevant fields
+			if capturedRequest.Model != tc.req.Model {
+				t.Errorf("model mismatch: got %q, want %q", capturedRequest.Model, tc.req.Model)
+			}
+
+			if diff := cmp.Diff(tc.req.Messages, capturedRequest.Messages,
+				cmpopts.IgnoreUnexported(api.ToolCallFunctionArguments{}, api.ToolPropertiesMap{})); diff != "" {
+				t.Errorf("messages mismatch (-want +got):\n%s", diff)
+			}
+
+			if tc.req.Stream != nil && capturedRequest.Stream != nil {
+				if *tc.req.Stream != *capturedRequest.Stream {
+					t.Errorf("stream mismatch: got %v, want %v", *capturedRequest.Stream, *tc.req.Stream)
+				}
+			}
+
+			if tc.req.Think != nil {
+				if capturedRequest.Think == nil {
+					t.Error("expected Think to be set")
+				} else if capturedRequest.Think.Value != tc.req.Think.Value {
+					t.Errorf("Think mismatch: got %v, want %v", capturedRequest.Think.Value, tc.req.Think.Value)
+				}
+			}
+		})
+	}
+}
+
+func TestAnthropicMessagesMiddleware_Headers(t *testing.T) {
+	gin.SetMode(gin.TestMode)
+
+	t.Run("streaming sets correct headers", func(t *testing.T) {
+		router := gin.New()
+		router.Use(AnthropicMessagesMiddleware())
+		router.POST("/v1/messages", func(c *gin.Context) {
+			// Check headers were set
+			if c.Writer.Header().Get("Content-Type") != "text/event-stream" {
+				t.Errorf("expected Content-Type text/event-stream, got %q", c.Writer.Header().Get("Content-Type"))
+			}
+			if c.Writer.Header().Get("Cache-Control") != "no-cache" {
+				t.Errorf("expected Cache-Control no-cache, got %q", c.Writer.Header().Get("Cache-Control"))
+			}
+			c.Status(http.StatusOK)
+		})
+
+		body := `{"model": "test", "max_tokens": 100, "stream": true, "messages": [{"role": "user", "content": "Hi"}]}`
+		req, _ := http.NewRequest(http.MethodPost, "/v1/messages", strings.NewReader(body))
+		req.Header.Set("Content-Type", "application/json")
+
+		resp := httptest.NewRecorder()
+		router.ServeHTTP(resp, req)
+	})
+}
+
+func TestAnthropicMessagesMiddleware_InvalidJSON(t *testing.T) {
+	gin.SetMode(gin.TestMode)
+	router := gin.New()
+	router.Use(AnthropicMessagesMiddleware())
+	router.POST("/v1/messages", func(c *gin.Context) {
+		c.Status(http.StatusOK)
+	})
+
+	req, _ := http.NewRequest(http.MethodPost, "/v1/messages", strings.NewReader(`{invalid json`))
+	req.Header.Set("Content-Type", "application/json")
+
+	resp := httptest.NewRecorder()
+	router.ServeHTTP(resp, req)
+
+	if resp.Code != http.StatusBadRequest {
+		t.Errorf("expected status 400, got %d", resp.Code)
+	}
+
+	var errResp anthropic.ErrorResponse
+	if err := json.Unmarshal(resp.Body.Bytes(), &errResp); err != nil {
+		t.Fatalf("failed to unmarshal error: %v", err)
+	}
+
+	if errResp.Type != "error" {
+		t.Errorf("expected type 'error', got %q", errResp.Type)
+	}
+	if errResp.Error.Type != "invalid_request_error" {
+		t.Errorf("expected error type 'invalid_request_error', got %q", errResp.Error.Type)
+	}
+}
+
+func TestAnthropicWriter_NonStreaming(t *testing.T) {
+	gin.SetMode(gin.TestMode)
+
+	router := gin.New()
+	router.Use(AnthropicMessagesMiddleware())
+	router.POST("/v1/messages", func(c *gin.Context) {
+		// Simulate Ollama response
+		resp := api.ChatResponse{
+			Model: "test-model",
+			Message: api.Message{
+				Role:    "assistant",
+				Content: "Hello there!",
+			},
+			Done:       true,
+			DoneReason: "stop",
+			Metrics: api.Metrics{
+				PromptEvalCount: 10,
+				EvalCount:       5,
+			},
+		}
+		data, _ := json.Marshal(resp)
+		c.Writer.WriteHeader(http.StatusOK)
+		_, _ = c.Writer.Write(data)
+	})
+
+	body := `{"model": "test-model", "max_tokens": 100, "messages": [{"role": "user", "content": "Hi"}]}`
+	req, _ := http.NewRequest(http.MethodPost, "/v1/messages", strings.NewReader(body))
+	req.Header.Set("Content-Type", "application/json")
+
+	resp := httptest.NewRecorder()
+	router.ServeHTTP(resp, req)
+
+	if resp.Code != http.StatusOK {
+		t.Fatalf("expected status 200, got %d", resp.Code)
+	}
+
+	var result anthropic.MessagesResponse
+	if err := json.Unmarshal(resp.Body.Bytes(), &result); err != nil {
+		t.Fatalf("failed to unmarshal response: %v", err)
+	}
+
+	if result.Type != "message" {
+		t.Errorf("expected type 'message', got %q", result.Type)
+	}
+	if result.Role != "assistant" {
+		t.Errorf("expected role 'assistant', got %q", result.Role)
+	}
+	if len(result.Content) != 1 {
+		t.Fatalf("expected 1 content block, got %d", len(result.Content))
+	}
+	if result.Content[0].Text == nil || *result.Content[0].Text != "Hello there!" {
+		t.Errorf("expected text 'Hello there!', got %v", result.Content[0].Text)
+	}
+	if result.StopReason != "end_turn" {
+		t.Errorf("expected stop_reason 'end_turn', got %q", result.StopReason)
+	}
+	if result.Usage.InputTokens != 10 {
+		t.Errorf("expected input_tokens 10, got %d", result.Usage.InputTokens)
+	}
+	if result.Usage.OutputTokens != 5 {
+		t.Errorf("expected output_tokens 5, got %d", result.Usage.OutputTokens)
+	}
+}
+
+// TestAnthropicWriter_ErrorFromRoutes tests error handling when routes.go sends
+// gin.H{"error": "message"} without a StatusCode field (which is the common case)
+func TestAnthropicWriter_ErrorFromRoutes(t *testing.T) {
+	gin.SetMode(gin.TestMode)
+
+	tests := []struct {
+		name          string
+		statusCode    int
+		errorPayload  any
+		wantErrorType string
+		wantMessage   string
+	}{
+		// routes.go sends errors without StatusCode in JSON, so we must use HTTP status
+		{
+			name:          "404 with gin.H error (model not found)",
+			statusCode:    http.StatusNotFound,
+			errorPayload:  gin.H{"error": "model 'nonexistent' not found"},
+			wantErrorType: "not_found_error",
+			wantMessage:   "model 'nonexistent' not found",
+		},
+		{
+			name:          "400 with gin.H error (bad request)",
+			statusCode:    http.StatusBadRequest,
+			errorPayload:  gin.H{"error": "model is required"},
+			wantErrorType: "invalid_request_error",
+			wantMessage:   "model is required",
+		},
+		{
+			name:          "500 with gin.H error (internal error)",
+			statusCode:    http.StatusInternalServerError,
+			errorPayload:  gin.H{"error": "something went wrong"},
+			wantErrorType: "api_error",
+			wantMessage:   "something went wrong",
+		},
+		{
+			name:       "404 with api.StatusError",
+			statusCode: http.StatusNotFound,
+			errorPayload: api.StatusError{
+				StatusCode:   http.StatusNotFound,
+				ErrorMessage: "model not found via StatusError",
+			},
+			wantErrorType: "not_found_error",
+			wantMessage:   "model not found via StatusError",
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			router := gin.New()
+			router.Use(AnthropicMessagesMiddleware())
+			router.POST("/v1/messages", func(c *gin.Context) {
+				// Simulate what routes.go does - set status and write error JSON
+				data, _ := json.Marshal(tt.errorPayload)
+				c.Writer.WriteHeader(tt.statusCode)
+				_, _ = c.Writer.Write(data)
+			})
+
+			body := `{"model": "test-model", "max_tokens": 100, "messages": [{"role": "user", "content": "Hi"}]}`
+			req, _ := http.NewRequest(http.MethodPost, "/v1/messages", strings.NewReader(body))
+			req.Header.Set("Content-Type", "application/json")
+
+			resp := httptest.NewRecorder()
+			router.ServeHTTP(resp, req)
+
+			if resp.Code != tt.statusCode {
+				t.Errorf("expected status %d, got %d", tt.statusCode, resp.Code)
+			}
+
+			var errResp anthropic.ErrorResponse
+			if err := json.Unmarshal(resp.Body.Bytes(), &errResp); err != nil {
+				t.Fatalf("failed to unmarshal error response: %v\nbody: %s", err, resp.Body.String())
+			}
+
+			if errResp.Type != "error" {
+				t.Errorf("expected type 'error', got %q", errResp.Type)
+			}
+			if errResp.Error.Type != tt.wantErrorType {
+				t.Errorf("expected error type %q, got %q", tt.wantErrorType, errResp.Error.Type)
+			}
+			if errResp.Error.Message != tt.wantMessage {
+				t.Errorf("expected message %q, got %q", tt.wantMessage, errResp.Error.Message)
+			}
+		})
+	}
+}
+
+func TestAnthropicMessagesMiddleware_SetsRelaxThinkingFlag(t *testing.T) {
+	gin.SetMode(gin.TestMode)
+
+	var flagSet bool
+	router := gin.New()
+	router.Use(AnthropicMessagesMiddleware())
+	router.POST("/v1/messages", func(c *gin.Context) {
+		_, flagSet = c.Get("relax_thinking")
+		c.Status(http.StatusOK)
+	})
+
+	body := `{"model": "test-model", "max_tokens": 100, "messages": [{"role": "user", "content": "Hi"}]}`
+	req, _ := http.NewRequest(http.MethodPost, "/v1/messages", strings.NewReader(body))
+	req.Header.Set("Content-Type", "application/json")
+
+	resp := httptest.NewRecorder()
+	router.ServeHTTP(resp, req)
+
+	if !flagSet {
+		t.Error("expected relax_thinking flag to be set in context")
+	}
+}

middleware/openai.go

@@ -8,6 +8,7 @@ import (
 	"math/rand"
 	"net/http"
 	"strings"
+	"time"
 
 	"github.com/gin-gonic/gin"
 
@@ -441,6 +442,7 @@ type ResponsesWriter struct {
 	stream     bool
 	responseID string
 	itemID     string
+	request    openai.ResponsesRequest
 }
 
 func (w *ResponsesWriter) writeEvent(eventType string, data any) error {
@@ -478,7 +480,9 @@ func (w *ResponsesWriter) writeResponse(data []byte) (int, error) {
 
 	// Non-streaming response
 	w.ResponseWriter.Header().Set("Content-Type", "application/json")
-	response := openai.ToResponse(w.model, w.responseID, w.itemID, chatResponse)
+	response := openai.ToResponse(w.model, w.responseID, w.itemID, chatResponse, w.request)
+	completedAt := time.Now().Unix()
+	response.CompletedAt = &completedAt
 	return len(data), json.NewEncoder(w.ResponseWriter).Encode(response)
 }
 
@@ -523,11 +527,12 @@ func ResponsesMiddleware() gin.HandlerFunc {
 
 		w := &ResponsesWriter{
 			BaseWriter: BaseWriter{ResponseWriter: c.Writer},
-			converter:  openai.NewResponsesStreamConverter(responseID, itemID, req.Model),
+			converter:  openai.NewResponsesStreamConverter(responseID, itemID, req.Model, req),
 			model:      req.Model,
 			stream:     streamRequested,
 			responseID: responseID,
 			itemID:     itemID,
+			request:    req,
 		}
 
 		// Set headers based on streaming mode
@@ -541,3 +546,66 @@ func ResponsesMiddleware() gin.HandlerFunc {
 		c.Next()
 	}
 }
+
+type ImageWriter struct {
+	BaseWriter
+}
+
+func (w *ImageWriter) writeResponse(data []byte) (int, error) {
+	var generateResponse api.GenerateResponse
+	if err := json.Unmarshal(data, &generateResponse); err != nil {
+		return 0, err
+	}
+
+	// Only write response when done with image
+	if generateResponse.Done && generateResponse.Image != "" {
+		w.ResponseWriter.Header().Set("Content-Type", "application/json")
+		return len(data), json.NewEncoder(w.ResponseWriter).Encode(openai.ToImageGenerationResponse(generateResponse))
+	}
+
+	return len(data), nil
+}
+
+func (w *ImageWriter) Write(data []byte) (int, error) {
+	code := w.ResponseWriter.Status()
+	if code != http.StatusOK {
+		return w.writeError(data)
+	}
+
+	return w.writeResponse(data)
+}
+
+func ImageGenerationsMiddleware() gin.HandlerFunc {
+	return func(c *gin.Context) {
+		var req openai.ImageGenerationRequest
+		if err := c.ShouldBindJSON(&req); err != nil {
+			c.AbortWithStatusJSON(http.StatusBadRequest, openai.NewError(http.StatusBadRequest, err.Error()))
+			return
+		}
+
+		if req.Prompt == "" {
+			c.AbortWithStatusJSON(http.StatusBadRequest, openai.NewError(http.StatusBadRequest, "prompt is required"))
+			return
+		}
+
+		if req.Model == "" {
+			c.AbortWithStatusJSON(http.StatusBadRequest, openai.NewError(http.StatusBadRequest, "model is required"))
+			return
+		}
+
+		var b bytes.Buffer
+		if err := json.NewEncoder(&b).Encode(openai.FromImageGenerationRequest(req)); err != nil {
+			c.AbortWithStatusJSON(http.StatusInternalServerError, openai.NewError(http.StatusInternalServerError, err.Error()))
+			return
+		}
+
+		c.Request.Body = io.NopCloser(&b)
+
+		w := &ImageWriter{
+			BaseWriter: BaseWriter{ResponseWriter: c.Writer},
+		}
+
+		c.Writer = w
+		c.Next()
+	}
+}

middleware/openai_test.go

@@ -961,3 +961,154 @@ func TestRetrieveMiddleware(t *testing.T) {
 		}
 	}
 }
+
+func TestImageGenerationsMiddleware(t *testing.T) {
+	type testCase struct {
+		name string
+		body string
+		req  api.GenerateRequest
+		err  openai.ErrorResponse
+	}
+
+	var capturedRequest *api.GenerateRequest
+
+	testCases := []testCase{
+		{
+			name: "image generation basic",
+			body: `{
+				"model": "test-model",
+				"prompt": "a beautiful sunset"
+			}`,
+			req: api.GenerateRequest{
+				Model:  "test-model",
+				Prompt: "a beautiful sunset",
+			},
+		},
+		{
+			name: "image generation with size",
+			body: `{
+				"model": "test-model",
+				"prompt": "a beautiful sunset",
+				"size": "512x768"
+			}`,
+			req: api.GenerateRequest{
+				Model:  "test-model",
+				Prompt: "a beautiful sunset",
+				Width:  512,
+				Height: 768,
+			},
+		},
+		{
+			name: "image generation missing prompt",
+			body: `{
+				"model": "test-model"
+			}`,
+			err: openai.ErrorResponse{
+				Error: openai.Error{
+					Message: "prompt is required",
+					Type:    "invalid_request_error",
+				},
+			},
+		},
+		{
+			name: "image generation missing model",
+			body: `{
+				"prompt": "a beautiful sunset"
+			}`,
+			err: openai.ErrorResponse{
+				Error: openai.Error{
+					Message: "model is required",
+					Type:    "invalid_request_error",
+				},
+			},
+		},
+	}
+
+	endpoint := func(c *gin.Context) {
+		c.Status(http.StatusOK)
+	}
+
+	gin.SetMode(gin.TestMode)
+	router := gin.New()
+	router.Use(ImageGenerationsMiddleware(), captureRequestMiddleware(&capturedRequest))
+	router.Handle(http.MethodPost, "/api/generate", endpoint)
+
+	for _, tc := range testCases {
+		t.Run(tc.name, func(t *testing.T) {
+			req, _ := http.NewRequest(http.MethodPost, "/api/generate", strings.NewReader(tc.body))
+			req.Header.Set("Content-Type", "application/json")
+
+			defer func() { capturedRequest = nil }()
+
+			resp := httptest.NewRecorder()
+			router.ServeHTTP(resp, req)
+
+			if tc.err.Error.Message != "" {
+				var errResp openai.ErrorResponse
+				if err := json.Unmarshal(resp.Body.Bytes(), &errResp); err != nil {
+					t.Fatal(err)
+				}
+				if diff := cmp.Diff(tc.err, errResp); diff != "" {
+					t.Fatalf("errors did not match:\n%s", diff)
+				}
+				return
+			}
+
+			if resp.Code != http.StatusOK {
+				t.Fatalf("expected status 200, got %d: %s", resp.Code, resp.Body.String())
+			}
+
+			if diff := cmp.Diff(&tc.req, capturedRequest); diff != "" {
+				t.Fatalf("requests did not match:\n%s", diff)
+			}
+		})
+	}
+}
+
+func TestImageWriterResponse(t *testing.T) {
+	gin.SetMode(gin.TestMode)
+
+	// Test that ImageWriter transforms GenerateResponse to OpenAI format
+	endpoint := func(c *gin.Context) {
+		resp := api.GenerateResponse{
+			Model:     "test-model",
+			CreatedAt: time.Unix(1234567890, 0).UTC(),
+			Done:      true,
+			Image:     "dGVzdC1pbWFnZS1kYXRh", // base64 of "test-image-data"
+		}
+		data, _ := json.Marshal(resp)
+		c.Writer.Write(append(data, '\n'))
+	}
+
+	router := gin.New()
+	router.Use(ImageGenerationsMiddleware())
+	router.Handle(http.MethodPost, "/api/generate", endpoint)
+
+	body := `{"model": "test-model", "prompt": "test"}`
+	req, _ := http.NewRequest(http.MethodPost, "/api/generate", strings.NewReader(body))
+	req.Header.Set("Content-Type", "application/json")
+
+	resp := httptest.NewRecorder()
+	router.ServeHTTP(resp, req)
+
+	if resp.Code != http.StatusOK {
+		t.Fatalf("expected status 200, got %d: %s", resp.Code, resp.Body.String())
+	}
+
+	var imageResp openai.ImageGenerationResponse
+	if err := json.Unmarshal(resp.Body.Bytes(), &imageResp); err != nil {
+		t.Fatalf("failed to unmarshal response: %v", err)
+	}
+
+	if imageResp.Created != 1234567890 {
+		t.Errorf("expected created 1234567890, got %d", imageResp.Created)
+	}
+
+	if len(imageResp.Data) != 1 {
+		t.Fatalf("expected 1 image, got %d", len(imageResp.Data))
+	}
+
+	if imageResp.Data[0].B64JSON != "dGVzdC1pbWFnZS1kYXRh" {
+		t.Errorf("expected image data 'dGVzdC1pbWFnZS1kYXRh', got %s", imageResp.Data[0].B64JSON)
+	}
+}

ml/backend.go

@@ -162,6 +162,7 @@ type Tensor interface {
 	AvgPool2D(ctx Context, k, s int, p float32) Tensor
 	Conv2D(ctx Context, weight Tensor, s0, s1, p0, p1, d0, d1 int) Tensor
 	Conv3D(ctx Context, weight Tensor, c, s0, s1, s2, p0, p1, p2, d0, d1, d2 int) Tensor
+	SSMConv(ctx Context, kernel Tensor) Tensor
 
 	IM2Col(ctx Context, weight Tensor, s0, s1, p0, p1, d0, d1 int) Tensor
 

ml/backend/ggml/ggml.go

@@ -1641,6 +1641,13 @@ func (t *Tensor) Conv3D(ctx ml.Context, t2 ml.Tensor, c, s0, s1, s2, p0, p1, p2,
 	return tt
 }
 
+func (t *Tensor) SSMConv(ctx ml.Context, kernel ml.Tensor) ml.Tensor {
+	return &Tensor{
+		b: t.b,
+		t: C.ggml_ssm_conv(ctx.(*Context).ctx, t.t, kernel.(*Tensor).t),
+	}
+}
+
 func (t *Tensor) AvgPool2D(ctx ml.Context, k, s int, p float32) ml.Tensor {
 	return &Tensor{
 		b: t.b,

model/models/glm4moelite/model.go

@@ -0,0 +1,304 @@
+package glm4moelite
+
+import (
+	"math"
+
+	"github.com/ollama/ollama/fs"
+	"github.com/ollama/ollama/kvcache"
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn"
+	"github.com/ollama/ollama/model"
+	"github.com/ollama/ollama/model/input"
+)
+
+type Options struct {
+	numExpertsUsed      int
+	numExperts          int
+	normTopKProb        bool
+	routedScalingFactor float32
+
+	kvLoraRank,
+	qkNopeHeadDim,
+	qkRopeHeadDim,
+	kqNopeHeadDim,
+	qkHeadDim int
+	qLoraRank int
+	vHeadDim  int
+
+	hiddenSize,
+	numHeads,
+	numKVHeads int
+
+	eps,
+	ropeBase float32
+	kqScale float64
+}
+
+func (o Options) applyRotaryPositionEmbeddings(ctx ml.Context, t, p ml.Tensor) ml.Tensor {
+	return nn.RoPE(ctx, t, p, o.qkRopeHeadDim, o.ropeBase, 1.0)
+}
+
+type Attention struct {
+	Q *nn.Linear `gguf:"attn_q"`
+
+	QA     *nn.Linear  `gguf:"attn_q_a"`
+	QANorm *nn.RMSNorm `gguf:"attn_q_a_norm"`
+	QB     *nn.Linear  `gguf:"attn_q_b"`
+
+	KVA     *nn.Linear  `gguf:"attn_kv_a_mqa"`
+	KVANorm *nn.RMSNorm `gguf:"attn_kv_a_norm"`
+	KVB     *nn.Linear  `gguf:"attn_kv_b"`
+
+	Output *nn.Linear `gguf:"attn_out,alt:attn_output"`
+}
+
+func (attn *Attention) Forward(ctx ml.Context, hiddenStates, positions ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor {
+	seqLength := hiddenStates.Dim(1)
+
+	var query ml.Tensor
+	if opts.qLoraRank == 0 {
+		query = attn.Q.Forward(ctx, hiddenStates)
+	} else {
+		query = attn.QA.Forward(ctx, hiddenStates)
+		query = attn.QANorm.Forward(ctx, query, opts.eps)
+		query = attn.QB.Forward(ctx, query)
+	}
+
+	query = query.Reshape(ctx, query.Dim(0)/opts.numHeads, opts.numHeads, seqLength)
+	queryChunks := query.ChunkSections(ctx, 0, opts.qkNopeHeadDim, opts.qkRopeHeadDim)
+
+	compressedKV := attn.KVA.Forward(ctx, hiddenStates)
+	kPass := compressedKV.Slice(ctx, 0, 0, opts.kvLoraRank, 1)
+	kRot := compressedKV.View(ctx,
+		opts.kvLoraRank*compressedKV.Stride(0), opts.qkRopeHeadDim,
+		compressedKV.Stride(1), 1,
+		compressedKV.Stride(1), compressedKV.Dim(1),
+	)
+
+	qRot := opts.applyRotaryPositionEmbeddings(ctx, queryChunks[1], positions)
+	kRot = opts.applyRotaryPositionEmbeddings(ctx, kRot, positions)
+	kPass = attn.KVANorm.Forward(ctx, kPass, opts.eps)
+	kPass = attn.KVB.Forward(ctx, kPass)
+
+	kv := kPass.Reshape(ctx, kPass.Dim(0)/opts.numKVHeads, opts.numKVHeads, seqLength)
+	kvChunks := kv.ChunkSections(ctx, 0, opts.kqNopeHeadDim, opts.vHeadDim)
+
+	kRot = kRot.Repeat(ctx, 1, queryChunks[0].Dim(1))
+	query = qRot.Concat(ctx, queryChunks[0], 0)
+	key := kRot.Concat(ctx, kvChunks[0], 0)
+	attention := nn.Attention(ctx, query, key, kvChunks[1], opts.kqScale, cache)
+
+	attention = attention.Reshape(ctx, attention.Dim(0)*attention.Dim(1), seqLength)
+	return attn.Output.Forward(ctx, attention)
+}
+
+type MLP interface {
+	Forward(ml.Context, ml.Tensor, *Options) ml.Tensor
+}
+
+type sparse struct {
+	Router       *nn.Linear `gguf:"ffn_gate_inp"`
+	Gate         *nn.Linear `gguf:"ffn_gate_exps"`
+	Up           *nn.Linear `gguf:"ffn_up_exps"`
+	Down         *nn.Linear `gguf:"ffn_down_exps"`
+	SharedExpert *dense     `gguf:",suf:_shexp"`
+	ExpProbsBias ml.Tensor  `gguf:"exp_probs_b.bias,alt:exp_probs_b"`
+}
+
+func (moe *sparse) Moe(ctx ml.Context, hiddenStates, topKIndices, topKWeights ml.Tensor, opts *Options) ml.Tensor {
+	hiddenStates = hiddenStates.Reshape(ctx, hiddenStates.Dim(0), 1, hiddenStates.Dim(1))
+
+	upStates := moe.Up.Weight.MulmatID(ctx, hiddenStates, topKIndices)
+	hiddenStates = moe.Gate.Weight.MulmatID(ctx, hiddenStates, topKIndices)
+	hiddenStates = hiddenStates.SILU(ctx, upStates)
+
+	experts := moe.Down.Weight.MulmatID(ctx, hiddenStates, topKIndices)
+	experts = experts.Mul(ctx, topKWeights)
+
+	nextStates := experts.View(ctx, 0, experts.Dim(0), experts.Stride(2), experts.Dim(2))
+	for i := 1; i < opts.numExpertsUsed; i++ {
+		nextStates = nextStates.Add(ctx, experts.View(ctx, i*experts.Stride(1), experts.Dim(0), experts.Stride(2), experts.Dim(2)))
+	}
+	return nextStates
+}
+
+func (moe *sparse) topKIndices(ctx ml.Context, scores ml.Tensor, opts *Options) ml.Tensor {
+	if moe.ExpProbsBias != nil {
+		scores = scores.Add(ctx, moe.ExpProbsBias)
+	}
+	topKIndices := scores.TopK(ctx, opts.numExpertsUsed)
+	return topKIndices
+}
+
+func (moe *sparse) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *Options) ml.Tensor {
+	residuals := hiddenStates
+
+	routerLogits := moe.Router.Forward(ctx, hiddenStates)
+	scores := routerLogits.Sigmoid(ctx)
+	topKIndices := moe.topKIndices(ctx, scores, opts)
+	topKWeights := scores.Reshape(ctx, 1, opts.numExperts, hiddenStates.Dim(1)).Rows(ctx, topKIndices)
+
+	if opts.normTopKProb {
+		topKWeights = topKWeights.Reshape(ctx, opts.numExpertsUsed, hiddenStates.Dim(1))
+		topKWeights = topKWeights.Div(ctx, topKWeights.SumRows(ctx))
+		topKWeights = topKWeights.Reshape(ctx, 1, opts.numExpertsUsed, hiddenStates.Dim(1))
+	}
+
+	topKWeights = topKWeights.Scale(ctx, float64(opts.routedScalingFactor))
+	hiddenStates = moe.Moe(ctx, hiddenStates, topKIndices, topKWeights, opts)
+	sharedExpertResult := moe.SharedExpert.Forward(ctx, residuals, opts)
+
+	hiddenStates = hiddenStates.Add(ctx, sharedExpertResult)
+	return hiddenStates
+}
+
+type dense struct {
+	Gate *nn.Linear `gguf:"ffn_gate"`
+	Up   *nn.Linear `gguf:"ffn_up"`
+	Down *nn.Linear `gguf:"ffn_down"`
+}
+
+func (mlp *dense) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *Options) ml.Tensor {
+	hiddenStates = mlp.Gate.Forward(ctx, hiddenStates).SILU(ctx, mlp.Up.Forward(ctx, hiddenStates))
+	return mlp.Down.Forward(ctx, hiddenStates)
+}
+
+type Layer struct {
+	AttentionNorm *nn.RMSNorm `gguf:"attn_norm"`
+	Attention     *Attention
+
+	MLPNorm *nn.RMSNorm `gguf:"ffn_norm"`
+	MLP     MLP
+}
+
+func (t *Layer) Forward(ctx ml.Context, hiddenStates, positions, outputs ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor {
+	residual := hiddenStates
+	hiddenStates = t.AttentionNorm.Forward(ctx, hiddenStates, opts.eps)
+	hiddenStates = t.Attention.Forward(ctx, hiddenStates, positions, cache, opts)
+
+	if outputs != nil {
+		hiddenStates = hiddenStates.Rows(ctx, outputs)
+		residual = residual.Rows(ctx, outputs)
+	}
+
+	hiddenStates = hiddenStates.Add(ctx, residual)
+	residual = hiddenStates
+
+	hiddenStates = t.MLPNorm.Forward(ctx, hiddenStates, opts.eps)
+	hiddenStates = t.MLP.Forward(ctx, hiddenStates, opts)
+	hiddenStates = hiddenStates.Add(ctx, residual)
+	return hiddenStates
+}
+
+type Model struct {
+	model.Base
+	model.BytePairEncoding
+
+	TokenEmbedding *nn.Embedding `gguf:"token_embd"`
+	Layers         []Layer       `gguf:"blk"`
+
+	OutputNorm *nn.RMSNorm `gguf:"output_norm"`
+	Output     *nn.Linear  `gguf:"output,alt:token_embd"`
+
+	*Options
+}
+
+func New(c fs.Config) (model.Model, error) {
+	layers := make([]Layer, c.Uint("block_count"))
+
+	firstDenseLayerIndex := int(c.Uint("leading_dense_block_count"))
+	for i := range layers {
+		if i < firstDenseLayerIndex {
+			layers[i].MLP = &dense{}
+		} else {
+			layers[i].MLP = &sparse{}
+		}
+	}
+
+	keyLength := int(c.Uint("attention.key_length"))
+	valueLength := int(c.Uint("attention.value_length"))
+
+	kqScale := 1.0 / math.Sqrt(float64(keyLength))
+
+	var pre []string
+	switch c.String("tokenizer.ggml.pre") {
+	case "glm4":
+		pre = []string{
+			`(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`,
+		}
+	default:
+		return nil, model.ErrUnsupportedTokenizer
+	}
+
+	m := Model{
+		BytePairEncoding: model.NewBytePairEncoding(
+			&model.Vocabulary{
+				Values: c.Strings("tokenizer.ggml.tokens"),
+				Types:  c.Ints("tokenizer.ggml.token_type"),
+				Merges: c.Strings("tokenizer.ggml.merges"),
+				AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),
+				BOS:    []int32{int32(c.Uint("tokenizer.ggml.bos_token_id"))},
+				AddEOS: c.Bool("tokenizer.ggml.add_eos_token", false),
+				EOS: append(
+					[]int32{int32(c.Uint("tokenizer.ggml.eos_token_id"))},
+					c.Ints("tokenizer.ggml.eos_token_ids")...,
+				),
+			},
+			pre...,
+		),
+		Layers: layers,
+		Options: &Options{
+			hiddenSize:     int(c.Uint("embedding_length")),
+			numHeads:       int(c.Uint("attention.head_count")),
+			numKVHeads:     int(c.Uint("attention.head_count_kv")),
+			eps:            c.Float("attention.layer_norm_rms_epsilon"),
+			ropeBase:       c.Float("rope.freq_base"),
+			numExperts:     int(c.Uint("expert_count")),
+			numExpertsUsed: int(c.Uint("expert_used_count")),
+			normTopKProb:   c.Bool("expert_weights_norm", true),
+
+			qLoraRank:     int(c.Uint("attention.q_lora_rank")),
+			kvLoraRank:    int(c.Uint("attention.kv_lora_rank")),
+			qkHeadDim:     keyLength,
+			vHeadDim:      valueLength,
+			qkRopeHeadDim: int(c.Uint("rope.dimension_count")),
+			qkNopeHeadDim: keyLength - int(c.Uint("rope.dimension_count")),
+			kqNopeHeadDim: keyLength - int(c.Uint("rope.dimension_count")),
+
+			routedScalingFactor: c.Float("expert_weights_scale"),
+
+			kqScale: kqScale,
+		},
+	}
+
+	m.Cache = kvcache.NewCausalCache(m.Shift)
+	return &m, nil
+}
+
+func (m Model) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
+	return m.applyRotaryPositionEmbeddings(ctx, key, shift), nil
+}
+
+func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
+	positions := ctx.Input().FromInts(batch.Positions, len(batch.Positions))
+
+	hiddenStates := m.TokenEmbedding.Forward(ctx, batch.Inputs)
+
+	for i, layer := range m.Layers {
+		m.Cache.SetLayer(i)
+
+		var outputs ml.Tensor
+		if i == len(m.Layers)-1 {
+			outputs = batch.Outputs
+		}
+
+		hiddenStates = layer.Forward(ctx, hiddenStates, positions, outputs, m.Cache, m.Options)
+	}
+
+	hiddenStates = m.OutputNorm.Forward(ctx, hiddenStates, m.eps)
+	return m.Output.Forward(ctx, hiddenStates), nil
+}
+
+func init() {
+	model.Register("glm4moelite", New)
+}

model/models/lfm2/cache.go

@@ -0,0 +1,410 @@
+package lfm2
+
+import (
+	"slices"
+
+	"github.com/ollama/ollama/kvcache"
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/model/input"
+)
+
+var _ kvcache.Cache = (*HybridCache)(nil)
+
+// HybridCache stores:
+// - a standard causal KV cache for attention layers
+// - a per-sequence recurrent conv state for shortconv layers
+//
+// Conv state shape (per layer, per sequence): [dConv, hiddenSize] where dConv = L_cache - 1.
+// Stored internally as a tensor of shape [dConv * hiddenSize, maxSlots].
+type HybridCache struct {
+	kv *kvcache.Causal
+
+	backend      ml.Backend
+	dtype        ml.DType
+	maxSequences int
+
+	hiddenSize int
+	dConv      int
+
+	// slot mapping for recurrent state
+	slotForSeq map[int]int
+	refCount   []int
+	freeSlots  []int
+
+	// per-layer conv state buffers (allocated lazily)
+	convCtxs   map[int]ml.Context
+	convStates map[int]ml.Tensor // [dConv*hiddenSize, maxSlots]
+
+	// current forward batch (derived in StartForward)
+	curSeqs       []int
+	curSlots      []int
+	curSlotsInput ml.Tensor
+	curSeqTokens  int
+
+	// track if EnsureWritable has been called for this forward pass
+	writableEnsured bool
+	// track any error from EnsureWritable to propagate later
+	writableError error
+}
+
+func NewHybridCache(shift func(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error), hiddenSize, dConv int) *HybridCache {
+	return &HybridCache{
+		kv:         kvcache.NewCausalCache(shift),
+		hiddenSize: hiddenSize,
+		dConv:      dConv,
+		slotForSeq: make(map[int]int),
+		convCtxs:   make(map[int]ml.Context),
+		convStates: make(map[int]ml.Tensor),
+	}
+}
+
+func (c *HybridCache) Init(backend ml.Backend, dtype ml.DType, maxSequences, capacity, maxBatch int) {
+	c.backend = backend
+	c.dtype = dtype
+	c.maxSequences = maxSequences
+
+	// initialize slot allocator
+	c.refCount = make([]int, maxSequences)
+	c.freeSlots = c.freeSlots[:0]
+	for i := maxSequences - 1; i >= 0; i-- {
+		c.freeSlots = append(c.freeSlots, i)
+	}
+
+	c.kv.Init(backend, dtype, maxSequences, capacity, maxBatch)
+}
+
+func (c *HybridCache) Close() {
+	for _, ctx := range c.convCtxs {
+		ctx.Close()
+	}
+	c.kv.Close()
+}
+
+func (c *HybridCache) SetConfig(config ml.CacheConfig) {
+	c.kv.SetConfig(config)
+}
+
+func (c *HybridCache) SetLayer(layer int) {
+	c.kv.SetLayer(layer)
+}
+
+func (c *HybridCache) Get(ctx ml.Context) (ml.Tensor, ml.Tensor, ml.Tensor) {
+	return c.kv.Get(ctx)
+}
+
+func (c *HybridCache) Put(ctx ml.Context, key, value ml.Tensor) {
+	c.kv.Put(ctx, key, value)
+}
+
+func (c *HybridCache) StartForward(ctx ml.Context, batch input.Batch, reserve bool) error {
+	if err := c.kv.StartForward(ctx, batch, reserve); err != nil {
+		return err
+	}
+
+	// Derive equal-length sequence layout for shortconv.
+	// LFM2 shortconv assumes tokens form a [seq_tokens, seqs] grid.
+	seqCounts := make(map[int]int)
+	c.curSeqs = c.curSeqs[:0]
+	for _, s := range batch.Sequences {
+		if _, ok := seqCounts[s]; !ok {
+			c.curSeqs = append(c.curSeqs, s)
+		}
+		seqCounts[s]++
+	}
+
+	if len(c.curSeqs) == 0 {
+		return nil
+	}
+
+	nTokens := len(batch.Sequences)
+	nSeqs := len(c.curSeqs)
+	want := nTokens / nSeqs
+	for _, s := range c.curSeqs {
+		if seqCounts[s] != want {
+			return kvcache.ErrNotSupported
+		}
+	}
+
+	c.curSeqTokens = want
+
+	// When reserving memory for estimation, use fake slot assignments
+	// without modifying permanent state (slotForSeq, refCount)
+	if reserve {
+		c.curSlots = c.curSlots[:0]
+		slots := make([]int32, nSeqs)
+		for i := range nSeqs {
+			c.curSlots = append(c.curSlots, i)
+			slots[i] = int32(i)
+		}
+		c.curSlotsInput = ctx.Input().FromInts(slots, len(slots))
+		return nil
+	}
+
+	// Ensure slots exist for sequences in this batch
+	c.curSlots = c.curSlots[:0]
+	var newSlots []int // track newly allocated slots that need zeroing
+	for _, s := range c.curSeqs {
+		slot, ok := c.slotForSeq[s]
+		if !ok {
+			var err error
+			slot, err = c.allocSlot()
+			if err != nil {
+				return err
+			}
+			c.slotForSeq[s] = slot
+			c.refCount[slot] = 1
+			newSlots = append(newSlots, slot)
+		}
+		c.curSlots = append(c.curSlots, slot)
+	}
+
+	// Zero conv state for newly allocated slots to clear stale data from previous sequences
+	if len(newSlots) > 0 {
+		c.zeroConvSlots(ctx, newSlots)
+	}
+
+	// Create a tensor for the current slots
+	slots := make([]int32, len(c.curSlots))
+	for i, v := range c.curSlots {
+		slots[i] = int32(v)
+	}
+	c.curSlotsInput = ctx.Input().FromInts(slots, len(slots))
+
+	// Reset writable state for new forward pass
+	c.writableEnsured = false
+	c.writableError = nil
+
+	return nil
+}
+
+func (c *HybridCache) allocSlot() (int, error) {
+	if len(c.freeSlots) == 0 {
+		return 0, kvcache.ErrKvCacheFull
+	}
+	slot := c.freeSlots[len(c.freeSlots)-1]
+	c.freeSlots = c.freeSlots[:len(c.freeSlots)-1]
+	return slot, nil
+}
+
+func (c *HybridCache) freeSlot(slot int) {
+	// Bounds check before freeing
+	if slot >= 0 && slot < c.maxSequences {
+		c.freeSlots = append(c.freeSlots, slot)
+	}
+}
+
+// zeroConvSlots zeros the conv state for the given slots across all layers.
+// This must be called when recycling slots to prevent stale state from affecting new sequences.
+func (c *HybridCache) zeroConvSlots(ctx ml.Context, slots []int) {
+	if len(slots) == 0 || len(c.convStates) == 0 {
+		return
+	}
+
+	// Use input context for creating tensors
+	inputCtx := ctx.Input()
+
+	// Create slot indices tensor
+	slotIndices := make([]int32, len(slots))
+	for i, s := range slots {
+		slotIndices[i] = int32(s)
+	}
+	slotsTensor := inputCtx.FromInts(slotIndices, len(slotIndices))
+
+	// Create zero tensor for the slots (SetRows requires F32 source)
+	zeros := inputCtx.Zeros(ml.DTypeF32, c.dConv*c.hiddenSize, len(slots))
+
+	// Zero each layer's conv state for these slots
+	for _, buf := range c.convStates {
+		ctx.Forward(buf.SetRows(ctx, zeros, slotsTensor))
+	}
+}
+
+// EnsureWritable ensures that sequences in the current batch have private (non-shared) conv slots.
+// Returns an error if slot allocation fails.
+func (c *HybridCache) EnsureWritable(ctx ml.Context) error {
+	for i, seq := range c.curSeqs {
+		slot, ok := c.slotForSeq[seq]
+		if !ok {
+			continue
+		}
+
+		// Bounds check
+		if slot < 0 || slot >= len(c.refCount) {
+			continue
+		}
+
+		if c.refCount[slot] <= 1 {
+			continue
+		}
+
+		newSlot, err := c.allocSlot()
+		if err != nil {
+			return err
+		}
+		c.refCount[slot]--
+		c.refCount[newSlot] = 1
+		c.slotForSeq[seq] = newSlot
+		c.curSlots[i] = newSlot
+
+		// Copy existing conv state for all initialized layers
+		for _, buf := range c.convStates {
+			// buf: [dConv*hiddenSize, maxSlots]
+			src := buf.Rows(ctx, ctx.Input().FromInts([]int32{int32(slot)}, 1))
+			// SetRows requires F32 source
+			srcF32 := src.Cast(ctx, ml.DTypeF32)
+			ctx.Forward(buf.SetRows(ctx, srcF32, ctx.Input().FromInts([]int32{int32(newSlot)}, 1)))
+		}
+	}
+
+	// Rebuild current slots tensor
+	slots := make([]int32, len(c.curSlots))
+	for i, v := range c.curSlots {
+		slots[i] = int32(v)
+	}
+	c.curSlotsInput = ctx.Input().FromInts(slots, len(slots))
+
+	return nil
+}
+
+func (c *HybridCache) CopyPrefix(srcSeq, dstSeq int, prefixLen int32) {
+	// KV cache shares prefix metadata (no copy) which is correct for prefix reuse.
+	c.kv.CopyPrefix(srcSeq, dstSeq, prefixLen)
+
+	// For shortconv state we implement copy-on-write: dst shares the same slot as src.
+	// On the first write to dst, EnsureWritable will create a private slot.
+	if dstSlot, ok := c.slotForSeq[dstSeq]; ok {
+		// Bounds check before decrementing
+		if dstSlot >= 0 && dstSlot < len(c.refCount) {
+			c.refCount[dstSlot]--
+			if c.refCount[dstSlot] <= 0 {
+				c.refCount[dstSlot] = 0
+				c.freeSlot(dstSlot)
+			}
+		}
+		delete(c.slotForSeq, dstSeq)
+	}
+
+	srcSlot, ok := c.slotForSeq[srcSeq]
+	if !ok {
+		// src may not have a slot yet; dst will allocate on demand
+		return
+	}
+
+	// Bounds check before incrementing
+	if srcSlot >= 0 && srcSlot < len(c.refCount) {
+		c.slotForSeq[dstSeq] = srcSlot
+		c.refCount[srcSlot]++
+	}
+}
+
+func (c *HybridCache) CanResume(seq int, pos int32) bool {
+	return c.kv.CanResume(seq, pos)
+}
+
+func (c *HybridCache) Remove(seq int, beginIndex, endIndex int32) error {
+	if err := c.kv.Remove(seq, beginIndex, endIndex); err != nil {
+		return err
+	}
+
+	// For recurrent state, any removal invalidates the state because
+	// the state at position N depends on all previous positions.
+	// Drop the slot mapping so it resets on next use.
+	slot, ok := c.slotForSeq[seq]
+	if !ok {
+		return nil
+	}
+
+	// Bounds check
+	if slot < 0 || slot >= len(c.refCount) {
+		delete(c.slotForSeq, seq)
+		return nil
+	}
+
+	c.refCount[slot]--
+	if c.refCount[slot] <= 0 {
+		c.refCount[slot] = 0
+		c.freeSlot(slot)
+	}
+	delete(c.slotForSeq, seq)
+
+	return nil
+}
+
+func (c *HybridCache) slotsTensor() ml.Tensor {
+	return c.curSlotsInput
+}
+
+func (c *HybridCache) seqTokens() int {
+	return c.curSeqTokens
+}
+
+func (c *HybridCache) numSeqs() int {
+	return len(c.curSeqs)
+}
+
+func (c *HybridCache) convBuffer(ctx ml.Context, layer int) ml.Tensor {
+	if buf, ok := c.convStates[layer]; ok {
+		return buf
+	}
+
+	if _, ok := c.convCtxs[layer]; !ok {
+		c.convCtxs[layer] = c.backend.NewContextSize(1).Layer(layer)
+	}
+
+	buf := c.convCtxs[layer].Zeros(c.dtype, c.dConv*c.hiddenSize, c.maxSequences)
+	c.convStates[layer] = buf
+	return buf
+}
+
+// ConvState returns the conv state for current batch sequences as shape [dConv, hiddenSize, nSeqs].
+// Returns an error if copy-on-write allocation fails.
+func (c *HybridCache) ConvState(ctx ml.Context, layer int) (ml.Tensor, error) {
+	if !c.writableEnsured {
+		needsWritable := false
+		for _, seq := range c.curSeqs {
+			slot, ok := c.slotForSeq[seq]
+			if !ok {
+				continue
+			}
+			if slot >= 0 && slot < len(c.refCount) && c.refCount[slot] > 1 {
+				needsWritable = true
+				break
+			}
+		}
+
+		if needsWritable {
+			if err := c.EnsureWritable(ctx); err != nil {
+				c.writableError = err
+			}
+		}
+		c.writableEnsured = true
+	}
+
+	if c.writableError != nil {
+		return nil, c.writableError
+	}
+
+	buf := c.convBuffer(ctx, layer)
+	cur := buf.Rows(ctx, c.slotsTensor())
+	return cur.Reshape(ctx, c.dConv, c.hiddenSize, c.numSeqs()), nil
+}
+
+// UpdateConvState writes a new conv state for current batch sequences.
+// newState must have shape [dConv, hiddenSize, nSeqs].
+func (c *HybridCache) UpdateConvState(ctx ml.Context, layer int, newState ml.Tensor) {
+	buf := c.convBuffer(ctx, layer)
+	src := newState.Reshape(ctx, c.dConv*c.hiddenSize, c.numSeqs())
+	// SetRows requires F32 source
+	srcF32 := src.Cast(ctx, ml.DTypeF32)
+	ctx.Forward(buf.SetRows(ctx, srcF32, c.slotsTensor()))
+}
+
+// IsSupportedForBatch returns true if the current batch layout supports shortconv.
+func (c *HybridCache) IsSupportedForBatch() bool {
+	return c.curSeqTokens > 0 && len(c.curSeqs) > 0
+}
+
+// Seqs returns the ordered unique sequences for the current forward pass.
+func (c *HybridCache) Seqs() []int {
+	return slices.Clone(c.curSeqs)
+}

model/models/lfm2/cache_test.go

@@ -0,0 +1,444 @@
+package lfm2
+
+import (
+	"testing"
+
+	"github.com/ollama/ollama/kvcache"
+	"github.com/ollama/ollama/ml"
+)
+
+// TestHybridCache tests verify the slot management logic of HybridCache.
+// These tests focus on the recurrent state slot allocation, reference counting,
+// and copy-on-write semantics without requiring a full ML backend.
+
+// createSlotOnlyCache creates a HybridCache with only the slot management
+// fields initialized. Used to test slot logic in isolation.
+func createSlotOnlyCache(maxSequences int) *HybridCache {
+	return &HybridCache{
+		hiddenSize:   256,
+		dConv:        3,
+		maxSequences: maxSequences,
+		refCount:     make([]int, maxSequences),
+		freeSlots:    initFreeSlots(maxSequences),
+		slotForSeq:   make(map[int]int),
+		convCtxs:     make(map[int]ml.Context),
+		convStates:   make(map[int]ml.Tensor),
+	}
+}
+
+func initFreeSlots(n int) []int {
+	slots := make([]int, 0, n)
+	for i := n - 1; i >= 0; i-- {
+		slots = append(slots, i)
+	}
+	return slots
+}
+
+func TestHybridCache_SlotAllocation(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Verify initial state
+	if len(cache.freeSlots) != 4 {
+		t.Errorf("expected 4 free slots, got %d", len(cache.freeSlots))
+	}
+
+	// Allocate all slots
+	for range 4 {
+		slot, err := cache.allocSlot()
+		if err != nil {
+			t.Fatalf("allocSlot failed: %v", err)
+		}
+		cache.refCount[slot] = 1
+	}
+
+	// Should be full now
+	if len(cache.freeSlots) != 0 {
+		t.Errorf("expected 0 free slots, got %d", len(cache.freeSlots))
+	}
+
+	// Trying to allocate another should fail
+	_, err := cache.allocSlot()
+	if err != kvcache.ErrKvCacheFull {
+		t.Errorf("expected ErrKvCacheFull, got %v", err)
+	}
+}
+
+func TestHybridCache_SlotReuse(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Allocate a slot
+	slot1, _ := cache.allocSlot()
+	cache.refCount[slot1] = 1
+
+	// Free it
+	cache.refCount[slot1] = 0
+	cache.freeSlot(slot1)
+
+	// Allocate again - should get the same slot back (LIFO)
+	slot2, _ := cache.allocSlot()
+	if slot2 != slot1 {
+		t.Errorf("expected slot %d to be reused, got %d", slot1, slot2)
+	}
+}
+
+func TestHybridCache_SlotRefCounting_ShareSlot(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Allocate slot for seq 1
+	slot1, _ := cache.allocSlot()
+	cache.slotForSeq[1] = slot1
+	cache.refCount[slot1] = 1
+
+	// Simulate sharing slot with seq 2 (copy-on-write style)
+	cache.slotForSeq[2] = slot1
+	cache.refCount[slot1]++
+
+	// Should share the same slot
+	if cache.slotForSeq[2] != slot1 {
+		t.Errorf("expected seq 2 to share slot %d, got %d", slot1, cache.slotForSeq[2])
+	}
+
+	// Ref count should be 2
+	if cache.refCount[slot1] != 2 {
+		t.Errorf("expected refCount 2, got %d", cache.refCount[slot1])
+	}
+}
+
+func TestHybridCache_SlotRefCounting_DecRef(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Allocate slot for seq 1
+	slot1, _ := cache.allocSlot()
+	cache.slotForSeq[1] = slot1
+	cache.refCount[slot1] = 1
+
+	// Share with seq 2
+	cache.slotForSeq[2] = slot1
+	cache.refCount[slot1]++
+
+	// Unshare seq 2
+	cache.refCount[slot1]--
+	delete(cache.slotForSeq, 2)
+
+	// Ref count should be back to 1
+	if cache.refCount[slot1] != 1 {
+		t.Errorf("expected refCount 1 after unshare, got %d", cache.refCount[slot1])
+	}
+
+	// Seq 2 should no longer have a slot
+	if _, ok := cache.slotForSeq[2]; ok {
+		t.Error("seq 2 should not have a slot after unshare")
+	}
+}
+
+func TestHybridCache_SlotFreeWhenUnused(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	initialFreeSlots := len(cache.freeSlots)
+
+	// Allocate slot for seq 1
+	slot1, _ := cache.allocSlot()
+	cache.slotForSeq[1] = slot1
+	cache.refCount[slot1] = 1
+
+	// Free the slot when refCount drops to 0
+	cache.refCount[slot1]--
+	if cache.refCount[slot1] <= 0 {
+		cache.refCount[slot1] = 0
+		cache.freeSlot(slot1)
+	}
+	delete(cache.slotForSeq, 1)
+
+	// Slot should be freed
+	if len(cache.freeSlots) != initialFreeSlots {
+		t.Errorf("expected %d free slots, got %d", initialFreeSlots, len(cache.freeSlots))
+	}
+
+	// Ref count should be 0
+	if cache.refCount[slot1] != 0 {
+		t.Errorf("expected refCount 0, got %d", cache.refCount[slot1])
+	}
+}
+
+func TestHybridCache_SlotOverwrite(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Allocate slots for seq 1 and seq 2
+	slot1, _ := cache.allocSlot()
+	cache.slotForSeq[1] = slot1
+	cache.refCount[slot1] = 1
+
+	slot2, _ := cache.allocSlot()
+	cache.slotForSeq[2] = slot2
+	cache.refCount[slot2] = 1
+
+	initialFreeSlots := len(cache.freeSlots)
+
+	// Simulate overwriting seq 2's slot with slot1 (sharing)
+	// First free the old slot
+	cache.refCount[slot2]--
+	if cache.refCount[slot2] <= 0 {
+		cache.refCount[slot2] = 0
+		cache.freeSlot(slot2)
+	}
+	// Then share slot1
+	cache.slotForSeq[2] = slot1
+	cache.refCount[slot1]++
+
+	// Seq 2 should now share slot1
+	if cache.slotForSeq[2] != slot1 {
+		t.Errorf("expected seq 2 to share slot %d, got %d", slot1, cache.slotForSeq[2])
+	}
+
+	// Old slot2 should be freed
+	if len(cache.freeSlots) != initialFreeSlots+1 {
+		t.Errorf("expected %d free slots, got %d", initialFreeSlots+1, len(cache.freeSlots))
+	}
+}
+
+func TestHybridCache_BoundsChecking(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Test freeing invalid slot (should not panic)
+	cache.freeSlot(-1)
+	cache.freeSlot(100) // out of bounds
+
+	// freeSlot does bounds checking, so invalid slots should be ignored
+	if len(cache.freeSlots) != 4 {
+		t.Errorf("invalid slots should not affect free list, got %d slots", len(cache.freeSlots))
+	}
+}
+
+func TestHybridCache_MultipleSequences_RefCounting(t *testing.T) {
+	cache := createSlotOnlyCache(8)
+
+	// Allocate slot for seq 1
+	slot1, _ := cache.allocSlot()
+	cache.slotForSeq[1] = slot1
+	cache.refCount[slot1] = 1
+
+	// Fork to seq 2, 3, 4 (all share slot1)
+	for _, seq := range []int{2, 3, 4} {
+		cache.slotForSeq[seq] = slot1
+		cache.refCount[slot1]++
+	}
+
+	// Ref count should be 4
+	if cache.refCount[slot1] != 4 {
+		t.Errorf("expected refCount 4, got %d", cache.refCount[slot1])
+	}
+
+	// Remove seq 2, 3
+	for _, seq := range []int{2, 3} {
+		delete(cache.slotForSeq, seq)
+		cache.refCount[slot1]--
+	}
+
+	if cache.refCount[slot1] != 2 {
+		t.Errorf("expected refCount 2, got %d", cache.refCount[slot1])
+	}
+
+	// Slot should still be allocated (not in free list)
+	found := false
+	for _, s := range cache.freeSlots {
+		if s == slot1 {
+			found = true
+			break
+		}
+	}
+	if found {
+		t.Error("slot1 should not be in free list yet")
+	}
+
+	// Remove remaining sequences
+	for _, seq := range []int{1, 4} {
+		delete(cache.slotForSeq, seq)
+		cache.refCount[slot1]--
+	}
+
+	if cache.refCount[slot1] != 0 {
+		t.Errorf("expected refCount 0, got %d", cache.refCount[slot1])
+	}
+}
+
+func TestHybridCache_ChainedSharing(t *testing.T) {
+	cache := createSlotOnlyCache(8)
+
+	// Create seq 1
+	slot1, _ := cache.allocSlot()
+	cache.slotForSeq[1] = slot1
+	cache.refCount[slot1] = 1
+
+	// Share 1 -> 2
+	cache.slotForSeq[2] = slot1
+	cache.refCount[slot1]++
+
+	// Share 2 -> 3 (should still share slot1)
+	cache.slotForSeq[3] = cache.slotForSeq[2] // which is slot1
+	cache.refCount[slot1]++
+
+	// All should share slot1
+	if cache.slotForSeq[1] != slot1 || cache.slotForSeq[2] != slot1 || cache.slotForSeq[3] != slot1 {
+		t.Error("all sequences should share slot1")
+	}
+
+	if cache.refCount[slot1] != 3 {
+		t.Errorf("expected refCount 3, got %d", cache.refCount[slot1])
+	}
+}
+
+func TestHybridCache_CacheParameters(t *testing.T) {
+	cache := NewHybridCache(nil, 512, 5) // hiddenSize=512, dConv=5
+
+	if cache.hiddenSize != 512 {
+		t.Errorf("expected hiddenSize 512, got %d", cache.hiddenSize)
+	}
+	if cache.dConv != 5 {
+		t.Errorf("expected dConv 5, got %d", cache.dConv)
+	}
+}
+
+func TestHybridCache_NumSeqs(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Initially no sequences
+	if cache.numSeqs() != 0 {
+		t.Errorf("expected 0 seqs, got %d", cache.numSeqs())
+	}
+
+	// Manually set up current batch state
+	cache.curSeqs = []int{1, 2, 3}
+
+	if cache.numSeqs() != 3 {
+		t.Errorf("expected 3 seqs, got %d", cache.numSeqs())
+	}
+}
+
+func TestHybridCache_SeqTokens(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Initially 0
+	if cache.seqTokens() != 0 {
+		t.Errorf("expected 0 seqTokens, got %d", cache.seqTokens())
+	}
+
+	// Manually set up current batch state
+	cache.curSeqTokens = 16
+
+	if cache.seqTokens() != 16 {
+		t.Errorf("expected 16 seqTokens, got %d", cache.seqTokens())
+	}
+}
+
+// Test that Seqs returns a clone of curSeqs
+func TestHybridCache_Seqs_ReturnsClone(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	cache.curSeqs = []int{1, 2, 3}
+
+	seqs := cache.Seqs()
+
+	// Modify returned slice
+	seqs[0] = 999
+
+	// Original should be unchanged
+	if cache.curSeqs[0] != 1 {
+		t.Error("Seqs should return a clone, not the original slice")
+	}
+}
+
+func TestHybridCache_IsSupportedForBatch(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Initially not supported (no batch set up)
+	if cache.IsSupportedForBatch() {
+		t.Error("expected IsSupportedForBatch to be false initially")
+	}
+
+	// Set up a valid batch
+	cache.curSeqTokens = 1
+	cache.curSeqs = []int{1}
+
+	if !cache.IsSupportedForBatch() {
+		t.Error("expected IsSupportedForBatch to be true with valid batch")
+	}
+}
+
+func TestHybridCache_ZeroConvSlots_EmptyInputs(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// zeroConvSlots should handle empty slots without panicking
+	cache.zeroConvSlots(nil, nil)
+	cache.zeroConvSlots(nil, []int{})
+
+	// zeroConvSlots should handle empty convStates without panicking
+	cache.zeroConvSlots(nil, []int{0, 1, 2})
+}
+
+func TestHybridCache_SlotRecycling_TracksNewSlots(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Allocate slot for seq 1
+	slot1, _ := cache.allocSlot()
+	cache.slotForSeq[1] = slot1
+	cache.refCount[slot1] = 1
+
+	// Free the slot (simulating sequence removal)
+	cache.refCount[slot1]--
+	cache.freeSlot(slot1)
+	delete(cache.slotForSeq, 1)
+
+	// Verify slot is in free list
+	if len(cache.freeSlots) != 4 {
+		t.Errorf("expected 4 free slots after freeing, got %d", len(cache.freeSlots))
+	}
+
+	// Allocate for new seq 2 - should get recycled slot
+	slot2, _ := cache.allocSlot()
+	if slot2 != slot1 {
+		t.Errorf("expected recycled slot %d, got %d", slot1, slot2)
+	}
+
+	// This recycled slot would need zeroing in the real implementation
+	// The actual zeroing is tested via integration tests since it requires ML context
+}
+
+func TestHybridCache_NewSequence_GetsTrackedForZeroing(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Simulate the slot allocation flow from StartForward
+	// When a sequence doesn't have a slot, it gets allocated and tracked as "new"
+
+	newSlots := []int{}
+
+	// Seq 1 doesn't have a slot - allocate and track
+	seq := 1
+	if _, ok := cache.slotForSeq[seq]; !ok {
+		slot, err := cache.allocSlot()
+		if err != nil {
+			t.Fatalf("allocSlot failed: %v", err)
+		}
+		cache.slotForSeq[seq] = slot
+		cache.refCount[slot] = 1
+		newSlots = append(newSlots, slot)
+	}
+
+	// Verify newSlots contains the allocated slot
+	if len(newSlots) != 1 {
+		t.Errorf("expected 1 new slot, got %d", len(newSlots))
+	}
+
+	// Seq 1 already has a slot - should NOT be tracked as new
+	newSlots2 := []int{}
+	if _, ok := cache.slotForSeq[seq]; !ok {
+		slot, _ := cache.allocSlot()
+		cache.slotForSeq[seq] = slot
+		cache.refCount[slot] = 1
+		newSlots2 = append(newSlots2, slot)
+	}
+
+	// Verify no new slots for existing sequence
+	if len(newSlots2) != 0 {
+		t.Errorf("expected 0 new slots for existing sequence, got %d", len(newSlots2))
+	}
+}

model/models/lfm2/model.go

@@ -0,0 +1,253 @@
+package lfm2
+
+import (
+	"cmp"
+	"math"
+
+	"github.com/ollama/ollama/fs"
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn"
+	"github.com/ollama/ollama/ml/nn/rope"
+	"github.com/ollama/ollama/model"
+	"github.com/ollama/ollama/model/input"
+)
+
+type Options struct {
+	hiddenSize       int
+	headDim, ropeDim int
+
+	eps, ropeBase, ropeScale float32
+
+	ropeType              string
+	originalContextLength int
+
+	// per-layer head counts (LFM2 alternates attention and recurrent layers)
+	numHeadsByLayer   []int
+	numKVHeadsByLayer []int
+}
+
+func (o Options) headDimValue() int {
+	// Head dim is shared across layers; fall back to first attention layer head count.
+	for _, h := range o.numHeadsByLayer {
+		if h > 0 {
+			return cmp.Or(o.headDim, o.hiddenSize/h)
+		}
+	}
+	return cmp.Or(o.headDim, o.hiddenSize)
+}
+
+func (o Options) applyRotaryPositionEmbeddings(ctx ml.Context, states, positions ml.Tensor) ml.Tensor {
+	opts := []func(*rope.Options){rope.WithTypeNeoX()}
+	if o.ropeType == "yarn" {
+		attnFactor := float32(1.0 / (1.0 + 0.1*math.Log(float64(o.ropeScale))))
+		opts = append(opts,
+			rope.WithOriginalContextLength(o.originalContextLength),
+			rope.WithExtrapolationFactor(1.),
+			rope.WithAttentionFactor(attnFactor),
+		)
+	}
+
+	headCount := 1
+	for _, h := range o.numHeadsByLayer {
+		if h > 0 {
+			headCount = h
+			break
+		}
+	}
+	return nn.RoPE(ctx, states, positions, cmp.Or(o.ropeDim, o.headDim, o.hiddenSize/headCount), o.ropeBase, 1./o.ropeScale, opts...)
+}
+
+type Model struct {
+	model.Base
+	model.TextProcessor
+
+	TokenEmbedding *nn.Embedding `gguf:"token_embd"`
+	Layers         []Layer       `gguf:"blk"`
+	OutputNorm     *nn.RMSNorm   `gguf:"output_norm,alt:token_embd_norm"`
+	Output         *nn.Linear    `gguf:"output,alt:token_embd"`
+
+	Options
+}
+
+func New(c fs.Config) (model.Model, error) {
+	if c.Uint("expert_count") > 0 {
+		return nil, model.ErrUnsupportedModel
+	}
+
+	if c.String("tokenizer.ggml.model") != "gpt2" {
+		return nil, model.ErrUnsupportedTokenizer
+	}
+
+	vocabulary := model.Vocabulary{
+		Values: c.Strings("tokenizer.ggml.tokens"),
+		Scores: c.Floats("tokenizer.ggml.scores"),
+		Types:  c.Ints("tokenizer.ggml.token_type"),
+		Merges: c.Strings("tokenizer.ggml.merges"),
+		AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),
+		BOS:    []int32{int32(c.Uint("tokenizer.ggml.bos_token_id"))},
+		AddEOS: c.Bool("tokenizer.ggml.add_eos_token", false),
+		EOS: append(
+			[]int32{int32(c.Uint("tokenizer.ggml.eos_token_id"))},
+			c.Ints("tokenizer.ggml.eos_token_ids")...,
+		),
+	}
+
+	var pretokenizers []string
+	switch c.String("tokenizer.ggml.pre") {
+	case "default":
+		// use default BPE pretokenizer
+	default:
+		// llama-bpe style (default for LFM2)
+		pretokenizers = []string{
+			`(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`,
+		}
+	}
+
+	m := Model{
+		TextProcessor: model.NewBytePairEncoding(&vocabulary, pretokenizers...),
+		Layers:        make([]Layer, c.Uint("block_count")),
+		Options: Options{
+			hiddenSize:            int(c.Uint("embedding_length")),
+			headDim:               int(c.Uint("attention.key_length")),
+			ropeDim:               int(c.Uint("rope.dimension_count")),
+			eps:                   c.Float("attention.layer_norm_rms_epsilon"),
+			ropeType:              c.String("rope.scaling.type"),
+			ropeBase:              c.Float("rope.freq_base"),
+			ropeScale:             c.Float("rope.scaling.factor", 1),
+			originalContextLength: int(c.Uint("rope.scaling.original_context_length")),
+		},
+	}
+
+	type headCounts interface {
+		HeadCount() []uint64
+		HeadCountKV() []uint64
+	}
+	hc, ok := c.(headCounts)
+	if !ok {
+		return nil, model.ErrUnsupportedModel
+	}
+
+	headCount := hc.HeadCount()
+	headCountKV := hc.HeadCountKV()
+
+	m.numHeadsByLayer = make([]int, len(m.Layers))
+	m.numKVHeadsByLayer = make([]int, len(m.Layers))
+	for i := range m.Layers {
+		m.numHeadsByLayer[i] = int(headCount[i])
+		m.numKVHeadsByLayer[i] = int(headCountKV[i])
+
+		if m.numKVHeadsByLayer[i] == 0 {
+			m.Layers[i].Operator = &ShortConv{}
+		} else {
+			m.Layers[i].Operator = &Attention{}
+		}
+	}
+
+	lCache := int(c.Uint("shortconv.l_cache"))
+	dConv := max(0, lCache-1)
+	m.Cache = NewHybridCache(m.Shift, m.hiddenSize, dConv)
+	return &m, nil
+}
+
+type Operator interface {
+	Forward(ctx ml.Context, hiddenStates, positions ml.Tensor, cache *HybridCache, layer int, opts *Options) ml.Tensor
+}
+
+type Attention struct {
+	Query     *nn.Linear  `gguf:"attn_q"`
+	QueryNorm *nn.RMSNorm `gguf:"attn_q_norm"`
+	Key       *nn.Linear  `gguf:"attn_k"`
+	KeyNorm   *nn.RMSNorm `gguf:"attn_k_norm"`
+	Value     *nn.Linear  `gguf:"attn_v"`
+	Output    *nn.Linear  `gguf:"attn_output,alt:attn_out"`
+}
+
+func (sa *Attention) Forward(ctx ml.Context, hiddenStates, positions ml.Tensor, cache *HybridCache, layer int, opts *Options) ml.Tensor {
+	batchSize := hiddenStates.Dim(1)
+	headDim := opts.headDimValue()
+	numHeads := opts.numHeadsByLayer[layer]
+	numKVHeads := opts.numKVHeadsByLayer[layer]
+
+	query := sa.Query.Forward(ctx, hiddenStates)
+	key := sa.Key.Forward(ctx, hiddenStates)
+	value := sa.Value.Forward(ctx, hiddenStates)
+
+	query = query.Reshape(ctx, headDim, numHeads, batchSize)
+	key = key.Reshape(ctx, headDim, numKVHeads, batchSize)
+	value = value.Reshape(ctx, headDim, numKVHeads, batchSize)
+
+	query = sa.QueryNorm.Forward(ctx, query, opts.eps)
+	key = sa.KeyNorm.Forward(ctx, key, opts.eps)
+
+	query = opts.applyRotaryPositionEmbeddings(ctx, query, positions)
+	key = opts.applyRotaryPositionEmbeddings(ctx, key, positions)
+
+	attention := nn.Attention(ctx, query, key, value, 1./math.Sqrt(float64(headDim)), cache)
+	attention = attention.Reshape(ctx, attention.Dim(0)*attention.Dim(1), batchSize)
+	return sa.Output.Forward(ctx, attention)
+}
+
+type MLP struct {
+	Up   *nn.Linear `gguf:"ffn_up"`
+	Down *nn.Linear `gguf:"ffn_down"`
+	Gate *nn.Linear `gguf:"ffn_gate"`
+}
+
+func (mlp *MLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *Options) ml.Tensor {
+	hiddenState = mlp.Gate.Forward(ctx, hiddenState).SILU(ctx, mlp.Up.Forward(ctx, hiddenState))
+	return mlp.Down.Forward(ctx, hiddenState)
+}
+
+type Layer struct {
+	AttentionNorm *nn.RMSNorm `gguf:"attn_norm"`
+	Operator      Operator
+	MLPNorm       *nn.RMSNorm `gguf:"ffn_norm"`
+	MLP           *MLP
+}
+
+func (l *Layer) Forward(ctx ml.Context, layer int, hiddenState, positions, outputs ml.Tensor, cache *HybridCache, opts *Options) ml.Tensor {
+	residual := hiddenState
+
+	hiddenState = l.AttentionNorm.Forward(ctx, hiddenState, opts.eps)
+	hiddenState = l.Operator.Forward(ctx, hiddenState, positions, cache, layer, opts)
+
+	if outputs != nil {
+		hiddenState = hiddenState.Rows(ctx, outputs)
+		residual = residual.Rows(ctx, outputs)
+	}
+
+	hiddenState = hiddenState.Add(ctx, residual)
+	residual = hiddenState
+
+	hiddenState = l.MLPNorm.Forward(ctx, hiddenState, opts.eps)
+	hiddenState = l.MLP.Forward(ctx, hiddenState, opts)
+	return hiddenState.Add(ctx, residual)
+}
+
+func (m *Model) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
+	return m.applyRotaryPositionEmbeddings(ctx, key, shift), nil
+}
+
+func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
+	positions := ctx.Input().FromInts(batch.Positions, len(batch.Positions))
+
+	hiddenState := m.TokenEmbedding.Forward(ctx, batch.Inputs)
+
+	for i, layer := range m.Layers {
+		m.Cache.SetLayer(i)
+
+		var outputs ml.Tensor
+		if i == len(m.Layers)-1 {
+			outputs = batch.Outputs
+		}
+
+		hiddenState = layer.Forward(ctx, i, hiddenState, positions, outputs, m.Cache.(*HybridCache), &m.Options)
+	}
+
+	hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps)
+	return m.Output.Forward(ctx, hiddenState), nil
+}
+
+func init() {
+	model.Register("lfm2", New)
+}

model/models/lfm2/shortconv.go

@@ -0,0 +1,52 @@
+package lfm2
+
+import (
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn"
+)
+
+type shortConvKernel struct {
+	Weight ml.Tensor `gguf:"weight"`
+}
+
+// ShortConv implements the LFM2 short-convolution block (GGML_OP_SSM_CONV) with a recurrent
+// state stored in the HybridCache.
+type ShortConv struct {
+	Conv    *shortConvKernel `gguf:"shortconv.conv"`
+	InProj  *nn.Linear       `gguf:"shortconv.in_proj"`
+	OutProj *nn.Linear       `gguf:"shortconv.out_proj"`
+}
+
+func (sc *ShortConv) Forward(ctx ml.Context, hiddenStates ml.Tensor, _ ml.Tensor, cache *HybridCache, layer int, opts *Options) ml.Tensor {
+	nSeqs := cache.numSeqs()
+	seqTokens := cache.seqTokens()
+	hiddenSize := hiddenStates.Dim(0)
+	if nSeqs <= 0 || seqTokens <= 0 || hiddenStates.Dim(1) != nSeqs*seqTokens {
+		panic("lfm2: unsupported batch layout for shortconv")
+	}
+
+	bcx := sc.InProj.Forward(ctx, hiddenStates).Reshape(ctx, 3*hiddenSize, seqTokens, nSeqs)
+
+	elementSize := bcx.Stride(0)
+	b := bcx.View(ctx, 0*hiddenSize*elementSize, hiddenSize, bcx.Stride(1), seqTokens, bcx.Stride(2), nSeqs)
+	c := bcx.View(ctx, 1*hiddenSize*elementSize, hiddenSize, bcx.Stride(1), seqTokens, bcx.Stride(2), nSeqs)
+	x := bcx.View(ctx, 2*hiddenSize*elementSize, hiddenSize, bcx.Stride(1), seqTokens, bcx.Stride(2), nSeqs)
+
+	bx := b.Mul(ctx, x).Permute(ctx, 1, 0, 2, 3)
+
+	state, err := cache.ConvState(ctx, layer)
+	if err != nil {
+		panic("lfm2: failed to get conv state: " + err.Error())
+	}
+	sx := state.Concat(ctx, bx, 0)
+
+	// Cast weight to F32 for SSMConv (Metal requires F32)
+	weightF32 := sc.Conv.Weight.Cast(ctx, ml.DTypeF32)
+	convOut := sx.SSMConv(ctx, weightF32)
+	y := c.Mul(ctx, convOut)
+
+	dConv := sx.Dim(0) - seqTokens
+	cache.UpdateConvState(ctx, layer, sx.Slice(ctx, 0, sx.Dim(0)-dConv, sx.Dim(0), 1))
+
+	return sc.OutProj.Forward(ctx, y.Reshape(ctx, hiddenSize, seqTokens*nSeqs))
+}

model/models/models.go

@@ -7,7 +7,9 @@ import (
 	_ "github.com/ollama/ollama/model/models/gemma2"
 	_ "github.com/ollama/ollama/model/models/gemma3"
 	_ "github.com/ollama/ollama/model/models/gemma3n"
+	_ "github.com/ollama/ollama/model/models/glm4moelite"
 	_ "github.com/ollama/ollama/model/models/gptoss"
+	_ "github.com/ollama/ollama/model/models/lfm2"
 	_ "github.com/ollama/ollama/model/models/llama"
 	_ "github.com/ollama/ollama/model/models/llama4"
 	_ "github.com/ollama/ollama/model/models/mistral3"

model/parsers/glm46.go

@@ -0,0 +1,410 @@
+package parsers
+
+import (
+	"context"
+	"encoding/xml"
+	"fmt"
+	"log/slog"
+	"strings"
+	"unicode"
+
+	"github.com/ollama/ollama/api"
+	"github.com/ollama/ollama/logutil"
+)
+
+type glm46ParserState int
+
+const (
+	glm46ParserState_LookingForThinkingOpen glm46ParserState = iota
+	glm46ParserState_ThinkingStartedEatingWhitespace
+	glm46ParserState_CollectingThinking
+	glm46ParserState_ThinkingDoneEatingWhitespace
+	glm46ParserState_CollectingContent
+	glm46ParserState_ToolStartedEatingWhitespace
+	glm46ParserState_CollectingToolContent
+)
+
+const (
+	glm46ThinkingOpenTag  = "<think>"
+	glm46ThinkingCloseTag = "</think>"
+	glm46ToolOpenTag      = "<tool_call>"
+	glm46ToolCloseTag     = "</tool_call>"
+)
+
+type GLM46Parser struct {
+	state  glm46ParserState
+	buffer strings.Builder
+	tools  []api.Tool
+}
+
+func (p *GLM46Parser) HasToolSupport() bool {
+	return true
+}
+
+func (p *GLM46Parser) HasThinkingSupport() bool {
+	return true
+}
+
+// func (p *GLM46Parser) Init(tools []api.Tool, lastMessage *api.Message) []api.Tool {
+func (p *GLM46Parser) Init(tools []api.Tool, lastMessage *api.Message, thinkValue *api.ThinkValue) []api.Tool {
+	p.tools = tools
+	return tools
+}
+
+type glm46Event interface {
+	isGLM46Event()
+}
+
+type glm46EventContent struct {
+	content string
+}
+
+func (glm46EventContent) isGLM46Event() {}
+
+type glm46EventRawToolCall struct {
+	raw string
+}
+
+func (glm46EventRawToolCall) isGLM46Event() {}
+
+type glm46EventThinkingContent struct {
+	content string
+}
+
+func (glm46EventThinkingContent) isGLM46Event() {}
+
+func (p *GLM46Parser) Add(s string, done bool) (content string, thinking string, calls []api.ToolCall, err error) {
+	p.buffer.WriteString(s)
+	events := p.parseEvents()
+
+	var toolCalls []api.ToolCall
+	var contentSb strings.Builder
+	var thinkingSb strings.Builder
+
+	for _, event := range events {
+		switch event := event.(type) {
+		case glm46EventRawToolCall:
+			toolCall, err := parseGLM46ToolCall(event, p.tools)
+			if err != nil {
+				slog.Warn("glm-4.6 tool call parsing failed", "error", err)
+				return "", "", nil, err
+			}
+			toolCalls = append(toolCalls, toolCall)
+		case glm46EventThinkingContent:
+			thinkingSb.WriteString(event.content)
+		case glm46EventContent:
+			// TODO(drifkin): if the same turn contains multiple interleaved content
+			// events, we naively append them together here.
+			contentSb.WriteString(event.content)
+		}
+	}
+
+	return contentSb.String(), thinkingSb.String(), toolCalls, nil
+}
+
+func (p *GLM46Parser) parseEvents() []glm46Event {
+	var all []glm46Event
+
+	keepLooping := true
+	for keepLooping {
+		var events []glm46Event
+		events, keepLooping = p.eat()
+		if len(events) > 0 {
+			all = append(all, events...)
+		}
+	}
+
+	if len(all) > 0 {
+		slog.Log(context.TODO(), logutil.LevelTrace, "glm-4.6 events parsed", "events", all, "state", p.state, "buffer", p.buffer.String())
+	}
+
+	return all
+}
+
+// eatLeadingWhitespaceAndTransitionTo consumes leading whitespace from the buffer
+// and transitions to the next state. Returns (nil, false) if only whitespace remains
+// in the buffer (needs more input), or (nil, true) if we successfully transitioned.
+func (p *GLM46Parser) eatLeadingWhitespaceAndTransitionTo(nextState glm46ParserState) ([]glm46Event, bool) {
+	trimmed := strings.TrimLeftFunc(p.buffer.String(), unicode.IsSpace)
+	p.buffer.Reset()
+	if trimmed == "" {
+		return nil, false // Still only whitespace, keep waiting for more input
+	}
+	p.state = nextState
+	p.buffer.WriteString(trimmed)
+	return nil, true // Successfully transitioned
+}
+
+// glm46SplitAtTag splits the buffer at the given tag, returns the content before (trimmed of trailing whitespace),
+// the content after (optionally trimmed of leading whitespace), and updates the buffer
+func glm46SplitAtTag(p *GLM46Parser, tag string, trimAfter bool) (string, string) {
+	split := strings.SplitN(p.buffer.String(), tag, 2)
+	before := split[0]
+	before = strings.TrimRightFunc(before, unicode.IsSpace)
+	after := split[1]
+	if trimAfter {
+		after = strings.TrimLeftFunc(after, unicode.IsSpace)
+	}
+	p.buffer.Reset()
+	p.buffer.WriteString(after)
+	return before, after
+}
+
+func (p *GLM46Parser) eat() ([]glm46Event, bool) {
+	var events []glm46Event
+
+	switch p.state {
+	case glm46ParserState_LookingForThinkingOpen:
+		trimmed := strings.TrimLeftFunc(p.buffer.String(), unicode.IsSpace)
+		if strings.HasPrefix(trimmed, glm46ThinkingOpenTag) {
+			// Found <think> opening tag
+			after := strings.TrimPrefix(trimmed, glm46ThinkingOpenTag)
+			after = strings.TrimLeftFunc(after, unicode.IsSpace)
+			p.buffer.Reset()
+			p.buffer.WriteString(after)
+			if after == "" {
+				p.state = glm46ParserState_ThinkingStartedEatingWhitespace
+			} else {
+				p.state = glm46ParserState_CollectingThinking
+			}
+			return events, true
+		} else if strings.HasPrefix(glm46ThinkingOpenTag, trimmed) {
+			// Partial opening tag seen, keep accumulating
+			return events, false
+		} else if trimmed == "" {
+			// Only whitespace, keep accumulating
+			return events, false
+		} else {
+			// No thinking tag found, skip to content collection
+			p.state = glm46ParserState_CollectingContent
+			// Don't trim - we want to keep the original content
+			return events, true
+		}
+
+	case glm46ParserState_ThinkingStartedEatingWhitespace:
+		return p.eatLeadingWhitespaceAndTransitionTo(glm46ParserState_CollectingThinking)
+
+	case glm46ParserState_CollectingThinking:
+		acc := p.buffer.String()
+		if strings.Contains(acc, glm46ThinkingCloseTag) {
+			thinking, remaining := glm46SplitAtTag(p, glm46ThinkingCloseTag, true)
+			if len(thinking) > 0 {
+				events = append(events, glm46EventThinkingContent{content: thinking})
+			}
+			if remaining == "" {
+				p.state = glm46ParserState_ThinkingDoneEatingWhitespace
+			} else {
+				p.state = glm46ParserState_CollectingContent
+			}
+			return events, true
+		} else if overlapLen := overlap(acc, glm46ThinkingCloseTag); overlapLen > 0 {
+			// Partial closing tag - withhold it along with any trailing whitespace before it
+			beforePartialTag := acc[:len(acc)-overlapLen]
+			trailingWhitespaceLen := trailingWhitespaceLen(beforePartialTag)
+			ambiguousStart := len(beforePartialTag) - trailingWhitespaceLen
+
+			unambiguous := acc[:ambiguousStart]
+			ambiguous := acc[ambiguousStart:]
+			p.buffer.Reset()
+			p.buffer.WriteString(ambiguous)
+			if len(unambiguous) > 0 {
+				events = append(events, glm46EventThinkingContent{content: unambiguous})
+			}
+			return events, false
+		} else {
+			// Pure thinking content - withhold trailing whitespace (might precede closing tag)
+			whitespaceLen := trailingWhitespaceLen(acc)
+			ambiguousStart := len(acc) - whitespaceLen
+
+			unambiguous := acc[:ambiguousStart]
+			ambiguous := acc[ambiguousStart:]
+			p.buffer.Reset()
+			p.buffer.WriteString(ambiguous)
+			if len(unambiguous) > 0 {
+				events = append(events, glm46EventThinkingContent{content: unambiguous})
+			}
+			return events, false
+		}
+
+	case glm46ParserState_ThinkingDoneEatingWhitespace:
+		return p.eatLeadingWhitespaceAndTransitionTo(glm46ParserState_CollectingContent)
+
+	case glm46ParserState_CollectingContent:
+		if strings.Contains(p.buffer.String(), glm46ToolOpenTag) {
+			before, after := glm46SplitAtTag(p, glm46ToolOpenTag, true)
+			if len(before) > 0 {
+				events = append(events, glm46EventContent{content: before})
+			}
+			if after == "" {
+				p.state = glm46ParserState_ToolStartedEatingWhitespace
+			} else {
+				p.state = glm46ParserState_CollectingToolContent
+			}
+			return events, true
+		} else if overlapLen := overlap(p.buffer.String(), glm46ToolOpenTag); overlapLen > 0 {
+			beforePartialTag := p.buffer.String()[:len(p.buffer.String())-overlapLen]
+			trailingWhitespaceLen := trailingWhitespaceLen(beforePartialTag)
+			ambiguousStart := len(beforePartialTag) - trailingWhitespaceLen
+
+			unambiguous := p.buffer.String()[:ambiguousStart]
+			ambiguous := p.buffer.String()[ambiguousStart:]
+			p.buffer.Reset()
+			p.buffer.WriteString(ambiguous)
+			if len(unambiguous) > 0 {
+				events = append(events, glm46EventContent{content: unambiguous})
+			}
+			return events, false
+		} else {
+			whitespaceLen := trailingWhitespaceLen(p.buffer.String())
+			ambiguousStart := len(p.buffer.String()) - whitespaceLen
+
+			unambiguous := p.buffer.String()[:ambiguousStart]
+			ambiguous := p.buffer.String()[ambiguousStart:]
+			p.buffer.Reset()
+			p.buffer.WriteString(ambiguous)
+			if len(unambiguous) > 0 {
+				events = append(events, glm46EventContent{content: unambiguous})
+			}
+			return events, false
+		}
+
+	case glm46ParserState_ToolStartedEatingWhitespace:
+		return p.eatLeadingWhitespaceAndTransitionTo(glm46ParserState_CollectingToolContent)
+
+	case glm46ParserState_CollectingToolContent:
+		acc := p.buffer.String()
+		if strings.Contains(acc, glm46ToolCloseTag) {
+			toolContent, _ := glm46SplitAtTag(p, glm46ToolCloseTag, true)
+			if len(toolContent) == 0 {
+				slog.Warn("glm46 tool call closing tag found but no content before it")
+			}
+			events = append(events, glm46EventRawToolCall{raw: toolContent})
+			p.state = glm46ParserState_CollectingContent
+			return events, true
+		} else {
+			// Keep accumulating - tool calls are not streamed
+			// We just wait for the closing tag
+			return events, false
+		}
+
+	default:
+		panic("unreachable")
+	}
+}
+
+// GLMToolCallXML represents the structure of a GLM-4.6 tool call for XML parsing
+type GLMToolCallXML struct {
+	XMLName xml.Name `xml:"tool_call"`
+	Content string   `xml:",chardata"` // Function name (text nodes between tags)
+	Keys    []string `xml:"arg_key"`   // All arg_key elements in document order
+	Values  []string `xml:"arg_value"` // All arg_value elements in document order
+}
+
+// escapeGLM46Content escapes XML entities in text content while preserving arg_key/arg_value tags
+func escapeGLM46Content(s string) string {
+	var result strings.Builder
+	inTag := false
+
+	for i := range len(s) {
+		ch := s[i]
+
+		if ch == '<' {
+			// Check if this is a known tag
+			if strings.HasPrefix(s[i:], "<arg_key>") ||
+				strings.HasPrefix(s[i:], "</arg_key>") ||
+				strings.HasPrefix(s[i:], "<arg_value>") ||
+				strings.HasPrefix(s[i:], "</arg_value>") {
+				inTag = true
+			}
+		}
+
+		if inTag {
+			result.WriteByte(ch)
+			if ch == '>' {
+				inTag = false
+			}
+		} else {
+			// Escape special characters in text content
+			switch ch {
+			case '&':
+				result.WriteString("&amp;")
+			case '<':
+				result.WriteString("&lt;")
+			case '>':
+				result.WriteString("&gt;")
+			default:
+				result.WriteByte(ch)
+			}
+		}
+	}
+
+	return result.String()
+}
+
+func parseGLM46ToolCall(raw glm46EventRawToolCall, tools []api.Tool) (api.ToolCall, error) {
+	// Escape any unescaped entities in text content
+	// We need to escape text between tags, but not the tags themselves
+	escaped := escapeGLM46Content(raw.raw)
+
+	// Wrap the content in a root element to make it valid XML
+	xmlString := "<tool_call>" + escaped + "</tool_call>"
+
+	// Parse XML into struct
+	var parsed GLMToolCallXML
+	if err := xml.Unmarshal([]byte(xmlString), &parsed); err != nil {
+		return api.ToolCall{}, fmt.Errorf("failed to parse XML: %w", err)
+	}
+
+	// Extract and trim function name
+	functionName := strings.TrimSpace(parsed.Content)
+	if functionName == "" {
+		return api.ToolCall{}, fmt.Errorf("empty function name")
+	}
+
+	// Verify keys and values are paired correctly
+	if len(parsed.Keys) != len(parsed.Values) {
+		return api.ToolCall{}, fmt.Errorf("mismatched arg_key and arg_value counts: %d keys, %d values", len(parsed.Keys), len(parsed.Values))
+	}
+
+	// Find the matching tool to get parameter types
+	var matchedTool *api.Tool
+	for i := range tools {
+		if tools[i].Function.Name == functionName {
+			matchedTool = &tools[i]
+			break
+		}
+	}
+
+	// Build arguments map by pairing keys and values
+	toolCall := api.ToolCall{
+		Function: api.ToolCallFunction{
+			Name:      functionName,
+			Arguments: api.NewToolCallFunctionArguments(),
+		},
+	}
+
+	for i := range parsed.Keys {
+		key := strings.TrimSpace(parsed.Keys[i])
+		value := parsed.Values[i] // Don't trim here - parseValue handles it
+
+		// Look up parameter type
+		var paramType api.PropertyType
+		if matchedTool != nil && matchedTool.Function.Parameters.Properties != nil {
+			if prop, ok := matchedTool.Function.Parameters.Properties.Get(key); ok {
+				// Handle anyOf by collecting all types from the union
+				if len(prop.AnyOf) > 0 {
+					for _, anyOfProp := range prop.AnyOf {
+						paramType = append(paramType, anyOfProp.Type...)
+					}
+				} else {
+					paramType = prop.Type
+				}
+			}
+		}
+
+		// Parse value with type coercion
+		toolCall.Function.Arguments.Set(key, parseValue(value, paramType))
+	}
+
+	return toolCall, nil
+}

model/parsers/glm46_test.go

@@ -0,0 +1,862 @@
+package parsers
+
+import (
+	"encoding/xml"
+	"reflect"
+	"testing"
+
+	"github.com/ollama/ollama/api"
+)
+
+func TestGLM46ParserStreaming(t *testing.T) {
+	type step struct {
+		input      string
+		wantEvents []glm46Event
+	}
+
+	cases := []struct {
+		desc  string
+		steps []step
+		only  bool
+	}{
+		{
+			desc: "leading whitespace before think tag",
+			steps: []step{
+				{
+					input:      "   \n\t  ",
+					wantEvents: []glm46Event{},
+				},
+				{
+					input:      "<think>thinking</think>",
+					wantEvents: []glm46Event{glm46EventThinkingContent{content: "thinking"}},
+				},
+			},
+		},
+		{
+			desc: "think tag with whitespace inside",
+			steps: []step{
+				{
+					input: "<think>  \n  thinking content  \n  </think>regular content",
+					wantEvents: []glm46Event{
+						glm46EventThinkingContent{content: "thinking content"},
+						glm46EventContent{content: "regular content"},
+					},
+				},
+			},
+		},
+		{
+			desc: "tool call with leading whitespace after opening tag",
+			steps: []step{
+				{
+					input: "<think></think><tool_call>  \n  test  \n  </tool_call>",
+					wantEvents: []glm46Event{
+						glm46EventRawToolCall{raw: "test"},
+					},
+				},
+			},
+		},
+		{
+			desc: "simple thinking then content",
+			steps: []step{
+				{
+					input: "<think>I am thinking</think>Now I respond",
+					wantEvents: []glm46Event{
+						glm46EventThinkingContent{content: "I am thinking"},
+						glm46EventContent{content: "Now I respond"},
+					},
+				},
+			},
+		},
+		{
+			desc: "streamed thinking content",
+			steps: []step{
+				{
+					input:      "<think>hello",
+					wantEvents: []glm46Event{glm46EventThinkingContent{content: "hello"}},
+				},
+				{
+					input:      " world",
+					wantEvents: []glm46Event{glm46EventThinkingContent{content: " world"}},
+				},
+				{
+					input: "</think>content",
+					wantEvents: []glm46Event{
+						glm46EventContent{content: "content"},
+					},
+				},
+			},
+		},
+		{
+			desc: "content before tool call",
+			steps: []step{
+				{
+					input: "<think>Let me call a tool</think>here is text<tool_call>",
+					wantEvents: []glm46Event{
+						glm46EventThinkingContent{content: "Let me call a tool"},
+						glm46EventContent{content: "here is text"},
+					},
+				},
+				{
+					input: "function_name\n<arg_key>param</arg_key>\n<arg_value>value</arg_value>\n</tool_call>",
+					wantEvents: []glm46Event{
+						glm46EventRawToolCall{raw: "function_name\n<arg_key>param</arg_key>\n<arg_value>value</arg_value>"},
+					},
+				},
+			},
+		},
+		{
+			desc: "tool call with content after",
+			steps: []step{
+				{
+					input: "<think>thinking</think><tool_call>test</tool_call>after tool",
+					wantEvents: []glm46Event{
+						glm46EventThinkingContent{content: "thinking"},
+						glm46EventRawToolCall{raw: "test"},
+						glm46EventContent{content: "after tool"},
+					},
+				},
+			},
+		},
+		{
+			desc: "trailing whitespace between content and tool call is trimmed",
+			steps: []step{
+				{
+					input: "<think>thinking</think>content\n  \t  <tool_call>test</tool_call>",
+					wantEvents: []glm46Event{
+						glm46EventThinkingContent{content: "thinking"},
+						glm46EventContent{content: "content"},
+						glm46EventRawToolCall{raw: "test"},
+					},
+				},
+			},
+		},
+		{
+			desc: "trailing whitespace between tool call and content is trimmed",
+			steps: []step{
+				{
+					input: "<think>think</think><tool_call>test</tool_call>\n\t  after",
+					wantEvents: []glm46Event{
+						glm46EventThinkingContent{content: "think"},
+						glm46EventRawToolCall{raw: "test"},
+						glm46EventContent{content: "after"},
+					},
+				},
+			},
+		},
+		{
+			desc: "split thinking close tag",
+			steps: []step{
+				{
+					input:      "<think>thinking content</th",
+					wantEvents: []glm46Event{glm46EventThinkingContent{content: "thinking content"}},
+				},
+				{
+					input: "ink>after",
+					wantEvents: []glm46Event{
+						glm46EventContent{content: "after"},
+					},
+				},
+			},
+		},
+		{
+			desc: "split thinking open tag",
+			steps: []step{
+				{
+					input:      "  <thi",
+					wantEvents: []glm46Event{},
+				},
+				{
+					input:      "nk>content</think>",
+					wantEvents: []glm46Event{glm46EventThinkingContent{content: "content"}},
+				},
+			},
+		},
+		{
+			desc: "split tool open tag",
+			steps: []step{
+				{
+					input:      "<think>think</think>content<tool",
+					wantEvents: []glm46Event{glm46EventThinkingContent{content: "think"}, glm46EventContent{content: "content"}},
+				},
+				{
+					input:      "_call>inside",
+					wantEvents: []glm46Event{},
+				},
+				{
+					input: "</tool_call>",
+					wantEvents: []glm46Event{
+						glm46EventRawToolCall{raw: "inside"},
+					},
+				},
+			},
+		},
+		{
+			desc: "partial thinking close tag fakeout",
+			steps: []step{
+				{
+					input:      "<think>content</th",
+					wantEvents: []glm46Event{glm46EventThinkingContent{content: "content"}},
+				},
+				{
+					input:      "ought more",
+					wantEvents: []glm46Event{glm46EventThinkingContent{content: "</thought more"}},
+				},
+			},
+		},
+		{
+			desc: "partial thinking open tag fakeout",
+			steps: []step{
+				{
+					input:      "  <thi",
+					wantEvents: []glm46Event{},
+				},
+				{
+					input: "nking is fun",
+					wantEvents: []glm46Event{
+						glm46EventContent{content: "  <thinking is fun"},
+					},
+				},
+			},
+		},
+		{
+			desc: "partial tool open tag fakeout",
+			steps: []step{
+				{
+					input: "<think></think>content\n<tool",
+					wantEvents: []glm46Event{
+						glm46EventContent{content: "content"},
+					},
+				},
+				{
+					input: " fakeout",
+					wantEvents: []glm46Event{
+						glm46EventContent{content: "\n<tool fakeout"},
+					},
+				},
+			},
+		},
+		{
+			desc: "partial tool close tag fakeout",
+			steps: []step{
+				{
+					input:      "<think></think><tool_call>content</tool",
+					wantEvents: []glm46Event{},
+				},
+				{
+					input:      " fakeout",
+					wantEvents: []glm46Event{},
+				},
+				{
+					input: "</tool_call>",
+					wantEvents: []glm46Event{
+						glm46EventRawToolCall{raw: "content</tool fakeout"},
+					},
+				},
+			},
+		},
+		{
+			desc: "empty thinking tag",
+			steps: []step{
+				{
+					input: "<think></think>content here",
+					wantEvents: []glm46Event{
+						glm46EventContent{content: "content here"},
+					},
+				},
+			},
+		},
+		{
+			desc: "multiple tool calls in sequence",
+			steps: []step{
+				{
+					input: "<think>think</think><tool_call>first</tool_call>between<tool_call>second</tool_call>end",
+					wantEvents: []glm46Event{
+						glm46EventThinkingContent{content: "think"},
+						glm46EventRawToolCall{raw: "first"},
+						glm46EventContent{content: "between"},
+						glm46EventRawToolCall{raw: "second"},
+						glm46EventContent{content: "end"},
+					},
+				},
+			},
+		},
+		{
+			desc: "no thinking tag - direct to content",
+			steps: []step{
+				{
+					input: "just content here",
+					wantEvents: []glm46Event{
+						glm46EventContent{content: "just content here"},
+					},
+				},
+			},
+		},
+		{
+			desc: "no thinking tag - skip to content then tool call",
+			steps: []step{
+				{
+					input: "Here's the answer:<tool_call>test</tool_call>done",
+					wantEvents: []glm46Event{
+						glm46EventContent{content: "Here's the answer:"},
+						glm46EventRawToolCall{raw: "test"},
+						glm46EventContent{content: "done"},
+					},
+				},
+			},
+		},
+		{
+			desc: "no thinking tag - whitespace preserved when no tags",
+			steps: []step{
+				{
+					input: "  \n  content with leading whitespace",
+					wantEvents: []glm46Event{
+						glm46EventContent{content: "  \n  content with leading whitespace"},
+					},
+				},
+			},
+		},
+		{
+			desc: "whitespace after think close tag gets eaten",
+			steps: []step{
+				{
+					input: "<think>thinking</think>  \n\t  content",
+					wantEvents: []glm46Event{
+						glm46EventThinkingContent{content: "thinking"},
+						glm46EventContent{content: "content"},
+					},
+				},
+			},
+		},
+		{
+			desc: "whitespace after tool_call close tag gets eaten",
+			steps: []step{
+				{
+					input: "<think></think><tool_call>test</tool_call>  \n\t  content",
+					wantEvents: []glm46Event{
+						glm46EventRawToolCall{raw: "test"},
+						glm46EventContent{content: "content"},
+					},
+				},
+			},
+		},
+		{
+			desc: "thinking content withholds trailing whitespace (single chunk)",
+			steps: []step{
+				{
+					input: "<think>thinking content   ",
+					wantEvents: []glm46Event{
+						glm46EventThinkingContent{content: "thinking content"},
+					},
+				},
+				{
+					input: "</think>after",
+					wantEvents: []glm46Event{
+						glm46EventContent{content: "after"},
+					},
+				},
+			},
+		},
+		{
+			desc: "thinking content withholds trailing whitespace with newlines",
+			steps: []step{
+				{
+					input: "<think>thinking\n\n  ",
+					wantEvents: []glm46Event{
+						glm46EventThinkingContent{content: "thinking"},
+					},
+				},
+				{
+					input: "</think>content",
+					wantEvents: []glm46Event{
+						glm46EventContent{content: "content"},
+					},
+				},
+			},
+		},
+		{
+			desc: "thinking content trailing whitespace emitted when more content arrives",
+			steps: []step{
+				{
+					input: "<think>thinking   ",
+					wantEvents: []glm46Event{
+						glm46EventThinkingContent{content: "thinking"},
+					},
+				},
+				{
+					input: "more thinking",
+					wantEvents: []glm46Event{
+						glm46EventThinkingContent{content: "   more thinking"},
+					},
+				},
+				{
+					input:      "</think>",
+					wantEvents: []glm46Event{},
+				},
+			},
+		},
+		{
+			desc: "thinking content withholds trailing whitespace before partial close tag",
+			steps: []step{
+				{
+					input: "<think>thinking   </th",
+					wantEvents: []glm46Event{
+						glm46EventThinkingContent{content: "thinking"},
+					},
+				},
+				{
+					input: "ink>content",
+					wantEvents: []glm46Event{
+						glm46EventContent{content: "content"},
+					},
+				},
+			},
+		},
+	}
+
+	anyOnlies := false
+	for _, tc := range cases {
+		if tc.only {
+			anyOnlies = true
+		}
+	}
+
+	for _, tc := range cases {
+		if anyOnlies && !tc.only {
+			continue
+		}
+
+		t.Run(tc.desc, func(t *testing.T) {
+			parser := GLM46Parser{}
+
+			for i, step := range tc.steps {
+				parser.buffer.WriteString(step.input)
+				gotEvents := parser.parseEvents()
+
+				if len(gotEvents) == 0 && len(step.wantEvents) == 0 {
+					// avoid deep equal on empty vs. nil slices
+					continue
+				}
+
+				if !reflect.DeepEqual(gotEvents, step.wantEvents) {
+					t.Errorf("step %d: input %q: got events %#v, want %#v", i, step.input, gotEvents, step.wantEvents)
+				}
+			}
+		})
+	}
+}
+
+// TestGLMToolCallXMLOrderPreservation verifies that xml.Unmarshal preserves
+// document order when collecting multiple elements with the same tag name into slices.
+// This is a critical assumption for the GLM-4.6 parser's struct-based approach.
+func TestGLMToolCallXMLOrderPreservation(t *testing.T) {
+	testCases := []struct {
+		name       string
+		xml        string
+		wantKeys   []string
+		wantValues []string
+	}{
+		{
+			name: "alternating keys and values",
+			xml: `<tool_call>
+function_name
+<arg_key>first</arg_key>
+<arg_value>A</arg_value>
+<arg_key>second</arg_key>
+<arg_value>B</arg_value>
+<arg_key>third</arg_key>
+<arg_value>C</arg_value>
+</tool_call>`,
+			wantKeys:   []string{"first", "second", "third"},
+			wantValues: []string{"A", "B", "C"},
+		},
+		{
+			name: "all keys then all values",
+			xml: `<tool_call>
+function_name
+<arg_key>key1</arg_key>
+<arg_key>key2</arg_key>
+<arg_key>key3</arg_key>
+<arg_value>val1</arg_value>
+<arg_value>val2</arg_value>
+<arg_value>val3</arg_value>
+</tool_call>`,
+			wantKeys:   []string{"key1", "key2", "key3"},
+			wantValues: []string{"val1", "val2", "val3"},
+		},
+		{
+			name: "mixed grouping",
+			xml: `<tool_call>
+function_name
+<arg_key>a</arg_key>
+<arg_value>1</arg_value>
+<arg_key>b</arg_key>
+<arg_key>c</arg_key>
+<arg_value>2</arg_value>
+<arg_value>3</arg_value>
+</tool_call>`,
+			wantKeys:   []string{"a", "b", "c"},
+			wantValues: []string{"1", "2", "3"},
+		},
+		{
+			name: "reverse order - all values then all keys",
+			xml: `<tool_call>
+function_name
+<arg_value>X</arg_value>
+<arg_value>Y</arg_value>
+<arg_value>Z</arg_value>
+<arg_key>x</arg_key>
+<arg_key>y</arg_key>
+<arg_key>z</arg_key>
+</tool_call>`,
+			wantKeys:   []string{"x", "y", "z"},
+			wantValues: []string{"X", "Y", "Z"},
+		},
+	}
+
+	for _, tc := range testCases {
+		t.Run(tc.name, func(t *testing.T) {
+			var parsed GLMToolCallXML
+			err := xml.Unmarshal([]byte(tc.xml), &parsed)
+			if err != nil {
+				t.Fatalf("failed to unmarshal XML: %v", err)
+			}
+
+			if !reflect.DeepEqual(parsed.Keys, tc.wantKeys) {
+				t.Errorf("Keys order mismatch:\ngot:  %v\nwant: %v", parsed.Keys, tc.wantKeys)
+			}
+
+			if !reflect.DeepEqual(parsed.Values, tc.wantValues) {
+				t.Errorf("Values order mismatch:\ngot:  %v\nwant: %v", parsed.Values, tc.wantValues)
+			}
+		})
+	}
+}
+
+func TestGLM46ToolCallParsing(t *testing.T) {
+	type testCase struct {
+		name         string
+		rawToolCall  string
+		tools        []api.Tool
+		wantToolCall api.ToolCall
+	}
+
+	cases := []testCase{
+		{
+			name:  "simple tool call",
+			tools: []api.Tool{},
+			rawToolCall: `get-current-weather
+<arg_key>location</arg_key>
+<arg_value>New York, NY</arg_value>
+<arg_key>unit</arg_key>
+<arg_value>celsius</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "get-current-weather",
+					Arguments: args(`{"location": "New York, NY", "unit": "celsius"}`),
+				},
+			},
+		},
+		{
+			name: "tool call with typed parameters",
+			tools: []api.Tool{
+				tool("calculate", map[string]api.ToolProperty{
+					"x":       {Type: api.PropertyType{"number"}},
+					"y":       {Type: api.PropertyType{"integer"}},
+					"enabled": {Type: api.PropertyType{"boolean"}},
+					"items":   {Type: api.PropertyType{"array"}},
+				}),
+			},
+			rawToolCall: `calculate
+<arg_key>x</arg_key>
+<arg_value>3.14</arg_value>
+<arg_key>y</arg_key>
+<arg_value>42</arg_value>
+<arg_key>enabled</arg_key>
+<arg_value>true</arg_value>
+<arg_key>items</arg_key>
+<arg_value>["a", "b", "c"]</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "calculate",
+					Arguments: args(`{"enabled": true, "items": ["a", "b", "c"], "x": 3.14, "y": 42}`),
+				},
+			},
+		},
+		{
+			name:  "function name with whitespace",
+			tools: []api.Tool{},
+			rawToolCall: `  get-weather
+<arg_key>city</arg_key>
+<arg_value>Paris</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "get-weather",
+					Arguments: args(`{"city": "Paris"}`),
+				},
+			},
+		},
+		{
+			name:  "values with special characters",
+			tools: []api.Tool{},
+			rawToolCall: `execute-command
+<arg_key>command</arg_key>
+<arg_value>ls && echo "done"</arg_value>
+<arg_key>message</arg_key>
+<arg_value>a < b and c > d</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "execute-command",
+					Arguments: args(`{"command": "ls && echo \"done\"", "message": "a < b and c > d"}`),
+				},
+			},
+		},
+		{
+			name:  "unicode in function names and values",
+			tools: []api.Tool{},
+			rawToolCall: `获取天气
+<arg_key>城市</arg_key>
+<arg_value>北京</arg_value>
+<arg_key>message</arg_key>
+<arg_value>Hello! 你好! 🌟</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "获取天气",
+					Arguments: args(`{"message": "Hello! 你好! 🌟", "城市": "北京"}`),
+				},
+			},
+		},
+		{
+			name:  "empty value",
+			tools: []api.Tool{},
+			rawToolCall: `test-function
+<arg_key>param1</arg_key>
+<arg_value></arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "test-function",
+					Arguments: args(`{"param1": ""}`),
+				},
+			},
+		},
+		{
+			name:  "special chars in arg_key names",
+			tools: []api.Tool{},
+			rawToolCall: `test-function
+<arg_key>param<1></arg_key>
+<arg_value>value1</arg_value>
+<arg_key>a&b</arg_key>
+<arg_value>value2</arg_value>
+<arg_key>x>y</arg_key>
+<arg_value>value3</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "test-function",
+					Arguments: args(`{"a&b": "value2", "param<1>": "value1", "x>y": "value3"}`),
+				},
+			},
+		},
+		{
+			name:  "multiple consecutive ampersands",
+			tools: []api.Tool{},
+			rawToolCall: `test-function
+<arg_key>param</arg_key>
+<arg_value>test &&&& more</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "test-function",
+					Arguments: args(`{"param": "test &&&& more"}`),
+				},
+			},
+		},
+		{
+			name:  "mixed special chars together",
+			tools: []api.Tool{},
+			rawToolCall: `test-function
+<arg_key>param</arg_key>
+<arg_value><>&<>&</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "test-function",
+					Arguments: args(`{"param": "<>&<>&"}`),
+				},
+			},
+		},
+		{
+			name:  "newlines and tabs in parameter values",
+			tools: []api.Tool{},
+			rawToolCall: `test-function
+<arg_key>multiline</arg_key>
+<arg_value>line1
+	indented line2
+line3</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "test-function",
+					Arguments: args(`{"multiline": "line1\n\tindented line2\nline3"}`),
+				},
+			},
+		},
+		{
+			name:  "single and double quotes in values",
+			tools: []api.Tool{},
+			rawToolCall: `test-function
+<arg_key>quotes</arg_key>
+<arg_value>She said "Hello's there!"</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "test-function",
+					Arguments: args(`{"quotes": "She said \"Hello's there!\""}`),
+				},
+			},
+		},
+		{
+			name:  "CDATA-like content that should be treated as text",
+			tools: []api.Tool{},
+			rawToolCall: `test-function
+<arg_key>cdata</arg_key>
+<arg_value><![CDATA[not actual cdata]]></arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "test-function",
+					Arguments: args(`{"cdata": "<![CDATA[not actual cdata]]>"}`),
+				},
+			},
+		},
+		{
+			name:  "all special XML entities",
+			tools: []api.Tool{},
+			rawToolCall: `test-function
+<arg_key>entities</arg_key>
+<arg_value>&lt;&gt;&amp;&apos;&quot;</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "test-function",
+					Arguments: args(`{"entities": "&lt;&gt;&amp;&apos;&quot;"}`),
+				},
+			},
+		},
+		{
+			name:  "order preservation with multiple parameters",
+			tools: []api.Tool{},
+			rawToolCall: `test-function
+<arg_key>first</arg_key>
+<arg_value>value1</arg_value>
+<arg_key>second</arg_key>
+<arg_value>value2</arg_value>
+<arg_key>third</arg_key>
+<arg_value>value3</arg_value>
+<arg_key>fourth</arg_key>
+<arg_value>value4</arg_value>
+<arg_key>fifth</arg_key>
+<arg_value>value5</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "test-function",
+					Arguments: args(`{"fifth": "value5", "first": "value1", "fourth": "value4", "second": "value2", "third": "value3"}`),
+				},
+			},
+		},
+		{
+			name:  "order preservation with identical key names but different positions",
+			tools: []api.Tool{},
+			rawToolCall: `test-function
+<arg_key>param</arg_key>
+<arg_value>first occurrence</arg_value>
+<arg_key>other</arg_key>
+<arg_value>middle</arg_value>
+<arg_key>param</arg_key>
+<arg_value>second occurrence</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name: "test-function",
+					// Later occurrence should overwrite earlier one
+					Arguments: args(`{"other": "middle", "param": "second occurrence"}`),
+				},
+			},
+		},
+		{
+			name: "array with mixed types",
+			tools: []api.Tool{
+				tool("process", map[string]api.ToolProperty{
+					"items": {Type: api.PropertyType{"array"}},
+				}),
+			},
+			rawToolCall: `process
+<arg_key>items</arg_key>
+<arg_value>[1, "hello", true, null]</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "process",
+					Arguments: args(`{"items": [1, "hello", true, null]}`),
+				},
+			},
+		},
+		{
+			name: "empty array",
+			tools: []api.Tool{
+				tool("test", map[string]api.ToolProperty{
+					"tags": {Type: api.PropertyType{"array"}},
+				}),
+			},
+			rawToolCall: `test
+<arg_key>tags</arg_key>
+<arg_value>[]</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "test",
+					Arguments: args(`{"tags": []}`),
+				},
+			},
+		},
+		{
+			name: "anyOf array or string - with array of objects",
+			tools: []api.Tool{
+				tool("TodoWrite", map[string]api.ToolProperty{
+					"todos": {AnyOf: []api.ToolProperty{{Type: api.PropertyType{"array"}}, {Type: api.PropertyType{"string"}}}},
+				}),
+			},
+			// <tool_call>TodoWrite
+			// <arg_key>todos</arg_key>
+			// <arg_value>[{"content": "Set up HTML file and basic structure", "id": "1", "priority": "high", "status": "pending"}, {"content": "Create 3D scene with Three.js", "id": "2", "priority": "high", "status": "pending"}, {"content": "Implement terrain generation with blocks", "id": "3", "priority": "high", "status": "pending"}, {"content": "Add player controls (movement, camera)", "id": "4", "priority": "high", "status": "pending"}, {"content": "Implement block placement/destruction", "id": "5", "priority": "medium", "status": "pending"}, {"content": "Add lighting and textures", "id": "6", "priority": "medium", "status": "pending"}, {"content": "Test and optimize performance", "id": "7", "priority": "low", "status": "pending"}]</arg_value>
+			// </tool_call>
+			rawToolCall: `TodoWrite
+<arg_key>todos</arg_key>
+<arg_value>[{"content": "task 1", "status": "pending", "priority": "high", "id": "1"}, {"content": "task 2", "status": "completed", "priority": "low", "id": "2"}]</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "TodoWrite",
+					Arguments: args(`{"todos": [{"content": "task 1", "id": "1", "priority": "high", "status": "pending"}, {"content": "task 2", "id": "2", "priority": "low", "status": "completed"}]}`),
+				},
+			},
+		},
+		{
+			name: "anyOf array or string - with plain string",
+			tools: []api.Tool{
+				tool("TodoWrite", map[string]api.ToolProperty{
+					"todos": {Type: api.PropertyType{"array", "string"}},
+				}),
+			},
+			rawToolCall: `TodoWrite
+<arg_key>todos</arg_key>
+<arg_value>Error: could not load todos</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "TodoWrite",
+					Arguments: args(`{"todos": "Error: could not load todos"}`),
+				},
+			},
+		},
+	}
+
+	for i, tc := range cases {
+		t.Run(tc.name, func(t *testing.T) {
+			gotToolCall, err := parseGLM46ToolCall(glm46EventRawToolCall{raw: tc.rawToolCall}, tc.tools)
+			if err != nil {
+				t.Errorf("case %d (%s): %v", i, tc.name, err)
+			}
+			if !toolCallEqual(gotToolCall, tc.wantToolCall) {
+				t.Errorf("case %d (%s): got tool call %#v, want %#v", i, tc.name, gotToolCall, tc.wantToolCall)
+			}
+		})
+	}
+}

model/parsers/glm47.go

@@ -0,0 +1,20 @@
+package parsers
+
+import "github.com/ollama/ollama/api"
+
+// GLM47Parser extends GLM46Parser with thinking-aware initialization.
+// GLM-4.7's prompt ends with <think> when thinking is enabled, so the parser
+// must start in CollectingThinking state (the model outputs thinking content directly).
+type GLM47Parser struct {
+	GLM46Parser
+}
+
+func (p *GLM47Parser) Init(tools []api.Tool, lastMessage *api.Message, thinkValue *api.ThinkValue) []api.Tool {
+	p.tools = tools
+	// When thinking is enabled (nil or true), the prompt ends with <think>,
+	// so model output starts directly with thinking content (no opening tag).
+	if thinkValue == nil || thinkValue.Bool() {
+		p.state = glm46ParserState_CollectingThinking
+	}
+	return tools
+}

model/parsers/glm47_test.go

@@ -0,0 +1,99 @@
+package parsers
+
+import (
+	"reflect"
+	"testing"
+
+	"github.com/ollama/ollama/api"
+)
+
+func TestGLM47ParserAdd(t *testing.T) {
+	parser := GLM47Parser{}
+	parser.Init([]api.Tool{
+		tool("calculate", map[string]api.ToolProperty{
+			"count":   {Type: api.PropertyType{"integer"}},
+			"enabled": {Type: api.PropertyType{"boolean"}},
+		}),
+	}, nil, nil)
+
+	// When thinking is enabled (thinkValue nil), the prompt ends with <think>,
+	// so the model output does NOT include the opening <think> tag.
+	content, thinking, calls, err := parser.Add("plan</think>Answer<tool_call>calculate<arg_key>count</arg_key><arg_value>3</arg_value><arg_key>enabled</arg_key><arg_value>true</arg_value></tool_call>", true)
+	if err != nil {
+		t.Fatalf("parse failed: %v", err)
+	}
+	if thinking != "plan" {
+		t.Fatalf("expected thinking 'plan', got %q", thinking)
+	}
+	if content != "Answer" {
+		t.Fatalf("expected content 'Answer', got %q", content)
+	}
+	if len(calls) != 1 {
+		t.Fatalf("expected 1 tool call, got %d", len(calls))
+	}
+	expectedArgs := args(`{"count": 3, "enabled": true}`)
+	if !toolCallEqual(api.ToolCall{Function: api.ToolCallFunction{Arguments: calls[0].Function.Arguments}}, api.ToolCall{Function: api.ToolCallFunction{Arguments: expectedArgs}}) {
+		t.Fatalf("expected args %#v, got %#v", expectedArgs.ToMap(), calls[0].Function.Arguments.ToMap())
+	}
+}
+
+func TestGLM47ParserNoThinkingContent(t *testing.T) {
+	parser := GLM47Parser{}
+	parser.Init(nil, nil, nil)
+
+	// When thinking is enabled but model has no thinking to output,
+	// it should output </think> immediately followed by content.
+	content, thinking, calls, err := parser.Add("</think>Plain answer", true)
+	if err != nil {
+		t.Fatalf("parse failed: %v", err)
+	}
+	if thinking != "" {
+		t.Fatalf("expected empty thinking, got %q", thinking)
+	}
+	if content != "Plain answer" {
+		t.Fatalf("expected content 'Plain answer', got %q", content)
+	}
+	if len(calls) != 0 {
+		t.Fatalf("expected no tool calls, got %d", len(calls))
+	}
+}
+
+func TestGLM47ParserThinkingDisabled(t *testing.T) {
+	parser := GLM47Parser{}
+	// When thinking is disabled, parser stays in LookingForThinkingOpen state
+	parser.Init(nil, nil, &api.ThinkValue{Value: false})
+
+	// Model outputs plain content (prompt ended with </think>)
+	content, thinking, calls, err := parser.Add("Plain answer", true)
+	if err != nil {
+		t.Fatalf("parse failed: %v", err)
+	}
+	if thinking != "" {
+		t.Fatalf("expected empty thinking, got %q", thinking)
+	}
+	if content != "Plain answer" {
+		t.Fatalf("expected content 'Plain answer', got %q", content)
+	}
+	if len(calls) != 0 {
+		t.Fatalf("expected no tool calls, got %d", len(calls))
+	}
+}
+
+func TestGLM47ParserToolCallEscaping(t *testing.T) {
+	toolCall, err := parseGLM46ToolCall(glm46EventRawToolCall{raw: `exec
+<arg_key>expr</arg_key>
+<arg_value>a < b && c > d</arg_value>`}, nil)
+	if err != nil {
+		t.Fatalf("parse failed: %v", err)
+	}
+
+	expected := api.ToolCall{
+		Function: api.ToolCallFunction{
+			Name:      "exec",
+			Arguments: args(`{"expr": "a < b && c > d"}`),
+		},
+	}
+	if !reflect.DeepEqual(toolCall, expected) {
+		t.Fatalf("expected %#v, got %#v", expected, toolCall)
+	}
+}

model/parsers/lfm2.go

@@ -0,0 +1,498 @@
+package parsers
+
+import (
+	"encoding/json"
+	"errors"
+	"log/slog"
+	"strconv"
+	"strings"
+	"unicode"
+
+	"github.com/ollama/ollama/api"
+)
+
+type LFM2ParserState int
+
+const (
+	LFM2CollectingThinking LFM2ParserState = iota
+	LFM2CollectingContent
+	LFM2CollectingToolCalls
+)
+
+const (
+	lfm2ThinkingOpenTag  = "<think>"
+	lfm2ThinkingCloseTag = "</think>"
+	lfm2ToolCallStartTag = "<|tool_call_start|>"
+	lfm2ToolCallEndTag   = "<|tool_call_end|>"
+)
+
+type LFM2Parser struct {
+	state                    LFM2ParserState
+	buffer                   strings.Builder
+	hasThinkingSupport       bool
+	needsThinkingLeadingTrim bool // trim leading whitespace after <think> tag
+	needsContentLeadingTrim  bool // trim leading whitespace after </think> tag
+}
+
+func (p *LFM2Parser) HasToolSupport() bool {
+	return true
+}
+
+func (p *LFM2Parser) HasThinkingSupport() bool {
+	return p.hasThinkingSupport
+}
+
+func (p *LFM2Parser) setInitialState(lastMessage *api.Message, thinkValue *api.ThinkValue) {
+	prefill := lastMessage != nil && lastMessage.Role == "assistant"
+
+	// Check both model capability AND request preference
+	thinkingEnabled := p.HasThinkingSupport() && (thinkValue != nil && thinkValue.Bool())
+
+	if !thinkingEnabled {
+		p.state = LFM2CollectingContent
+		return
+	}
+
+	if prefill && lastMessage.Content != "" {
+		p.state = LFM2CollectingContent
+		return
+	}
+
+	p.state = LFM2CollectingThinking
+	p.needsThinkingLeadingTrim = true
+}
+
+func (p *LFM2Parser) Init(tools []api.Tool, lastMessage *api.Message, thinkValue *api.ThinkValue) []api.Tool {
+	p.setInitialState(lastMessage, thinkValue)
+	return tools
+}
+
+type lfm2Event interface {
+	isLFM2Event()
+}
+
+type lfm2EventThinkingContent struct {
+	content string
+}
+
+type lfm2EventContent struct {
+	content string
+}
+
+type lfm2EventToolCall struct {
+	toolCall api.ToolCall
+}
+
+func (lfm2EventThinkingContent) isLFM2Event() {}
+func (lfm2EventContent) isLFM2Event()         {}
+func (lfm2EventToolCall) isLFM2Event()        {}
+
+func (p *LFM2Parser) Add(s string, done bool) (content string, thinking string, calls []api.ToolCall, err error) {
+	p.buffer.WriteString(s)
+	events := p.parseEvents()
+
+	var toolCalls []api.ToolCall
+	var contentSb strings.Builder
+	var thinkingSb strings.Builder
+	for _, event := range events {
+		switch event := event.(type) {
+		case lfm2EventToolCall:
+			toolCalls = append(toolCalls, event.toolCall)
+		case lfm2EventThinkingContent:
+			thinkingSb.WriteString(event.content)
+		case lfm2EventContent:
+			contentSb.WriteString(event.content)
+		}
+	}
+
+	return contentSb.String(), thinkingSb.String(), toolCalls, nil
+}
+
+func (p *LFM2Parser) parseEvents() []lfm2Event {
+	var all []lfm2Event
+
+	keepLooping := true
+	for keepLooping {
+		var events []lfm2Event
+		events, keepLooping = p.eat()
+		if len(events) > 0 {
+			all = append(all, events...)
+		}
+	}
+
+	return all
+}
+
+func (p *LFM2Parser) eat() ([]lfm2Event, bool) {
+	var events []lfm2Event
+	bufStr := p.buffer.String()
+	if bufStr == "" {
+		return events, false
+	}
+
+	switch p.state {
+	case LFM2CollectingThinking:
+		// Strip opening <think> tag if present
+		if strings.HasPrefix(bufStr, lfm2ThinkingOpenTag) {
+			bufStr = bufStr[len(lfm2ThinkingOpenTag):]
+			p.needsThinkingLeadingTrim = true
+			p.buffer.Reset()
+			p.buffer.WriteString(bufStr)
+		}
+
+		// Trim leading whitespace after <think> tag (may span multiple chunks)
+		if p.needsThinkingLeadingTrim {
+			if trimmed := strings.TrimLeftFunc(bufStr, unicode.IsSpace); trimmed != bufStr {
+				bufStr = trimmed
+				p.buffer.Reset()
+				p.buffer.WriteString(bufStr)
+			}
+			// Clear flag once we have non-whitespace content or buffer is empty
+			if len(bufStr) > 0 {
+				p.needsThinkingLeadingTrim = false
+			}
+		}
+
+		if strings.Contains(bufStr, lfm2ThinkingCloseTag) { // thinking[</think>] -> content
+			split := strings.SplitN(bufStr, lfm2ThinkingCloseTag, 2)
+			thinking := split[0]
+			thinking = strings.TrimRightFunc(thinking, unicode.IsSpace)
+
+			remaining := split[1]
+			remaining = strings.TrimLeftFunc(remaining, unicode.IsSpace)
+
+			p.buffer.Reset()
+			p.buffer.WriteString(remaining)
+			p.state = LFM2CollectingContent
+			p.needsThinkingLeadingTrim = false
+			// Set flag to trim any additional whitespace that may arrive in later chunks
+			p.needsContentLeadingTrim = len(remaining) == 0
+
+			if len(thinking) > 0 {
+				events = append(events, lfm2EventThinkingContent{content: thinking})
+			}
+			return events, true
+		} else if overlapLen := overlap(bufStr, lfm2ThinkingCloseTag); overlapLen > 0 { // partial </think>
+			beforePartialTag := bufStr[:len(bufStr)-overlapLen]
+			trailingLen := trailingWhitespaceLen(beforePartialTag)
+			ambiguousStart := len(beforePartialTag) - trailingLen
+
+			unambiguous := bufStr[:ambiguousStart]
+			ambiguous := bufStr[ambiguousStart:]
+			p.buffer.Reset()
+			p.buffer.WriteString(ambiguous)
+			if len(unambiguous) > 0 {
+				events = append(events, lfm2EventThinkingContent{content: unambiguous})
+			}
+			return events, false
+		} else { // otherwise its thinking content
+			whitespaceLen := trailingWhitespaceLen(bufStr)
+			ambiguousStart := len(bufStr) - whitespaceLen
+
+			unambiguous := bufStr[:ambiguousStart]
+			ambiguous := bufStr[ambiguousStart:]
+			p.buffer.Reset()
+			p.buffer.WriteString(ambiguous)
+			if len(unambiguous) > 0 {
+				events = append(events, lfm2EventThinkingContent{content: unambiguous})
+			}
+			return events, false
+		}
+
+	case LFM2CollectingContent:
+		// Trim leading whitespace after </think> tag (may span multiple chunks)
+		if p.needsContentLeadingTrim {
+			if trimmed := strings.TrimLeftFunc(bufStr, unicode.IsSpace); trimmed != bufStr {
+				bufStr = trimmed
+				p.buffer.Reset()
+				p.buffer.WriteString(bufStr)
+			}
+			// Clear flag once we have non-whitespace content
+			if len(bufStr) > 0 {
+				p.needsContentLeadingTrim = false
+			}
+		}
+
+		if strings.Contains(bufStr, lfm2ToolCallStartTag) { // content[<|tool_call_start|>] -> tool calls
+			split := strings.SplitN(bufStr, lfm2ToolCallStartTag, 2)
+			contentBefore := strings.TrimRightFunc(split[0], unicode.IsSpace)
+			remaining := split[1]
+
+			p.buffer.Reset()
+			p.buffer.WriteString(remaining)
+			p.state = LFM2CollectingToolCalls
+
+			if len(contentBefore) > 0 {
+				events = append(events, lfm2EventContent{content: contentBefore})
+			}
+			return events, true
+		} else { // otherwise its content
+			p.buffer.Reset()
+			if len(bufStr) > 0 {
+				events = append(events, lfm2EventContent{content: bufStr})
+			}
+			return events, false
+		}
+
+	case LFM2CollectingToolCalls:
+		// Look for complete tool call JSON between tags
+		if idx := strings.Index(bufStr, lfm2ToolCallEndTag); idx != -1 {
+			toolCallContent := bufStr[:idx]
+
+			if toolCalls, err := p.parseToolCallsContent(toolCallContent); err == nil && len(toolCalls) > 0 {
+				remaining := bufStr[idx+len(lfm2ToolCallEndTag):]
+
+				// Check if there's another tool call
+				if strings.HasPrefix(remaining, lfm2ToolCallStartTag) {
+					remaining = remaining[len(lfm2ToolCallStartTag):]
+				} else {
+					// No more tool calls, go back to content
+					remaining = strings.TrimLeftFunc(remaining, unicode.IsSpace)
+					p.state = LFM2CollectingContent
+				}
+
+				p.buffer.Reset()
+				p.buffer.WriteString(remaining)
+
+				for _, tc := range toolCalls {
+					events = append(events, lfm2EventToolCall{toolCall: tc})
+				}
+				return events, true
+			} else if err != nil {
+				slog.Warn("lfm2 tool call parsing failed", "error", err, "content", toolCallContent)
+			}
+		}
+
+		return events, false
+	}
+
+	return events, false
+}
+
+// parseToolCallsContent parses one or more tool calls from content
+// Supports JSON format and Python-style format including multiple calls: [func1(...),func2(...)]
+func (p *LFM2Parser) parseToolCallsContent(content string) ([]api.ToolCall, error) {
+	content = strings.TrimSpace(content)
+
+	// Try JSON format first: {"name": "func", "arguments": {...}}
+	var parsed struct {
+		Name      string          `json:"name"`
+		Arguments json.RawMessage `json:"arguments"`
+	}
+
+	if err := json.Unmarshal([]byte(content), &parsed); err == nil && parsed.Name != "" {
+		var args api.ToolCallFunctionArguments
+		if len(parsed.Arguments) > 0 {
+			if err := json.Unmarshal(parsed.Arguments, &args); err != nil {
+				return nil, err
+			}
+		} else {
+			args = api.NewToolCallFunctionArguments()
+		}
+
+		return []api.ToolCall{{
+			Function: api.ToolCallFunction{
+				Name:      parsed.Name,
+				Arguments: args,
+			},
+		}}, nil
+	}
+
+	// Try Python-style format: [func(arg1='val1'),func2(arg2='val2')] or func(arg1='val1')
+	return p.parsePythonStyleToolCalls(content)
+}
+
+// parsePythonStyleToolCalls parses one or more Python-style tool calls
+// Examples: [bash(command='ls'),bash(command='pwd')] or bash(command='ls')
+func (p *LFM2Parser) parsePythonStyleToolCalls(content string) ([]api.ToolCall, error) {
+	content = strings.TrimSpace(content)
+
+	// Strip outer brackets if present: [func(...)] -> func(...)
+	if strings.HasPrefix(content, "[") && strings.HasSuffix(content, "]") {
+		content = content[1 : len(content)-1]
+	}
+
+	var toolCalls []api.ToolCall
+
+	// Parse multiple function calls separated by commas at the top level
+	for len(content) > 0 {
+		content = strings.TrimSpace(content)
+		if content == "" {
+			break
+		}
+
+		// Skip leading comma from previous iteration
+		if strings.HasPrefix(content, ",") {
+			content = strings.TrimSpace(content[1:])
+			if content == "" {
+				break
+			}
+		}
+
+		// Find function name
+		parenIdx := strings.Index(content, "(")
+		if parenIdx == -1 {
+			return nil, errors.New("invalid tool call: no opening parenthesis")
+		}
+
+		funcName := strings.TrimSpace(content[:parenIdx])
+		if funcName == "" {
+			return nil, errors.New("invalid tool call: empty function name")
+		}
+
+		// Find matching closing parenthesis
+		closeIdx := findMatchingParen(content, parenIdx)
+		if closeIdx == -1 {
+			return nil, errors.New("invalid tool call: no matching closing parenthesis")
+		}
+
+		argsStr := content[parenIdx+1 : closeIdx]
+		args := api.NewToolCallFunctionArguments()
+
+		if argsStr != "" {
+			if err := parsePythonArgs(argsStr, &args); err != nil {
+				return nil, err
+			}
+		}
+
+		toolCalls = append(toolCalls, api.ToolCall{
+			Function: api.ToolCallFunction{
+				Name:      funcName,
+				Arguments: args,
+			},
+		})
+
+		// Move past this function call
+		content = content[closeIdx+1:]
+	}
+
+	if len(toolCalls) == 0 {
+		return nil, errors.New("no tool calls found")
+	}
+
+	return toolCalls, nil
+}
+
+// findMatchingParen finds the index of the closing parenthesis matching the one at openIdx
+// Returns -1 if not found. Handles nested parentheses and quoted strings.
+func findMatchingParen(s string, openIdx int) int {
+	depth := 1
+	i := openIdx + 1
+	for i < len(s) && depth > 0 {
+		switch s[i] {
+		case '(':
+			depth++
+		case ')':
+			depth--
+			if depth == 0 {
+				return i
+			}
+		case '\'', '"':
+			// Skip quoted string
+			quote := s[i]
+			i++
+			for i < len(s) && s[i] != quote {
+				if s[i] == '\\' && i+1 < len(s) {
+					i++ // skip escaped char
+				}
+				i++
+			}
+		}
+		i++
+	}
+	return -1
+}
+
+// parseToolCallContent parses a single tool call (for backward compatibility with tests)
+func (p *LFM2Parser) parseToolCallContent(content string) (api.ToolCall, error) {
+	calls, err := p.parseToolCallsContent(content)
+	if err != nil {
+		return api.ToolCall{}, err
+	}
+	if len(calls) == 0 {
+		return api.ToolCall{}, errors.New("no tool call found")
+	}
+	return calls[0], nil
+}
+
+// parsePythonArgs parses Python-style keyword arguments: key='value', key2="value2"
+func parsePythonArgs(argsStr string, args *api.ToolCallFunctionArguments) error {
+	// Simple state machine to parse key='value' pairs
+	// Handles: command='ls', flag="-la", count=42, enabled=true
+	var key string
+	i := 0
+
+	for i < len(argsStr) {
+		// Skip whitespace
+		for i < len(argsStr) && (argsStr[i] == ' ' || argsStr[i] == '\t' || argsStr[i] == '\n') {
+			i++
+		}
+		if i >= len(argsStr) {
+			break
+		}
+
+		// Parse key
+		keyStart := i
+		for i < len(argsStr) && argsStr[i] != '=' && argsStr[i] != ',' {
+			i++
+		}
+		if i >= len(argsStr) || argsStr[i] != '=' {
+			return errors.New("invalid argument: expected '='")
+		}
+		key = strings.TrimSpace(argsStr[keyStart:i])
+		i++ // skip '='
+
+		// Skip whitespace after =
+		for i < len(argsStr) && (argsStr[i] == ' ' || argsStr[i] == '\t') {
+			i++
+		}
+
+		// Parse value
+		var value string
+		if i < len(argsStr) && (argsStr[i] == '\'' || argsStr[i] == '"') {
+			// Quoted string
+			quote := argsStr[i]
+			i++
+			valueStart := i
+			for i < len(argsStr) && argsStr[i] != quote {
+				if argsStr[i] == '\\' && i+1 < len(argsStr) {
+					i += 2 // skip escaped char
+				} else {
+					i++
+				}
+			}
+			value = argsStr[valueStart:i]
+			if i < len(argsStr) {
+				i++ // skip closing quote
+			}
+			args.Set(key, value)
+		} else {
+			// Unquoted value (number, bool, etc)
+			valueStart := i
+			for i < len(argsStr) && argsStr[i] != ',' {
+				i++
+			}
+			value = strings.TrimSpace(argsStr[valueStart:i])
+
+			// Try to parse as number or bool
+			if v, err := strconv.ParseInt(value, 10, 64); err == nil {
+				args.Set(key, v)
+			} else if v, err := strconv.ParseFloat(value, 64); err == nil {
+				args.Set(key, v)
+			} else if value == "true" {
+				args.Set(key, true)
+			} else if value == "false" {
+				args.Set(key, false)
+			} else {
+				args.Set(key, value)
+			}
+		}
+
+		// Skip comma and whitespace
+		for i < len(argsStr) && (argsStr[i] == ',' || argsStr[i] == ' ' || argsStr[i] == '\t' || argsStr[i] == '\n') {
+			i++
+		}
+	}
+
+	return nil
+}

model/parsers/lfm2_test.go

@@ -0,0 +1,780 @@
+package parsers
+
+import (
+	"testing"
+
+	"github.com/google/go-cmp/cmp"
+
+	"github.com/ollama/ollama/api"
+)
+
+func TestLFM2Parser(t *testing.T) {
+	tests := []struct {
+		name             string
+		input            string
+		expectedContent  string
+		expectedThinking string
+		expectedCalls    []api.ToolCall
+		hasThinking      bool
+	}{
+		{
+			name:            "simple_content",
+			input:           "Hello, how are you?",
+			expectedContent: "Hello, how are you?",
+			hasThinking:     false,
+		},
+		{
+			name:             "thinking_content",
+			input:            "I need to think about this...</think>The answer is 42.",
+			expectedThinking: "I need to think about this...",
+			expectedContent:  "The answer is 42.",
+			hasThinking:      true,
+		},
+		{
+			name:             "thinking_with_newlines",
+			input:            "Let me think:\n- Point 1\n- Point 2</think>\n\nHere's my answer.",
+			expectedThinking: "Let me think:\n- Point 1\n- Point 2",
+			expectedContent:  "Here's my answer.",
+			hasThinking:      true,
+		},
+		{
+			name:            "tool_call_simple",
+			input:           "I'll check the weather.<|tool_call_start|>{\"name\":\"get_weather\",\"arguments\":{\"location\":\"Paris\"}}<|tool_call_end|>",
+			expectedContent: "I'll check the weather.",
+			expectedCalls: []api.ToolCall{
+				{
+					Function: api.ToolCallFunction{
+						Name: "get_weather",
+						Arguments: testArgs(map[string]any{
+							"location": "Paris",
+						}),
+					},
+				},
+			},
+			hasThinking: false,
+		},
+		{
+			name:            "multiple_tool_calls",
+			input:           "Getting weather for both cities.<|tool_call_start|>{\"name\":\"get_weather\",\"arguments\":{\"location\":\"Paris\"}}<|tool_call_end|><|tool_call_start|>{\"name\":\"get_weather\",\"arguments\":{\"location\":\"London\"}}<|tool_call_end|>",
+			expectedContent: "Getting weather for both cities.",
+			expectedCalls: []api.ToolCall{
+				{
+					Function: api.ToolCallFunction{
+						Name: "get_weather",
+						Arguments: testArgs(map[string]any{
+							"location": "Paris",
+						}),
+					},
+				},
+				{
+					Function: api.ToolCallFunction{
+						Name: "get_weather",
+						Arguments: testArgs(map[string]any{
+							"location": "London",
+						}),
+					},
+				},
+			},
+			hasThinking: false,
+		},
+		{
+			name:            "complex_tool_arguments",
+			input:           "Processing data.<|tool_call_start|>{\"name\":\"process_data\",\"arguments\":{\"items\":[\"item1\",\"item2\"],\"config\":{\"enabled\":true,\"threshold\":0.95}}}<|tool_call_end|>",
+			expectedContent: "Processing data.",
+			expectedCalls: []api.ToolCall{
+				{
+					Function: api.ToolCallFunction{
+						Name: "process_data",
+						Arguments: testArgs(map[string]any{
+							"items":  []interface{}{"item1", "item2"},
+							"config": map[string]interface{}{"enabled": true, "threshold": 0.95},
+						}),
+					},
+				},
+			},
+			hasThinking: false,
+		},
+		{
+			name:             "thinking_with_tool_call",
+			input:            "Let me check the weather...</think>I'll get that for you.<|tool_call_start|>{\"name\":\"get_weather\",\"arguments\":{\"location\":\"Paris\"}}<|tool_call_end|>",
+			expectedThinking: "Let me check the weather...",
+			expectedContent:  "I'll get that for you.",
+			expectedCalls: []api.ToolCall{
+				{
+					Function: api.ToolCallFunction{
+						Name: "get_weather",
+						Arguments: testArgs(map[string]any{
+							"location": "Paris",
+						}),
+					},
+				},
+			},
+			hasThinking: true,
+		},
+		{
+			name:            "empty_content",
+			input:           "",
+			expectedContent: "",
+			hasThinking:     false,
+		},
+		{
+			name:             "only_thinking",
+			input:            "Just thinking content</think>",
+			expectedThinking: "Just thinking content",
+			expectedContent:  "",
+			hasThinking:      true,
+		},
+		{
+			name:            "unicode_content",
+			input:           "مرحبا بالعالم! 你好世界! 🌍",
+			expectedContent: "مرحبا بالعالم! 你好世界! 🌍",
+			hasThinking:     false,
+		},
+		{
+			name:            "newlines_and_whitespace",
+			input:           "Line 1\n\nLine 3\t\tTabbed content",
+			expectedContent: "Line 1\n\nLine 3\t\tTabbed content",
+			hasThinking:     false,
+		},
+		{
+			name:             "thinking_with_unicode",
+			input:            "我在思考这个问题...</think>答案是42。",
+			expectedThinking: "我在思考这个问题...",
+			expectedContent:  "答案是42。",
+			hasThinking:      true,
+		},
+		{
+			name:            "tool_call_with_unicode_args",
+			input:           "Searching for information.<|tool_call_start|>{\"name\":\"search\",\"arguments\":{\"query\":\"北京天气\",\"language\":\"中文\"}}<|tool_call_end|>",
+			expectedContent: "Searching for information.",
+			expectedCalls: []api.ToolCall{
+				{
+					Function: api.ToolCallFunction{
+						Name: "search",
+						Arguments: testArgs(map[string]any{
+							"query":    "北京天气",
+							"language": "中文",
+						}),
+					},
+				},
+			},
+			hasThinking: false,
+		},
+		{
+			name:             "thinking_with_special_chars",
+			input:            "Let me calculate: 2+2=4 & 3*3=9...</think>The results are correct!",
+			expectedThinking: "Let me calculate: 2+2=4 & 3*3=9...",
+			expectedContent:  "The results are correct!",
+			hasThinking:      true,
+		},
+		{
+			name:            "empty_tool_call_args",
+			input:           "Pinging server.<|tool_call_start|>{\"name\":\"ping\",\"arguments\":{}}<|tool_call_end|>",
+			expectedContent: "Pinging server.",
+			expectedCalls: []api.ToolCall{
+				{
+					Function: api.ToolCallFunction{
+						Name:      "ping",
+						Arguments: api.NewToolCallFunctionArguments(),
+					},
+				},
+			},
+			hasThinking: false,
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			parser := &LFM2Parser{hasThinkingSupport: tt.hasThinking}
+			parser.Init([]api.Tool{}, nil, &api.ThinkValue{Value: tt.hasThinking})
+
+			content, thinking, calls, err := parser.Add(tt.input, true)
+			if err != nil {
+				t.Fatalf("Add() error = %v", err)
+			}
+
+			if diff := cmp.Diff(tt.expectedContent, content); diff != "" {
+				t.Errorf("Content mismatch (-want +got):\n%s", diff)
+			}
+
+			if diff := cmp.Diff(tt.expectedThinking, thinking); diff != "" {
+				t.Errorf("Thinking mismatch (-want +got):\n%s", diff)
+			}
+
+			if diff := cmp.Diff(tt.expectedCalls, calls, argsComparer); diff != "" {
+				t.Errorf("Tool calls mismatch (-want +got):\n%s", diff)
+			}
+		})
+	}
+}
+
+func TestLFM2Parser_Streaming(t *testing.T) {
+	tests := []struct {
+		name             string
+		chunks           []string
+		expectedContent  string
+		expectedThinking string
+		expectedCalls    []api.ToolCall
+		hasThinking      bool
+	}{
+		{
+			name:            "streaming_simple_content",
+			chunks:          []string{"Hello, ", "how are ", "you?"},
+			expectedContent: "Hello, how are you?",
+			hasThinking:     false,
+		},
+		{
+			name:             "streaming_thinking",
+			chunks:           []string{"I need to ", "think about this", "...</think>", "The answer is 42."},
+			expectedThinking: "I need to think about this...",
+			expectedContent:  "The answer is 42.",
+			hasThinking:      true,
+		},
+		{
+			name:            "streaming_tool_call",
+			chunks:          []string{"I'll check weather.", "<|tool_call_start|>", "{\"name\":\"get_weather\",", "\"arguments\":{\"location\":\"Paris\"}}", "<|tool_call_end|>"},
+			expectedContent: "I'll check weather.",
+			expectedCalls: []api.ToolCall{
+				{
+					Function: api.ToolCallFunction{
+						Name: "get_weather",
+						Arguments: testArgs(map[string]any{
+							"location": "Paris",
+						}),
+					},
+				},
+			},
+			hasThinking: false,
+		},
+		{
+			name:             "streaming_thinking_with_partial_tag",
+			chunks:           []string{"Thinking about this", "...</", "think>", "Done thinking."},
+			expectedThinking: "Thinking about this...",
+			expectedContent:  "Done thinking.",
+			hasThinking:      true,
+		},
+		{
+			name:            "streaming_unicode_content",
+			chunks:          []string{"مرحبا ", "بالعالم! ", "你好", "世界!"},
+			expectedContent: "مرحبا بالعالم! 你好世界!",
+			hasThinking:     false,
+		},
+		{
+			name:            "streaming_tool_call_with_split_json",
+			chunks:          []string{"Processing.", "<|tool_call_start|>{\"name\":\"calc\",\"arguments\":{\"x\":", "42,\"y\":", "24}}<|tool_call_end|>"},
+			expectedContent: "Processing.",
+			expectedCalls: []api.ToolCall{
+				{
+					Function: api.ToolCallFunction{
+						Name: "calc",
+						Arguments: testArgs(map[string]any{
+							"x": float64(42),
+							"y": float64(24),
+						}),
+					},
+				},
+			},
+			hasThinking: false,
+		},
+		{
+			// Test that leading whitespace after <think> is trimmed even when in separate chunks
+			name:             "streaming_thinking_whitespace_after_tag",
+			chunks:           []string{"<think>", "\n\n  ", "Actual thinking content", "</think>", "Response"},
+			expectedThinking: "Actual thinking content",
+			expectedContent:  "Response",
+			hasThinking:      true,
+		},
+		{
+			// Test whitespace between </think> and content in streaming
+			name:             "streaming_whitespace_after_close_tag",
+			chunks:           []string{"<think>Thinking</think>", "\n\n\n", "Response content"},
+			expectedThinking: "Thinking",
+			expectedContent:  "Response content",
+			hasThinking:      true,
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			parser := &LFM2Parser{hasThinkingSupport: tt.hasThinking}
+			parser.Init([]api.Tool{}, nil, &api.ThinkValue{Value: tt.hasThinking})
+
+			var allContent, allThinking string
+			var allCalls []api.ToolCall
+
+			for i, chunk := range tt.chunks {
+				done := i == len(tt.chunks)-1
+				content, thinking, calls, err := parser.Add(chunk, done)
+				if err != nil {
+					t.Fatalf("Add() error = %v", err)
+				}
+
+				allContent += content
+				allThinking += thinking
+				allCalls = append(allCalls, calls...)
+			}
+
+			if diff := cmp.Diff(tt.expectedContent, allContent); diff != "" {
+				t.Errorf("Content mismatch (-want +got):\n%s", diff)
+			}
+
+			if diff := cmp.Diff(tt.expectedThinking, allThinking); diff != "" {
+				t.Errorf("Thinking mismatch (-want +got):\n%s", diff)
+			}
+
+			if diff := cmp.Diff(tt.expectedCalls, allCalls, argsComparer); diff != "" {
+				t.Errorf("Tool calls mismatch (-want +got):\n%s", diff)
+			}
+		})
+	}
+}
+
+func TestLFM2Parser_HasThinkingSupport(t *testing.T) {
+	tests := []struct {
+		name            string
+		hasThinking     bool
+		expectedSupport bool
+	}{
+		{
+			name:            "thinking_enabled",
+			hasThinking:     true,
+			expectedSupport: true,
+		},
+		{
+			name:            "thinking_disabled",
+			hasThinking:     false,
+			expectedSupport: false,
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			parser := &LFM2Parser{hasThinkingSupport: tt.hasThinking}
+			if got := parser.HasThinkingSupport(); got != tt.expectedSupport {
+				t.Errorf("HasThinkingSupport() = %v, want %v", got, tt.expectedSupport)
+			}
+		})
+	}
+}
+
+func TestLFM2Parser_HasToolSupport(t *testing.T) {
+	parser := &LFM2Parser{}
+	if !parser.HasToolSupport() {
+		t.Error("HasToolSupport() should return true")
+	}
+}
+
+func TestLFM2Parser_Init(t *testing.T) {
+	parser := &LFM2Parser{hasThinkingSupport: true}
+	tools := []api.Tool{
+		{
+			Type: "function",
+			Function: api.ToolFunction{
+				Name: "test_tool",
+			},
+		},
+	}
+
+	returnedTools := parser.Init(tools, nil, &api.ThinkValue{Value: true})
+
+	if diff := cmp.Diff(tools, returnedTools, toolsComparer); diff != "" {
+		t.Errorf("Init() returned tools mismatch (-want +got):\n%s", diff)
+	}
+
+	// Test initial state is set to thinking when enabled
+	if parser.state != LFM2CollectingThinking {
+		t.Errorf("Expected initial state to be LFM2CollectingThinking, got %v", parser.state)
+	}
+}
+
+func TestLFM2Parser_parseToolCallContent(t *testing.T) {
+	tests := []struct {
+		name        string
+		content     string
+		expected    api.ToolCall
+		expectError bool
+	}{
+		{
+			name:    "valid_tool_call",
+			content: `{"name":"get_weather","arguments":{"location":"Paris"}}`,
+			expected: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name: "get_weather",
+					Arguments: testArgs(map[string]any{
+						"location": "Paris",
+					}),
+				},
+			},
+		},
+		{
+			name:    "complex_arguments",
+			content: `{"name":"process_data","arguments":{"items":["a","b"],"config":{"enabled":true}}}`,
+			expected: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name: "process_data",
+					Arguments: testArgs(map[string]any{
+						"items":  []interface{}{"a", "b"},
+						"config": map[string]interface{}{"enabled": true},
+					}),
+				},
+			},
+		},
+		{
+			name:    "empty_arguments",
+			content: `{"name":"ping","arguments":{}}`,
+			expected: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "ping",
+					Arguments: api.NewToolCallFunctionArguments(),
+				},
+			},
+		},
+		{
+			name:    "unicode_in_tool_name",
+			content: `{"name":"获取天气","arguments":{"城市":"北京"}}`,
+			expected: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name: "获取天气",
+					Arguments: testArgs(map[string]any{
+						"城市": "北京",
+					}),
+				},
+			},
+		},
+		{
+			name:    "numeric_arguments",
+			content: `{"name":"calculate","arguments":{"x":3.14,"y":42,"enabled":true}}`,
+			expected: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name: "calculate",
+					Arguments: testArgs(map[string]any{
+						"x":       3.14,
+						"y":       float64(42),
+						"enabled": true,
+					}),
+				},
+			},
+		},
+		{
+			name:        "invalid_json",
+			content:     `{invalid json}`,
+			expectError: true,
+		},
+		{
+			name:        "missing_name",
+			content:     `{"arguments":{"arg":"value"}}`,
+			expectError: true,
+		},
+		{
+			name:        "empty_name",
+			content:     `{"name":"","arguments":{"arg":"value"}}`,
+			expectError: true,
+		},
+	}
+
+	parser := &LFM2Parser{}
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			result, err := parser.parseToolCallContent(tt.content)
+
+			if tt.expectError {
+				if err == nil {
+					t.Error("Expected error but got none")
+				}
+				return
+			}
+
+			if err != nil {
+				t.Fatalf("Unexpected error: %v", err)
+			}
+
+			if diff := cmp.Diff(tt.expected, result, argsComparer); diff != "" {
+				t.Errorf("parseToolCallContent() mismatch (-want +got):\n%s", diff)
+			}
+		})
+	}
+}
+
+func TestLFM2Parser_parseToolCallsContent(t *testing.T) {
+	tests := []struct {
+		name        string
+		content     string
+		expected    []api.ToolCall
+		expectError bool
+	}{
+		{
+			name:    "multiple_python_style_calls",
+			content: `[bash(command='curl google.com'),bash(command='curl example.com')]`,
+			expected: []api.ToolCall{
+				{
+					Function: api.ToolCallFunction{
+						Name: "bash",
+						Arguments: testArgs(map[string]any{
+							"command": "curl google.com",
+						}),
+					},
+				},
+				{
+					Function: api.ToolCallFunction{
+						Name: "bash",
+						Arguments: testArgs(map[string]any{
+							"command": "curl example.com",
+						}),
+					},
+				},
+			},
+		},
+		{
+			name:    "single_python_style_call",
+			content: `bash(command='ls -la')`,
+			expected: []api.ToolCall{
+				{
+					Function: api.ToolCallFunction{
+						Name: "bash",
+						Arguments: testArgs(map[string]any{
+							"command": "ls -la",
+						}),
+					},
+				},
+			},
+		},
+		{
+			name:    "single_bracketed_call",
+			content: `[bash(command='pwd')]`,
+			expected: []api.ToolCall{
+				{
+					Function: api.ToolCallFunction{
+						Name: "bash",
+						Arguments: testArgs(map[string]any{
+							"command": "pwd",
+						}),
+					},
+				},
+			},
+		},
+		{
+			name:    "multiple_different_functions",
+			content: `[get_weather(location='Paris'),search(query='news')]`,
+			expected: []api.ToolCall{
+				{
+					Function: api.ToolCallFunction{
+						Name: "get_weather",
+						Arguments: testArgs(map[string]any{
+							"location": "Paris",
+						}),
+					},
+				},
+				{
+					Function: api.ToolCallFunction{
+						Name: "search",
+						Arguments: testArgs(map[string]any{
+							"query": "news",
+						}),
+					},
+				},
+			},
+		},
+		{
+			name:    "nested_parentheses_in_arg",
+			content: `bash(command='echo "(hello)"')`,
+			expected: []api.ToolCall{
+				{
+					Function: api.ToolCallFunction{
+						Name: "bash",
+						Arguments: testArgs(map[string]any{
+							"command": `echo "(hello)"`,
+						}),
+					},
+				},
+			},
+		},
+		{
+			name:    "comma_inside_quotes",
+			content: `bash(command='echo "hello, world"')`,
+			expected: []api.ToolCall{
+				{
+					Function: api.ToolCallFunction{
+						Name: "bash",
+						Arguments: testArgs(map[string]any{
+							"command": `echo "hello, world"`,
+						}),
+					},
+				},
+			},
+		},
+		{
+			name:    "equals_inside_quotes",
+			content: `bash(command='export FOO=bar')`,
+			expected: []api.ToolCall{
+				{
+					Function: api.ToolCallFunction{
+						Name: "bash",
+						Arguments: testArgs(map[string]any{
+							"command": `export FOO=bar`,
+						}),
+					},
+				},
+			},
+		},
+		{
+			name:    "double_quotes_with_single_inside",
+			content: `bash(command="echo 'hello'")`,
+			expected: []api.ToolCall{
+				{
+					Function: api.ToolCallFunction{
+						Name: "bash",
+						Arguments: testArgs(map[string]any{
+							"command": `echo 'hello'`,
+						}),
+					},
+				},
+			},
+		},
+		{
+			name:    "multiple_args",
+			content: `bash(command='ls', flag='-la', count=42)`,
+			expected: []api.ToolCall{
+				{
+					Function: api.ToolCallFunction{
+						Name: "bash",
+						Arguments: testArgs(map[string]any{
+							"command": "ls",
+							"flag":    "-la",
+							"count":   int64(42),
+						}),
+					},
+				},
+			},
+		},
+		{
+			name:    "no_args",
+			content: `ping()`,
+			expected: []api.ToolCall{
+				{
+					Function: api.ToolCallFunction{
+						Name:      "ping",
+						Arguments: api.NewToolCallFunctionArguments(),
+					},
+				},
+			},
+		},
+		{
+			name:    "three_calls",
+			content: `[a(x='1'),b(y='2'),c(z='3')]`,
+			expected: []api.ToolCall{
+				{
+					Function: api.ToolCallFunction{
+						Name:      "a",
+						Arguments: testArgs(map[string]any{"x": "1"}),
+					},
+				},
+				{
+					Function: api.ToolCallFunction{
+						Name:      "b",
+						Arguments: testArgs(map[string]any{"y": "2"}),
+					},
+				},
+				{
+					Function: api.ToolCallFunction{
+						Name:      "c",
+						Arguments: testArgs(map[string]any{"z": "3"}),
+					},
+				},
+			},
+		},
+		{
+			// Note: backslash escapes are preserved as-is, not processed
+			name:    "escaped_quote_in_value",
+			content: `bash(command='echo \'hello\'')`,
+			expected: []api.ToolCall{
+				{
+					Function: api.ToolCallFunction{
+						Name: "bash",
+						Arguments: testArgs(map[string]any{
+							"command": `echo \'hello\'`,
+						}),
+					},
+				},
+			},
+		},
+	}
+
+	parser := &LFM2Parser{}
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			result, err := parser.parseToolCallsContent(tt.content)
+
+			if tt.expectError {
+				if err == nil {
+					t.Error("Expected error but got none")
+				}
+				return
+			}
+
+			if err != nil {
+				t.Fatalf("Unexpected error: %v", err)
+			}
+
+			if diff := cmp.Diff(tt.expected, result, argsComparer); diff != "" {
+				t.Errorf("parseToolCallsContent() mismatch (-want +got):\n%s", diff)
+			}
+		})
+	}
+}
+
+func TestLFM2Parser_EdgeCases(t *testing.T) {
+	tests := []struct {
+		name             string
+		input            string
+		expectedContent  string
+		expectedThinking string
+		hasThinking      bool
+	}{
+		{
+			name:             "multiple_think_close_tags",
+			input:            "First thought</think>Second thought</think>Final content",
+			expectedThinking: "First thought",
+			expectedContent:  "Second thought</think>Final content",
+			hasThinking:      true,
+		},
+		{
+			name:             "empty_thinking_content",
+			input:            "</think>Just content",
+			expectedThinking: "",
+			expectedContent:  "Just content",
+			hasThinking:      true,
+		},
+		{
+			name:            "thinking_disabled_with_think_tags",
+			input:           "Some content</think>More content",
+			expectedContent: "Some content</think>More content",
+			hasThinking:     false,
+		},
+		{
+			name:            "whitespace_only_content",
+			input:           "   \n\t   ",
+			expectedContent: "   \n\t   ",
+			hasThinking:     false,
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			parser := &LFM2Parser{hasThinkingSupport: tt.hasThinking}
+			parser.Init([]api.Tool{}, nil, &api.ThinkValue{Value: tt.hasThinking})
+
+			content, thinking, _, err := parser.Add(tt.input, true)
+			if err != nil {
+				t.Fatalf("Add() error = %v", err)
+			}
+
+			if diff := cmp.Diff(tt.expectedContent, content); diff != "" {
+				t.Errorf("Content mismatch (-want +got):\n%s", diff)
+			}
+
+			if diff := cmp.Diff(tt.expectedThinking, thinking); diff != "" {
+				t.Errorf("Thinking mismatch (-want +got):\n%s", diff)
+			}
+		})
+	}
+}

model/parsers/nemotron3nano.go

@@ -1,7 +1,6 @@
 package parsers
 
 import (
-	"regexp"
 	"strings"
 	"unicode"
 
@@ -14,243 +13,114 @@ const (
 	Nemotron3NanoCollectingThinking Nemotron3NanoParserState = iota
 	Nemotron3NanoSkipWhitespaceAfterThinking
 	Nemotron3NanoCollectingContent
-	Nemotron3NanoCollectingToolCalls
 )
 
 const (
-	nemotronThinkClose    = "</think>"
-	nemotronToolCallOpen  = "<tool_call>"
-	nemotronToolCallClose = "</tool_call>"
+	nemotronThinkClose   = "</think>"
+	nemotronToolCallOpen = "<tool_call>"
 )
 
 type Nemotron3NanoParser struct {
-	state  Nemotron3NanoParserState
-	buffer strings.Builder
-	tools  []api.Tool
+	state      Nemotron3NanoParserState
+	buffer     strings.Builder
+	toolParser *Qwen3CoderParser
 }
 
 func (p *Nemotron3NanoParser) HasToolSupport() bool     { return true }
 func (p *Nemotron3NanoParser) HasThinkingSupport() bool { return true }
 
 func (p *Nemotron3NanoParser) Init(tools []api.Tool, lastMessage *api.Message, thinkValue *api.ThinkValue) []api.Tool {
-	p.tools = tools
+	p.toolParser = &Qwen3CoderParser{}
+	p.toolParser.Init(tools, nil, nil)
 
-	// thinking is enabled if user requests it
 	thinkingEnabled := thinkValue != nil && thinkValue.Bool()
-
 	prefill := lastMessage != nil && lastMessage.Role == "assistant"
 
-	if !thinkingEnabled {
+	if !thinkingEnabled || (prefill && lastMessage.Content != "") {
 		p.state = Nemotron3NanoCollectingContent
-		return tools
+	} else {
+		p.state = Nemotron3NanoCollectingThinking
 	}
 
-	if prefill && lastMessage.Content != "" {
-		p.state = Nemotron3NanoCollectingContent
-		return tools
-	}
-
-	p.state = Nemotron3NanoCollectingThinking
 	return tools
 }
 
-type nemotronEvent interface {
-	isNemotronEvent()
-}
-
-type nemotronEventThinkingContent struct {
-	content string
-}
-
-type nemotronEventContent struct {
-	content string
-}
-
-type nemotronEventToolCall struct {
-	toolCall api.ToolCall
-}
-
-func (nemotronEventThinkingContent) isNemotronEvent() {}
-func (nemotronEventContent) isNemotronEvent()         {}
-func (nemotronEventToolCall) isNemotronEvent()        {}
-
 func (p *Nemotron3NanoParser) Add(s string, done bool) (content string, thinking string, calls []api.ToolCall, err error) {
-	p.buffer.WriteString(s)
-	events := p.parseEvents()
-
-	var toolCalls []api.ToolCall
-	var contentSb strings.Builder
-	var thinkingSb strings.Builder
-	for _, event := range events {
-		switch event := event.(type) {
-		case nemotronEventToolCall:
-			toolCalls = append(toolCalls, event.toolCall)
-		case nemotronEventThinkingContent:
-			thinkingSb.WriteString(event.content)
-		case nemotronEventContent:
-			contentSb.WriteString(event.content)
-		}
+	if p.state == Nemotron3NanoCollectingContent {
+		return p.toolParser.Add(s, done)
 	}
 
-	return contentSb.String(), thinkingSb.String(), toolCalls, nil
-}
-
-func (p *Nemotron3NanoParser) parseEvents() []nemotronEvent {
-	var all []nemotronEvent
-
-	keepLooping := true
-	for keepLooping {
-		var events []nemotronEvent
-		events, keepLooping = p.eat()
-		if len(events) > 0 {
-			all = append(all, events...)
-		}
-	}
-
-	return all
-}
-
-// emitWithPartialCheck extracts unambiguous content before a potential partial tag
-func (p *Nemotron3NanoParser) emitWithPartialCheck(bufStr, tag string) (unambiguous, ambiguous string) {
-	if overlapLen := overlap(bufStr, tag); overlapLen > 0 {
-		beforePartialTag := bufStr[:len(bufStr)-overlapLen]
-		trailingLen := trailingWhitespaceLen(beforePartialTag)
-		return bufStr[:len(beforePartialTag)-trailingLen], bufStr[len(beforePartialTag)-trailingLen:]
-	}
-	wsLen := trailingWhitespaceLen(bufStr)
-	return bufStr[:len(bufStr)-wsLen], bufStr[len(bufStr)-wsLen:]
-}
-
-func (p *Nemotron3NanoParser) eat() ([]nemotronEvent, bool) {
-	bufStr := p.buffer.String()
-	if bufStr == "" {
-		return nil, false
-	}
-
-	switch p.state {
-	case Nemotron3NanoCollectingThinking:
-		if strings.Contains(bufStr, nemotronThinkClose) {
-			split := strings.SplitN(bufStr, nemotronThinkClose, 2)
-			thinking := strings.TrimRightFunc(split[0], unicode.IsSpace)
-			p.buffer.Reset()
-			remainder := strings.TrimLeftFunc(split[1], unicode.IsSpace)
-			p.buffer.WriteString(remainder)
-			// Transition to whitespace-skipping state if buffer is empty,
-			// otherwise go directly to content collection
-			if remainder == "" {
-				p.state = Nemotron3NanoSkipWhitespaceAfterThinking
-			} else {
-				p.state = Nemotron3NanoCollectingContent
-			}
-			if thinking != "" {
-				return []nemotronEvent{nemotronEventThinkingContent{content: thinking}}, true
-			}
-			return nil, true
-		}
-		unambig, ambig := p.emitWithPartialCheck(bufStr, nemotronThinkClose)
-		p.buffer.Reset()
-		p.buffer.WriteString(ambig)
-		if unambig != "" {
-			return []nemotronEvent{nemotronEventThinkingContent{content: unambig}}, false
-		}
-		return nil, false
-
-	// We only want to skip whitespace between thinking and content
-	case Nemotron3NanoSkipWhitespaceAfterThinking:
-		bufStr = strings.TrimLeftFunc(bufStr, unicode.IsSpace)
-		p.buffer.Reset()
-		p.buffer.WriteString(bufStr)
-		if bufStr == "" {
-			return nil, false
+	if p.state == Nemotron3NanoSkipWhitespaceAfterThinking {
+		s = strings.TrimLeftFunc(s, unicode.IsSpace)
+		if s == "" {
+			return "", "", nil, nil
 		}
 		p.state = Nemotron3NanoCollectingContent
-		return nil, true
+		return p.toolParser.Add(s, done)
+	}
 
-	case Nemotron3NanoCollectingContent:
-		if strings.Contains(bufStr, nemotronToolCallOpen) {
-			split := strings.SplitN(bufStr, nemotronToolCallOpen, 2)
-			content := strings.TrimRightFunc(split[0], unicode.IsSpace)
-			p.buffer.Reset()
-			p.buffer.WriteString(split[1])
-			p.state = Nemotron3NanoCollectingToolCalls
-			if content != "" {
-				return []nemotronEvent{nemotronEventContent{content: content}}, true
-			}
-			return nil, true
-		}
-		unambig, ambig := p.emitWithPartialCheck(bufStr, nemotronToolCallOpen)
+	// Nemotron3NanoCollectingThinking - buffer and look for end markers
+	p.buffer.WriteString(s)
+	bufStr := p.buffer.String()
+
+	// Look for end of thinking: </think> or <tool_call> (model may skip </think>)
+	thinkIdx := strings.Index(bufStr, nemotronThinkClose)
+	toolIdx := strings.Index(bufStr, nemotronToolCallOpen)
+
+	var endIdx int = -1
+	var remainder string
+
+	if thinkIdx != -1 && (toolIdx == -1 || thinkIdx < toolIdx) {
+		endIdx = thinkIdx
+		remainder = strings.TrimLeftFunc(bufStr[thinkIdx+len(nemotronThinkClose):], unicode.IsSpace)
+	} else if toolIdx != -1 {
+		endIdx = toolIdx
+		remainder = bufStr[toolIdx:] // Include <tool_call> tag
+	}
+
+	if endIdx != -1 {
+		thinking = strings.TrimRightFunc(bufStr[:endIdx], unicode.IsSpace)
 		p.buffer.Reset()
-		p.buffer.WriteString(ambig)
-		if unambig != "" {
-			return []nemotronEvent{nemotronEventContent{content: unambig}}, false
+
+		if remainder == "" {
+			p.state = Nemotron3NanoSkipWhitespaceAfterThinking
+		} else {
+			p.state = Nemotron3NanoCollectingContent
+			content, _, calls, err = p.toolParser.Add(remainder, done)
 		}
-		return nil, false
-
-	case Nemotron3NanoCollectingToolCalls:
-		if strings.Contains(bufStr, nemotronToolCallClose) {
-			split := strings.SplitN(bufStr, nemotronToolCallClose, 2)
-			remaining := strings.TrimLeftFunc(split[1], unicode.IsSpace)
-			p.buffer.Reset()
-			p.buffer.WriteString(remaining)
-
-			var events []nemotronEvent
-			if tc, err := p.parseToolCall(split[0]); err == nil {
-				events = append(events, nemotronEventToolCall{toolCall: tc})
-			}
-
-			if !strings.Contains(remaining, nemotronToolCallOpen) {
-				p.state = Nemotron3NanoCollectingContent
-			}
-			return events, true
-		}
-		return nil, false
+		return content, thinking, calls, err
 	}
 
-	return nil, false
+	// No end marker - emit unambiguous thinking
+	thinking = p.emitThinking(bufStr)
+	return "", thinking, nil, nil
 }
 
-var (
-	nemotronFunctionRegex  = regexp.MustCompile(`<function=([^>]+)>`)
-	nemotronParameterRegex = regexp.MustCompile(`<parameter=([^>]+)>\n?([\s\S]*?)\n?</parameter>`)
-)
+// emitThinking returns unambiguous thinking content, keeping potential partial tags in buffer
+func (p *Nemotron3NanoParser) emitThinking(bufStr string) string {
+	// Check for partial </think> or <tool_call> at end
+	thinkOverlap := overlap(bufStr, nemotronThinkClose)
+	toolOverlap := overlap(bufStr, nemotronToolCallOpen)
+	maxOverlap := max(thinkOverlap, toolOverlap)
 
-func (p *Nemotron3NanoParser) parseToolCall(content string) (api.ToolCall, error) {
-	toolCall := api.ToolCall{}
-
-	// Extract function name
-	fnMatch := nemotronFunctionRegex.FindStringSubmatch(content)
-	if len(fnMatch) < 2 {
-		return toolCall, nil
-	}
-	toolCall.Function.Name = fnMatch[1]
-
-	// Extract parameters
-	toolCall.Function.Arguments = api.NewToolCallFunctionArguments()
-	paramMatches := nemotronParameterRegex.FindAllStringSubmatch(content, -1)
-	for _, match := range paramMatches {
-		if len(match) >= 3 {
-			paramName := match[1]
-			paramValue := strings.TrimSpace(match[2])
-
-			// Try to parse as typed value based on tool definition
-			toolCall.Function.Arguments.Set(paramName, p.parseParamValue(paramName, paramValue))
-		}
+	if maxOverlap > 0 {
+		unambiguous := bufStr[:len(bufStr)-maxOverlap]
+		unambiguous = strings.TrimRightFunc(unambiguous, unicode.IsSpace)
+		p.buffer.Reset()
+		p.buffer.WriteString(bufStr[len(bufStr)-maxOverlap:])
+		return unambiguous
 	}
 
-	return toolCall, nil
-}
-
-func (p *Nemotron3NanoParser) parseParamValue(paramName string, raw string) any {
-	// Find the matching tool to get parameter type
-	var paramType api.PropertyType
-	for _, tool := range p.tools {
-		if tool.Function.Parameters.Properties != nil {
-			if prop, ok := tool.Function.Parameters.Properties.Get(paramName); ok {
-				paramType = prop.Type
-				break
-			}
-		}
-	}
-
-	return parseValue(raw, paramType)
+	// No partial tags - emit all but trailing whitespace
+	wsLen := trailingWhitespaceLen(bufStr)
+	if wsLen > 0 {
+		unambiguous := bufStr[:len(bufStr)-wsLen]
+		p.buffer.Reset()
+		p.buffer.WriteString(bufStr[len(bufStr)-wsLen:])
+		return unambiguous
+	}
+
+	// Nothing to hold back
+	p.buffer.Reset()
+	return bufStr
 }

model/parsers/nemotron3nano_test.go

@@ -8,6 +8,8 @@ import (
 	"github.com/ollama/ollama/api"
 )
 
+// TestNemotron3NanoParser tests Nemotron-specific behavior (thinking support).
+// Tool call parsing is tested in qwen3coder_test.go since Nemotron delegates to Qwen3CoderParser.
 func TestNemotron3NanoParser(t *testing.T) {
 	tests := []struct {
 		name             string
@@ -17,18 +19,6 @@ func TestNemotron3NanoParser(t *testing.T) {
 		expectedThinking string
 		expectedCalls    []api.ToolCall
 	}{
-		{
-			name:            "simple content - no thinking",
-			input:           "Hello, how can I help you?",
-			thinkValue:      nil,
-			expectedContent: "Hello, how can I help you?",
-		},
-		{
-			name:            "simple content - thinking disabled",
-			input:           "Hello, how can I help you?",
-			thinkValue:      &api.ThinkValue{Value: false},
-			expectedContent: "Hello, how can I help you?",
-		},
 		{
 			name:             "thinking then content",
 			input:            "Let me think about this...</think>\nHere is my answer.",
@@ -43,69 +33,6 @@ func TestNemotron3NanoParser(t *testing.T) {
 			expectedThinking: "Step 1: Analyze\nStep 2: Process\nStep 3: Conclude",
 			expectedContent:  "The answer is 42.",
 		},
-		{
-			name:       "simple tool call",
-			input:      "<tool_call>\n<function=get_weather>\n<parameter=city>\nParis\n</parameter>\n</function>\n</tool_call>",
-			thinkValue: nil,
-			expectedCalls: []api.ToolCall{
-				{
-					Function: api.ToolCallFunction{
-						Name:      "get_weather",
-						Arguments: testArgs(map[string]any{"city": "Paris"}),
-					},
-				},
-			},
-		},
-		{
-			name:            "content then tool call",
-			input:           "Let me check the weather.\n<tool_call>\n<function=get_weather>\n<parameter=city>\nNYC\n</parameter>\n</function>\n</tool_call>",
-			thinkValue:      nil,
-			expectedContent: "Let me check the weather.",
-			expectedCalls: []api.ToolCall{
-				{
-					Function: api.ToolCallFunction{
-						Name:      "get_weather",
-						Arguments: testArgs(map[string]any{"city": "NYC"}),
-					},
-				},
-			},
-		},
-		{
-			name:       "tool call with multiple parameters",
-			input:      "<tool_call>\n<function=book_flight>\n<parameter=from>\nSFO\n</parameter>\n<parameter=to>\nNYC\n</parameter>\n</function>\n</tool_call>",
-			thinkValue: nil,
-			expectedCalls: []api.ToolCall{
-				{
-					Function: api.ToolCallFunction{
-						Name: "book_flight",
-						Arguments: testArgs(map[string]any{
-							"from": "SFO",
-							"to":   "NYC",
-						}),
-					},
-				},
-			},
-		},
-		{
-			name: "multiple tool calls",
-			input: "<tool_call>\n<function=get_weather>\n<parameter=city>\nSan Francisco\n</parameter>\n</function>\n</tool_call>\n" +
-				"<tool_call>\n<function=get_weather>\n<parameter=city>\nNew York\n</parameter>\n</function>\n</tool_call>",
-			thinkValue: nil,
-			expectedCalls: []api.ToolCall{
-				{
-					Function: api.ToolCallFunction{
-						Name:      "get_weather",
-						Arguments: testArgs(map[string]any{"city": "San Francisco"}),
-					},
-				},
-				{
-					Function: api.ToolCallFunction{
-						Name:      "get_weather",
-						Arguments: testArgs(map[string]any{"city": "New York"}),
-					},
-				},
-			},
-		},
 		{
 			name:             "thinking then tool call",
 			input:            "I should check the weather...</think>\n<tool_call>\n<function=get_weather>\n<parameter=city>\nParis\n</parameter>\n</function>\n</tool_call>",
@@ -135,19 +62,6 @@ func TestNemotron3NanoParser(t *testing.T) {
 				},
 			},
 		},
-		{
-			name:       "tool call with multiline parameter value",
-			input:      "<tool_call>\n<function=create_note>\n<parameter=content>\nLine 1\nLine 2\nLine 3\n</parameter>\n</function>\n</tool_call>",
-			thinkValue: nil,
-			expectedCalls: []api.ToolCall{
-				{
-					Function: api.ToolCallFunction{
-						Name:      "create_note",
-						Arguments: testArgs(map[string]any{"content": "Line 1\nLine 2\nLine 3"}),
-					},
-				},
-			},
-		},
 		{
 			name:             "empty thinking block - immediate close",
 			input:            "</think>\nHere is my answer.",
@@ -161,18 +75,6 @@ func TestNemotron3NanoParser(t *testing.T) {
 			thinkValue:      &api.ThinkValue{Value: false},
 			expectedContent: "</think>\nSome content after spurious tag.",
 		},
-		{
-			name:          "tool call with no function name - returns empty tool call",
-			input:         "<tool_call>\n<function=>\n</function>\n</tool_call>",
-			thinkValue:    nil,
-			expectedCalls: []api.ToolCall{{Function: api.ToolCallFunction{Name: "", Arguments: api.NewToolCallFunctionArguments()}}},
-		},
-		{
-			name:            "content with newlines preserved",
-			input:           "Line 1\n\nLine 2\n\n\nLine 3",
-			thinkValue:      nil,
-			expectedContent: "Line 1\n\nLine 2\n\n\nLine 3",
-		},
 		{
 			name:             "thinking with only whitespace after close tag",
 			input:            "My thoughts...</think>   \n\t\n   Content here.",
@@ -180,25 +82,6 @@ func TestNemotron3NanoParser(t *testing.T) {
 			expectedThinking: "My thoughts...",
 			expectedContent:  "Content here.",
 		},
-		{
-			name:            "unicode content",
-			input:           "Hello 世界! 🌍 Ñoño",
-			thinkValue:      nil,
-			expectedContent: "Hello 世界! 🌍 Ñoño",
-		},
-		{
-			name:       "tool call with numeric parameter",
-			input:      "<tool_call>\n<function=set_temp>\n<parameter=value>\n42\n</parameter>\n</function>\n</tool_call>",
-			thinkValue: nil,
-			expectedCalls: []api.ToolCall{
-				{
-					Function: api.ToolCallFunction{
-						Name:      "set_temp",
-						Arguments: testArgs(map[string]any{"value": "42"}),
-					},
-				},
-			},
-		},
 	}
 
 	for _, tt := range tests {
@@ -233,6 +116,8 @@ func TestNemotron3NanoParser(t *testing.T) {
 	}
 }
 
+// TestNemotron3NanoParser_Streaming tests streaming behavior for thinking support.
+// Tool call streaming is tested in qwen3coder_test.go.
 func TestNemotron3NanoParser_Streaming(t *testing.T) {
 	tests := []struct {
 		name             string
@@ -242,18 +127,6 @@ func TestNemotron3NanoParser_Streaming(t *testing.T) {
 		expectedThinking string
 		expectedCalls    []api.ToolCall
 	}{
-		{
-			name:            "streaming content character by character",
-			chunks:          []string{"H", "e", "l", "l", "o", ",", " ", "w", "o", "r", "l", "d", "!"},
-			thinkValue:      nil,
-			expectedContent: "Hello, world!",
-		},
-		{
-			name:            "streaming content small tokens",
-			chunks:          []string{"Hel", "lo", ", ", "how ", "can", " I", " help", " you", " today", "?"},
-			thinkValue:      nil,
-			expectedContent: "Hello, how can I help you today?",
-		},
 		{
 			name:             "streaming thinking then content - granular",
 			chunks:           []string{"Let", " me", " th", "ink", " about", " this", "...", "<", "/", "think", ">", "\n", "Here", " is", " my", " answer", "."},
@@ -268,45 +141,6 @@ func TestNemotron3NanoParser_Streaming(t *testing.T) {
 			expectedThinking: "Step 1: Analyze\nStep 2: Process",
 			expectedContent:  "The answer.",
 		},
-		{
-			name:       "streaming tool call - highly granular",
-			chunks:     []string{"<", "tool", "_", "call", ">", "\n", "<", "func", "tion", "=", "get", "_", "weather", ">", "\n", "<", "param", "eter", "=", "city", ">", "\n", "Par", "is", "\n", "</", "param", "eter", ">", "\n", "</", "func", "tion", ">", "\n", "</", "tool", "_", "call", ">"},
-			thinkValue: nil,
-			expectedCalls: []api.ToolCall{
-				{
-					Function: api.ToolCallFunction{
-						Name:      "get_weather",
-						Arguments: testArgs(map[string]any{"city": "Paris"}),
-					},
-				},
-			},
-		},
-		{
-			name:            "streaming content then tool call - granular",
-			chunks:          []string{"Let", " me", " check", " the", " weather", ".", "\n<", "tool_call", ">", "\n", "<function=", "get_weather", ">", "\n", "<parameter=", "city", ">", "\n", "NYC", "\n", "</parameter>", "\n", "</function>", "\n", "</tool_call>"},
-			thinkValue:      nil,
-			expectedContent: "Let me check the weather.",
-			expectedCalls: []api.ToolCall{
-				{
-					Function: api.ToolCallFunction{
-						Name:      "get_weather",
-						Arguments: testArgs(map[string]any{"city": "NYC"}),
-					},
-				},
-			},
-		},
-		{
-			name:   "tool call tag split character by character",
-			chunks: []string{"<", "t", "o", "o", "l", "_", "c", "a", "l", "l", ">", "\n", "<", "f", "u", "n", "c", "t", "i", "o", "n", "=", "t", "e", "s", "t", ">", "\n", "<", "/", "f", "u", "n", "c", "t", "i", "o", "n", ">", "\n", "<", "/", "t", "o", "o", "l", "_", "c", "a", "l", "l", ">"},
-			expectedCalls: []api.ToolCall{
-				{
-					Function: api.ToolCallFunction{
-						Name:      "test",
-						Arguments: api.NewToolCallFunctionArguments(),
-					},
-				},
-			},
-		},
 		{
 			name:             "thinking close tag split character by character",
 			chunks:           []string{"I", "'", "m", " ", "t", "h", "i", "n", "k", "i", "n", "g", ".", ".", ".", "<", "/", "t", "h", "i", "n", "k", ">", "\n", "D", "o", "n", "e", "!"},
@@ -321,22 +155,6 @@ func TestNemotron3NanoParser_Streaming(t *testing.T) {
 			expectedThinking: "Thinking...",
 			expectedContent:  "Content here.",
 		},
-		{
-			name:       "tool call with multiple parameters - streaming",
-			chunks:     []string{"<tool_", "call>\n", "<function", "=book_", "flight>", "\n<para", "meter=", "from>\n", "SFO\n", "</param", "eter>", "\n<param", "eter=to", ">\nNYC", "\n</para", "meter>", "\n</func", "tion>\n", "</tool_", "call>"},
-			thinkValue: nil,
-			expectedCalls: []api.ToolCall{
-				{
-					Function: api.ToolCallFunction{
-						Name: "book_flight",
-						Arguments: testArgs(map[string]any{
-							"from": "SFO",
-							"to":   "NYC",
-						}),
-					},
-				},
-			},
-		},
 		{
 			name:             "thinking then content then tool call - streaming",
 			chunks:           []string{"Ana", "lyzing", " your", " request", "...", "</", "think", ">\n", "I'll", " check", " that", " for", " you", ".", "\n", "<tool", "_call", ">\n", "<function", "=search", ">\n", "<parameter", "=query", ">\n", "test", " query", "\n</", "parameter", ">\n", "</function", ">\n", "</tool", "_call", ">"},
@@ -352,45 +170,6 @@ func TestNemotron3NanoParser_Streaming(t *testing.T) {
 				},
 			},
 		},
-		{
-			name: "multiple tool calls - streaming",
-			chunks: []string{
-				"<tool_call>", "\n", "<function=", "get_weather>", "\n",
-				"<parameter=", "city>\n", "San Fran", "cisco\n", "</parameter>", "\n",
-				"</function>", "\n", "</tool_call>", "\n",
-				"<tool_", "call>\n", "<function", "=get_weather", ">\n",
-				"<param", "eter=city", ">\nNew", " York\n", "</parameter>\n",
-				"</function>\n", "</tool_call>",
-			},
-			thinkValue: nil,
-			expectedCalls: []api.ToolCall{
-				{
-					Function: api.ToolCallFunction{
-						Name:      "get_weather",
-						Arguments: testArgs(map[string]any{"city": "San Francisco"}),
-					},
-				},
-				{
-					Function: api.ToolCallFunction{
-						Name:      "get_weather",
-						Arguments: testArgs(map[string]any{"city": "New York"}),
-					},
-				},
-			},
-		},
-		{
-			name:       "tool call with multiline parameter - streaming",
-			chunks:     []string{"<tool_call>\n", "<function=", "create_note>\n", "<parameter=", "content>\n", "Line 1", "\nLine", " 2\n", "Line 3", "\n</parameter>\n", "</function>\n", "</tool_call>"},
-			thinkValue: nil,
-			expectedCalls: []api.ToolCall{
-				{
-					Function: api.ToolCallFunction{
-						Name:      "create_note",
-						Arguments: testArgs(map[string]any{"content": "Line 1\nLine 2\nLine 3"}),
-					},
-				},
-			},
-		},
 		{
 			name:             "empty thinking block",
 			chunks:           []string{"</think>", "\n", "Just content."},
@@ -398,12 +177,6 @@ func TestNemotron3NanoParser_Streaming(t *testing.T) {
 			expectedThinking: "",
 			expectedContent:  "Just content.",
 		},
-		{
-			name:            "empty input chunks interspersed",
-			chunks:          []string{"Hello", "", " ", "", "world", "", "!"},
-			thinkValue:      nil,
-			expectedContent: "Hello world!",
-		},
 		{
 			name:             "tool call immediately after think close - no content",
 			chunks:           []string{"Analyzing...", "</think>", "\n", "<tool_call>", "\n<function=test>\n</function>\n", "</tool_call>"},
@@ -418,25 +191,6 @@ func TestNemotron3NanoParser_Streaming(t *testing.T) {
 				},
 			},
 		},
-		{
-			name:       "tool call with empty parameter value",
-			chunks:     []string{"<tool_call>\n<function=test>\n<parameter=name>\n", "\n</parameter>\n</function>\n</tool_call>"},
-			thinkValue: nil,
-			expectedCalls: []api.ToolCall{
-				{
-					Function: api.ToolCallFunction{
-						Name:      "test",
-						Arguments: testArgs(map[string]any{"name": ""}),
-					},
-				},
-			},
-		},
-		{
-			name:            "partial tool call tag at end - buffered",
-			chunks:          []string{"Here's some content", "<tool"},
-			thinkValue:      nil,
-			expectedContent: "Here's some content",
-		},
 	}
 
 	for _, tt := range tests {
@@ -572,3 +326,65 @@ func TestNemotron3NanoParser_WithTools(t *testing.T) {
 		t.Errorf("calls mismatch (-got +want):\n%s", diff)
 	}
 }
+
+// TestNemotron3NanoParser_ToolCallWithoutThinkClose tests the case where thinking is enabled
+// but the model outputs content + tool call WITHOUT the </think> tag.
+// The parser should still parse the tool call (content before is treated as thinking).
+func TestNemotron3NanoParser_ToolCallWithoutThinkClose(t *testing.T) {
+	chunks := []string{
+		"Let", " me", " analyze", " this", ".", "\n",
+		"<tool_call>", "\n",
+		"<function=get_weather>", "\n",
+		"<parameter=city>", "Paris", "</parameter>", "\n",
+		"</function>", "\n",
+		"</tool_call>",
+	}
+
+	p := &Nemotron3NanoParser{}
+	p.Init(nil, nil, &api.ThinkValue{Value: true}) // thinking ENABLED but model doesn't output </think>
+
+	var allContent string
+	var allThinking string
+	var allCalls []api.ToolCall
+
+	for _, chunk := range chunks {
+		content, thinking, calls, err := p.Add(chunk, false)
+		if err != nil {
+			t.Fatalf("unexpected error: %v", err)
+		}
+		allContent += content
+		allThinking += thinking
+		allCalls = append(allCalls, calls...)
+	}
+
+	// Drain
+	content, thinking, calls, err := p.Add("", true)
+	if err != nil {
+		t.Fatalf("unexpected error on done: %v", err)
+	}
+	allContent += content
+	allThinking += thinking
+	allCalls = append(allCalls, calls...)
+
+	// The parser was in thinking mode, so text before <tool_call> is emitted as thinking.
+	expectedThinking := "Let me analyze this."
+
+	expectedCalls := []api.ToolCall{
+		{
+			Function: api.ToolCallFunction{
+				Name:      "get_weather",
+				Arguments: testArgs(map[string]any{"city": "Paris"}),
+			},
+		},
+	}
+
+	if allContent != "" {
+		t.Errorf("expected no content (text was streamed as thinking), got: %q", allContent)
+	}
+	if diff := cmp.Diff(allThinking, expectedThinking); diff != "" {
+		t.Errorf("thinking mismatch (-got +want):\n%s", diff)
+	}
+	if diff := cmp.Diff(allCalls, expectedCalls, argsComparer); diff != "" {
+		t.Errorf("calls mismatch (-got +want):\n%s", diff)
+	}
+}

model/parsers/parsers.go

@@ -68,6 +68,12 @@ func ParserForName(name string) Parser {
 		return &Nemotron3NanoParser{}
 	case "functiongemma":
 		return &FunctionGemmaParser{}
+	case "glm-4.7":
+		return &GLM47Parser{}
+	case "lfm2":
+		return &LFM2Parser{hasThinkingSupport: false}
+	case "lfm2-thinking":
+		return &LFM2Parser{hasThinkingSupport: true}
 	default:
 		return nil
 	}

model/parsers/qwen3coder_test.go

@@ -91,6 +91,37 @@ func TestQwenParserStreaming(t *testing.T) {
 				},
 			},
 		},
+		{
+			desc: "tool call tags split character by character",
+			steps: []step{
+				{input: "<", wantEvents: []qwenEvent{}},
+				{input: "t", wantEvents: []qwenEvent{}},
+				{input: "o", wantEvents: []qwenEvent{}},
+				{input: "o", wantEvents: []qwenEvent{}},
+				{input: "l", wantEvents: []qwenEvent{}},
+				{input: "_", wantEvents: []qwenEvent{}},
+				{input: "c", wantEvents: []qwenEvent{}},
+				{input: "a", wantEvents: []qwenEvent{}},
+				{input: "l", wantEvents: []qwenEvent{}},
+				{input: "l", wantEvents: []qwenEvent{}},
+				{input: ">", wantEvents: []qwenEvent{}},
+				{input: "a", wantEvents: []qwenEvent{}},
+				{input: "b", wantEvents: []qwenEvent{}},
+				{input: "c", wantEvents: []qwenEvent{}},
+				{input: "<", wantEvents: []qwenEvent{}},
+				{input: "/", wantEvents: []qwenEvent{}},
+				{input: "t", wantEvents: []qwenEvent{}},
+				{input: "o", wantEvents: []qwenEvent{}},
+				{input: "o", wantEvents: []qwenEvent{}},
+				{input: "l", wantEvents: []qwenEvent{}},
+				{input: "_", wantEvents: []qwenEvent{}},
+				{input: "c", wantEvents: []qwenEvent{}},
+				{input: "a", wantEvents: []qwenEvent{}},
+				{input: "l", wantEvents: []qwenEvent{}},
+				{input: "l", wantEvents: []qwenEvent{}},
+				{input: ">", wantEvents: []qwenEvent{qwenEventRawToolCall{raw: "abc"}}},
+			},
+		},
 		{
 			desc: "trailing whitespace between content and tool call",
 			steps: []step{

model/parsers/testhelpers_test.go

@@ -96,3 +96,11 @@ func testArgs(m map[string]any) api.ToolCallFunctionArguments {
 	}
 	return args
 }
+
+func args(s string) api.ToolCallFunctionArguments {
+	var result api.ToolCallFunctionArguments
+	if err := json.Unmarshal([]byte(s), &result); err != nil {
+		panic("invalid JSON in args(): " + err.Error())
+	}
+	return result
+}

model/renderers/glm46.go

@@ -0,0 +1,110 @@
+package renderers
+
+import (
+	"encoding/json"
+	"fmt"
+	"strings"
+
+	"github.com/ollama/ollama/api"
+)
+
+type GLM46Renderer struct{}
+
+func (r *GLM46Renderer) Render(messages []api.Message, tools []api.Tool, thinkValue *api.ThinkValue) (string, error) {
+	var sb strings.Builder
+
+	sb.WriteString("[gMASK]<sop>")
+
+	var lastUserIndex int
+	for i, message := range messages {
+		if message.Role == "user" {
+			lastUserIndex = i
+		}
+	}
+
+	if len(tools) > 0 {
+		sb.WriteString("<|system|>\n")
+		sb.WriteString("# Tools\n\n")
+		sb.WriteString("You may call one or more functions to assist with the user query.\n\n")
+		sb.WriteString("You are provided with function signatures within <tools></tools> XML tags:\n")
+		sb.WriteString("<tools>\n")
+		for _, tool := range tools {
+			d, _ := json.Marshal(tool)
+			sb.WriteString(string(d) + "\n")
+		}
+		sb.WriteString("</tools>\n\n")
+		sb.WriteString("For each function call, output the function name and arguments within the following XML format:\n")
+		sb.WriteString("<tool_call>{function-name}\n")
+		sb.WriteString("<arg_key>{arg-key-1}</arg_key>\n")
+		sb.WriteString("<arg_value>{arg-value-1}</arg_value>\n")
+		sb.WriteString("<arg_key>{arg-key-2}</arg_key>\n")
+		sb.WriteString("<arg_value>{arg-value-2}</arg_value>\n")
+		sb.WriteString("...\n")
+		sb.WriteString("</tool_call>")
+	}
+
+	for i, message := range messages {
+		switch message.Role {
+		case "user":
+			sb.WriteString("<|user|>\n")
+			sb.WriteString(message.Content)
+			if thinkValue != nil && !thinkValue.Bool() && !strings.HasSuffix(message.Content, "/nothink") {
+				sb.WriteString("/nothink")
+			}
+		case "assistant":
+			sb.WriteString("<|assistant|>")
+			if i > lastUserIndex {
+				if message.Thinking != "" {
+					sb.WriteString("\n<think>" + message.Thinking + "</think>")
+				} else {
+					sb.WriteString("\n<think></think>")
+				}
+			}
+			if message.Content != "" {
+				sb.WriteString("\n" + message.Content)
+			}
+			if len(message.ToolCalls) > 0 {
+				for _, toolCall := range message.ToolCalls {
+					sb.WriteString("\n<tool_call>" + toolCall.Function.Name + "\n")
+					for key, value := range toolCall.Function.Arguments.All() {
+						sb.WriteString("<arg_key>" + key + "</arg_key>\n")
+
+						var valueStr string
+						if str, ok := value.(string); ok {
+							valueStr = str
+						} else {
+							jsonBytes, err := json.Marshal(value)
+							if err != nil {
+								valueStr = fmt.Sprintf("%v", value)
+							} else {
+								valueStr = string(jsonBytes)
+							}
+						}
+
+						sb.WriteString("<arg_value>" + valueStr + "</arg_value>\n")
+					}
+
+					sb.WriteString("</tool_call>")
+				}
+			}
+		case "tool":
+			if i == 0 || messages[i-1].Role != "tool" {
+				sb.WriteString("<|observation|>")
+			}
+			sb.WriteString("\n<tool_response>\n")
+			sb.WriteString(message.Content)
+			sb.WriteString("\n</tool_response>")
+		case "system":
+			sb.WriteString("<|system|>\n")
+			sb.WriteString(message.Content)
+		}
+	}
+
+	// Add generation prompt
+	sb.WriteString("<|assistant|>")
+	if thinkValue != nil && !thinkValue.Bool() {
+		sb.WriteString("\n<think></think>\n")
+	}
+
+	return sb.String(), nil
+}

model/renderers/glm46_test.go

@@ -0,0 +1,223 @@
+package renderers
+
+import (
+	"testing"
+
+	"github.com/google/go-cmp/cmp"
+	"github.com/ollama/ollama/api"
+)
+
+func TestGLM46Renderer(t *testing.T) {
+	tests := []struct {
+		name       string
+		messages   []api.Message
+		tools      []api.Tool
+		thinkValue *api.ThinkValue
+		expected   string
+		skip       string
+	}{
+		{
+			name: "basic",
+			messages: []api.Message{
+				{Role: "user", Content: "Hello, how are you?"},
+			},
+			expected: `[gMASK]<sop><|user|>
+Hello, how are you?<|assistant|>`,
+		},
+		{
+			name: "basic with system message",
+			messages: []api.Message{
+				{Role: "system", Content: "You are a helpful assistant."},
+				{Role: "user", Content: "Hello, how are you?"},
+			},
+			expected: `[gMASK]<sop><|system|>
+You are a helpful assistant.<|user|>
+Hello, how are you?<|assistant|>`,
+		},
+		{
+			name: "basic with user assistant user",
+			messages: []api.Message{
+				{Role: "user", Content: "What is the capital of France?"},
+				{Role: "assistant", Thinking: "Let me analyze the request...", Content: "The capital of France is Paris."},
+				{Role: "user", Content: "Fantastic!"},
+			},
+			expected: `[gMASK]<sop><|user|>
+What is the capital of France?<|assistant|>
+The capital of France is Paris.<|user|>
+Fantastic!<|assistant|>`,
+		},
+		{
+			skip: "tool call ordering not guaranteed yet",
+			name: "tools",
+			messages: []api.Message{
+				{Role: "system", Content: "You are a helpful assistant with access to tools."},
+				{Role: "user", Content: "What is the weather like in Tokyo?"},
+			},
+			tools: []api.Tool{
+				{
+					Type: "function",
+					Function: api.ToolFunction{
+						Name:        "get_weather",
+						Description: "Get the current weather in a given location",
+						Parameters: api.ToolFunctionParameters{
+							Type:       "object",
+							Required:   []string{"location"},
+							Properties: propsMap(`{"location": {"type": "string", "description": "The city and state, e.g. San Francisco, CA"}, "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}}`),
+						},
+					},
+				},
+			},
+			expected: `[gMASK]<sop><|system|>
+# Tools
+
+You may call one or more functions to assist with the user query.
+
+You are provided with function signatures within <tools></tools> XML tags:
+<tools>
+{"type":"function","function":{"name":"get_weather","description":"Get the current weather in a given location","parameters":{"type":"object","required":["location"],"properties":{"location":{"type":"string","description":"The city and state, e.g. San Francisco, CA"},"unit":{"type":"string","description":"","enum":["celsius","fahrenheit"]}}}}}
+</tools>
+
+For each function call, output the function name and arguments within the following XML format:
+<tool_call>{function-name}
+<arg_key>{arg-key-1}</arg_key>
+<arg_value>{arg-value-1}</arg_value>
+<arg_key>{arg-key-2}</arg_key>
+<arg_value>{arg-value-2}</arg_value>
+...
+</tool_call><|system|>
+You are a helpful assistant with access to tools.<|user|>
+What is the weather like in Tokyo?<|assistant|>`,
+		},
+		{
+			skip: "tool call ordering not guaranteed yet",
+			name: "tool calls",
+			messages: []api.Message{
+				{Role: "system", Content: "You are a helpful assistant with access to tools."},
+				{Role: "user", Content: "What is the weather like in Tokyo?"},
+				{
+					Role: "assistant",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name:      "get_weather",
+								Arguments: args(`{"location": "Tokyo, Japan", "unit": "celsius"}`),
+							},
+						},
+						{
+							Function: api.ToolCallFunction{
+								Name:      "get_weather",
+								Arguments: args(`{"location": "Japan", "unit": "fahrenheit"}`),
+							},
+						},
+					},
+				},
+				{
+					Role:     "tool",
+					Content:  "{\"temperature\": 22, \"weather\": \"partly cloudy\", \"humidity\": 65}",
+					ToolName: "get_weather",
+				},
+				{
+					Role:     "tool",
+					Content:  "{\"temperature\": 68, \"weather\": \"sunny\", \"humidity\": 75}",
+					ToolName: "get_weather",
+				},
+				{
+					Role:    "assistant",
+					Content: "The weather in Tokyo is currently partly cloudy with a temperature of 22°C and 65% humidity. It's a pleasant day with moderate temperatures.",
+				},
+			},
+			tools: []api.Tool{
+				{
+					Type: "function",
+					Function: api.ToolFunction{
+						Name:        "get_weather",
+						Description: "Get the current weather in a given location",
+						Parameters: api.ToolFunctionParameters{
+							Type:       "object",
+							Required:   []string{"location"},
+							Properties: propsMap(`{"location": {"type": "string", "description": "The city and state, e.g. San Francisco, CA"}, "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}}`),
+						},
+					},
+				},
+			},
+			expected: `[gMASK]<sop><|system|>
+# Tools
+
+You may call one or more functions to assist with the user query.
+
+You are provided with function signatures within <tools></tools> XML tags:
+<tools>
+{"type":"function","function":{"name":"get_weather","description":"Get the current weather in a given location","parameters":{"type":"object","required":["location"],"properties":{"location":{"type":"string","description":"The city and state, e.g. San Francisco, CA"},"unit":{"type":"string","description":"","enum":["celsius","fahrenheit"]}}}}}
+</tools>
+
+For each function call, output the function name and arguments within the following XML format:
+<tool_call>{function-name}
+<arg_key>{arg-key-1}</arg_key>
+<arg_value>{arg-value-1}</arg_value>
+<arg_key>{arg-key-2}</arg_key>
+<arg_value>{arg-value-2}</arg_value>
+...
+</tool_call><|system|>
+You are a helpful assistant with access to tools.<|user|>
+What is the weather like in Tokyo?<|assistant|>
+<think></think>
+<tool_call>get_weather
+<arg_key>location</arg_key>
+<arg_value>Tokyo, Japan</arg_value>
+<arg_key>unit</arg_key>
+<arg_value>celsius</arg_value>
+</tool_call>
+<tool_call>get_weather
+<arg_key>location</arg_key>
+<arg_value>Japan</arg_value>
+<arg_key>unit</arg_key>
+<arg_value>fahrenheit</arg_value>
+</tool_call><|observation|>
+<tool_response>
+{"temperature": 22, "weather": "partly cloudy", "humidity": 65}
+</tool_response>
+<tool_response>
+{"temperature": 68, "weather": "sunny", "humidity": 75}
+</tool_response><|assistant|>
+<think></think>
+The weather in Tokyo is currently partly cloudy with a temperature of 22°C and 65% humidity. It's a pleasant day with moderate temperatures.<|assistant|>`,
+		},
+		{
+			name: "think true",
+			messages: []api.Message{
+				{Role: "user", Content: "Hello, how are you?"},
+			},
+			thinkValue: &api.ThinkValue{Value: true},
+			expected: `[gMASK]<sop><|user|>
+Hello, how are you?<|assistant|>`,
+		},
+		{
+			name: "think false",
+			messages: []api.Message{
+				{Role: "user", Content: "Hello, how are you?"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected: `[gMASK]<sop><|user|>
+Hello, how are you?/nothink<|assistant|>
+<think></think>
+`,
+		},
+	}
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			if tt.skip != "" {
+				t.Skip(tt.skip)
+			}
+			renderer := &GLM46Renderer{}
+			rendered, err := renderer.Render(tt.messages, tt.tools, tt.thinkValue)
+			if err != nil {
+				t.Fatal(err)
+			}
+			if diff := cmp.Diff(rendered, tt.expected); diff != "" {
+				t.Errorf("mismatch (-got +want):\n%s", diff)
+				t.Logf("Got:\n%s", rendered)
+				t.Logf("Expected:\n%s", tt.expected)
+			}
+		})
+	}
+}

model/renderers/glm47.go

@@ -0,0 +1,170 @@
+package renderers
+
+import (
+	"encoding/json"
+	"fmt"
+	"strings"
+
+	"github.com/ollama/ollama/api"
+)
+
+// GLM47Renderer renders messages for GLM-4.7 models.
+//
+// GLM-4.7 Thinking Modes (ref: https://docs.z.ai/guides/capabilities/thinking-mode):
+//
+//  1. INTERLEAVED THINKING
+//     The model thinks between tool calls and after receiving tool results.
+//     This enables complex step-by-step reasoning: interpreting each tool output
+//     before deciding what to do next. Thinking blocks are preserved and returned
+//     with tool results to maintain reasoning continuity.
+//
+//  2. PRESERVED THINKING
+//     The model retains reasoning content from previous assistant turns in context.
+//     This preserves reasoning continuity across multi-turn conversations. The
+//     upstream API has a "clear_thinking" parameter to control this:
+//     - clear_thinking=true:  clears reasoning from previous turns (outputs </think>)
+//     - clear_thinking=false: preserves <think>...</think> blocks from previous turns
+//
+//  3. TURN-LEVEL THINKING
+//     Controls whether the model should reason on each turn. The upstream API
+//     uses "enable_thinking" parameter:
+//     - enable_thinking=true:  outputs <think> to start reasoning
+//     - enable_thinking=false: outputs </think> to skip reasoning
+//
+// OLLAMA DEFAULTS:
+//   - Thinking is ENABLED by default (thinkValue=nil or true outputs <think>)
+//   - Thinking is PRESERVED by default (reasoning content from previous turns is always
+//     included in <think>...</think> blocks, equivalent to clear_thinking=false)
+//   - Users can disable thinking per-turn via thinkValue=false
+type GLM47Renderer struct{}
+
+func (r *GLM47Renderer) Render(messages []api.Message, tools []api.Tool, thinkValue *api.ThinkValue) (string, error) {
+	var sb strings.Builder
+
+	sb.WriteString("[gMASK]<sop>")
+
+	if len(tools) > 0 {
+		sb.WriteString("<|system|>\n")
+		sb.WriteString("# Tools\n\n")
+		sb.WriteString("You may call one or more functions to assist with the user query.\n\n")
+		sb.WriteString("You are provided with function signatures within <tools></tools> XML tags:\n")
+		sb.WriteString("<tools>\n")
+		for _, tool := range tools {
+			d, _ := json.Marshal(tool)
+			sb.WriteString(formatGLM47ToolJSON(d))
+			sb.WriteString("\n")
+		}
+		sb.WriteString("</tools>\n\n")
+		sb.WriteString("For each function call, output the function name and arguments within the following XML format:\n")
+		sb.WriteString("<tool_call>{function-name}<arg_key>{arg-key-1}</arg_key><arg_value>{arg-value-1}</arg_value><arg_key>{arg-key-2}</arg_key><arg_value>{arg-value-2}</arg_value>...</tool_call>")
+	}
+
+	think := true
+	if thinkValue != nil && !thinkValue.Bool() {
+		think = false
+	}
+
+	for i, message := range messages {
+		switch message.Role {
+		case "user":
+			sb.WriteString("<|user|>")
+			sb.WriteString(message.Content)
+		case "assistant":
+			sb.WriteString("<|assistant|>")
+			if message.Thinking != "" {
+				sb.WriteString("<think>" + message.Thinking + "</think>")
+			} else {
+				sb.WriteString("</think>")
+			}
+			if message.Content != "" {
+				sb.WriteString(message.Content)
+			}
+			if len(message.ToolCalls) > 0 {
+				for _, toolCall := range message.ToolCalls {
+					sb.WriteString("<tool_call>" + toolCall.Function.Name)
+					sb.WriteString(renderGLM47ToolArguments(toolCall.Function.Arguments))
+					sb.WriteString("</tool_call>")
+				}
+			}
+		case "tool":
+			if i == 0 || messages[i-1].Role != "tool" {
+				sb.WriteString("<|observation|>")
+			}
+			sb.WriteString("<tool_response>")
+			sb.WriteString(message.Content)
+			sb.WriteString("</tool_response>")
+		case "system":
+			sb.WriteString("<|system|>")
+			sb.WriteString(message.Content)
+		}
+	}
+
+	sb.WriteString("<|assistant|>")
+	if think {
+		sb.WriteString("<think>")
+	} else {
+		sb.WriteString("</think>")
+	}
+
+	return sb.String(), nil
+}
+
+func renderGLM47ToolArguments(args api.ToolCallFunctionArguments) string {
+	var sb strings.Builder
+	for key, value := range args.All() {
+		sb.WriteString("<arg_key>" + key + "</arg_key>")
+		var valueStr string
+		if str, ok := value.(string); ok {
+			valueStr = str
+		} else {
+			jsonBytes, err := json.Marshal(value)
+			if err != nil {
+				valueStr = fmt.Sprintf("%v", value)
+			} else {
+				valueStr = string(jsonBytes)
+			}
+		}
+
+		sb.WriteString("<arg_value>" + valueStr + "</arg_value>")
+	}
+
+	return sb.String()
+}
+
+func formatGLM47ToolJSON(raw []byte) string {
+	var sb strings.Builder
+	sb.Grow(len(raw) + len(raw)/10)
+
+	inString := false
+	escaped := false
+	for i := range raw {
+		ch := raw[i]
+		sb.WriteByte(ch)
+
+		if inString {
+			if escaped {
+				escaped = false
+				continue
+			}
+			if ch == '\\' {
+				escaped = true
+				continue
+			}
+			if ch == '"' {
+				inString = false
+			}
+			continue
+		}
+
+		if ch == '"' {
+			inString = true
+			continue
+		}
+
+		if ch == ':' || ch == ',' {
+			sb.WriteByte(' ')
+		}
+	}
+
+	return sb.String()
+}

model/renderers/glm47_test.go

@@ -0,0 +1,191 @@
+package renderers
+
+import (
+	"testing"
+
+	"github.com/google/go-cmp/cmp"
+	"github.com/ollama/ollama/api"
+)
+
+func TestGLM47Renderer(t *testing.T) {
+	tests := []struct {
+		name       string
+		messages   []api.Message
+		tools      []api.Tool
+		thinkValue *api.ThinkValue
+		expected   string
+	}{
+		{
+			name: "basic user message",
+			messages: []api.Message{
+				{Role: "user", Content: "Hello"},
+			},
+			expected: "[gMASK]<sop><|user|>Hello<|assistant|><think>",
+		},
+		{
+			name: "thinking disabled",
+			messages: []api.Message{
+				{Role: "user", Content: "Hello"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "[gMASK]<sop><|user|>Hello<|assistant|></think>",
+		},
+		{
+			name: "system and user",
+			messages: []api.Message{
+				{Role: "system", Content: "You are helpful."},
+				{Role: "user", Content: "Hello"},
+			},
+			expected: "[gMASK]<sop><|system|>You are helpful.<|user|>Hello<|assistant|><think>",
+		},
+		{
+			name: "multi-turn conversation",
+			messages: []api.Message{
+				{Role: "user", Content: "Hi"},
+				{Role: "assistant", Content: "Hello there"},
+				{Role: "user", Content: "How are you?"},
+			},
+			expected: "[gMASK]<sop><|user|>Hi<|assistant|></think>Hello there<|user|>How are you?<|assistant|><think>",
+		},
+		{
+			name: "assistant with reasoning_content",
+			messages: []api.Message{
+				{Role: "user", Content: "Answer with reasoning."},
+				{Role: "assistant", Thinking: "Plan.", Content: "Done."},
+			},
+			expected: "[gMASK]<sop><|user|>Answer with reasoning.<|assistant|><think>Plan.</think>Done.<|assistant|><think>",
+		},
+		{
+			name: "tool call with empty content",
+			messages: []api.Message{
+				{Role: "user", Content: "Weather?"},
+				{
+					Role: "assistant",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name:      "get_weather",
+								Arguments: args(`{"location": "Tokyo", "unit": "celsius"}`),
+							},
+						},
+					},
+				},
+				{Role: "tool", Content: `{"temperature":22}`},
+			},
+			tools: []api.Tool{
+				{
+					Type: "function",
+					Function: api.ToolFunction{
+						Name:        "get_weather",
+						Description: "Get weather",
+						Parameters: api.ToolFunctionParameters{
+							Type:       "object",
+							Required:   []string{"location"},
+							Properties: propsMap(`{"location": {"type": "string"}}`),
+						},
+					},
+				},
+			},
+			expected: "[gMASK]<sop><|system|>\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>\n{\"type\": \"function\", \"function\": {\"name\": \"get_weather\", \"description\": \"Get weather\", \"parameters\": {\"type\": \"object\", \"required\": [\"location\"], \"properties\": {\"location\": {\"type\": \"string\"}}}}}\n</tools>\n\nFor each function call, output the function name and arguments within the following XML format:\n<tool_call>{function-name}<arg_key>{arg-key-1}</arg_key><arg_value>{arg-value-1}</arg_value><arg_key>{arg-key-2}</arg_key><arg_value>{arg-value-2}</arg_value>...</tool_call><|user|>Weather?<|assistant|></think><tool_call>get_weather<arg_key>location</arg_key><arg_value>Tokyo</arg_value><arg_key>unit</arg_key><arg_value>celsius</arg_value></tool_call><|observation|><tool_response>{\"temperature\":22}</tool_response><|assistant|><think>",
+		},
+		{
+			name: "tool call with content",
+			messages: []api.Message{
+				{Role: "user", Content: "Weather?"},
+				{
+					Role:    "assistant",
+					Content: "Let me check",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name:      "get_weather",
+								Arguments: args(`{"location": "Tokyo"}`),
+							},
+						},
+					},
+				},
+				{Role: "tool", Content: `{"temperature":22}`},
+				{Role: "assistant", Content: "It is 22C."},
+			},
+			tools: []api.Tool{
+				{
+					Type: "function",
+					Function: api.ToolFunction{
+						Name:        "get_weather",
+						Description: "Get weather",
+						Parameters: api.ToolFunctionParameters{
+							Type:       "object",
+							Required:   []string{"location"},
+							Properties: propsMap(`{"location": {"type": "string"}}`),
+						},
+					},
+				},
+			},
+			expected: "[gMASK]<sop><|system|>\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>\n{\"type\": \"function\", \"function\": {\"name\": \"get_weather\", \"description\": \"Get weather\", \"parameters\": {\"type\": \"object\", \"required\": [\"location\"], \"properties\": {\"location\": {\"type\": \"string\"}}}}}\n</tools>\n\nFor each function call, output the function name and arguments within the following XML format:\n<tool_call>{function-name}<arg_key>{arg-key-1}</arg_key><arg_value>{arg-value-1}</arg_value><arg_key>{arg-key-2}</arg_key><arg_value>{arg-value-2}</arg_value>...</tool_call><|user|>Weather?<|assistant|></think>Let me check<tool_call>get_weather<arg_key>location</arg_key><arg_value>Tokyo</arg_value></tool_call><|observation|><tool_response>{\"temperature\":22}</tool_response><|assistant|></think>It is 22C.<|assistant|><think>",
+		},
+		{
+			name: "multiple tool calls and responses",
+			messages: []api.Message{
+				{Role: "user", Content: "Compare weather"},
+				{
+					Role: "assistant",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name:      "get_weather",
+								Arguments: args(`{"location": "Tokyo"}`),
+							},
+						},
+						{
+							Function: api.ToolCallFunction{
+								Name:      "get_weather",
+								Arguments: args(`{"location": "Paris"}`),
+							},
+						},
+					},
+				},
+				{Role: "tool", Content: `{"temperature":22}`},
+				{Role: "tool", Content: `{"temperature":18}`},
+			},
+			tools: []api.Tool{
+				{
+					Type: "function",
+					Function: api.ToolFunction{
+						Name:        "get_weather",
+						Description: "Get weather",
+						Parameters: api.ToolFunctionParameters{
+							Type:       "object",
+							Required:   []string{"location"},
+							Properties: propsMap(`{"location": {"type": "string"}}`),
+						},
+					},
+				},
+			},
+			expected: "[gMASK]<sop><|system|>\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>\n{\"type\": \"function\", \"function\": {\"name\": \"get_weather\", \"description\": \"Get weather\", \"parameters\": {\"type\": \"object\", \"required\": [\"location\"], \"properties\": {\"location\": {\"type\": \"string\"}}}}}\n</tools>\n\nFor each function call, output the function name and arguments within the following XML format:\n<tool_call>{function-name}<arg_key>{arg-key-1}</arg_key><arg_value>{arg-value-1}</arg_value><arg_key>{arg-key-2}</arg_key><arg_value>{arg-value-2}</arg_value>...</tool_call><|user|>Compare weather<|assistant|></think><tool_call>get_weather<arg_key>location</arg_key><arg_value>Tokyo</arg_value></tool_call><tool_call>get_weather<arg_key>location</arg_key><arg_value>Paris</arg_value></tool_call><|observation|><tool_response>{\"temperature\":22}</tool_response><tool_response>{\"temperature\":18}</tool_response><|assistant|><think>",
+		},
+		{
+			name: "preserved thinking in multi-turn",
+			messages: []api.Message{
+				{Role: "user", Content: "Think step by step"},
+				{Role: "assistant", Thinking: "Let me think...", Content: "Here's my answer."},
+				{Role: "user", Content: "Continue"},
+			},
+			expected: "[gMASK]<sop><|user|>Think step by step<|assistant|><think>Let me think...</think>Here's my answer.<|user|>Continue<|assistant|><think>",
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			renderer := &GLM47Renderer{}
+			rendered, err := renderer.Render(tt.messages, tt.tools, tt.thinkValue)
+			if err != nil {
+				t.Fatal(err)
+			}
+			if diff := cmp.Diff(rendered, tt.expected); diff != "" {
+				t.Errorf("mismatch (-got +want):\n%s", diff)
+				t.Logf("Got:\n%s", rendered)
+				t.Logf("Expected:\n%s", tt.expected)
+			}
+		})
+	}
+}

model/renderers/lfm2.go

@@ -0,0 +1,144 @@
+package renderers
+
+import (
+	"encoding/json"
+	"strings"
+
+	"github.com/ollama/ollama/api"
+)
+
+type LFM2Renderer struct {
+	IsThinking bool
+}
+
+func (r *LFM2Renderer) Render(messages []api.Message, tools []api.Tool, thinkValue *api.ThinkValue) (string, error) {
+	var sb strings.Builder
+
+	// Note: BOS token is added by the tokenizer (add_bos_token: true), not the renderer
+
+	// Extract first system message if present (to combine with tools)
+	var firstSystemContent string
+	startIdx := 0
+	if len(messages) > 0 && messages[0].Role == "system" {
+		firstSystemContent = messages[0].Content
+		startIdx = 1
+	}
+
+	// Append tools to first system content
+	if len(tools) > 0 {
+		if firstSystemContent != "" {
+			firstSystemContent += "\n"
+		}
+		firstSystemContent += "List of tools: ["
+		for i, tool := range tools {
+			toolJSON, err := json.Marshal(tool)
+			if err != nil {
+				return "", err
+			}
+			firstSystemContent += string(toolJSON)
+			if i < len(tools)-1 {
+				firstSystemContent += ", "
+			}
+		}
+		firstSystemContent += "]"
+	}
+
+	// Output first system block if it has content
+	if firstSystemContent != "" {
+		sb.WriteString("<|im_start|>system\n")
+		sb.WriteString(firstSystemContent)
+		sb.WriteString("<|im_end|>\n")
+	}
+
+	// Find the index of the last assistant message for thinking stripping
+	lastAssistantIndex := -1
+	for i := len(messages) - 1; i >= startIdx; i-- {
+		if messages[i].Role == "assistant" {
+			lastAssistantIndex = i
+			break
+		}
+	}
+
+	// Track whether we need to add generation prompt
+	needsGenerationPrompt := len(messages) > 0
+
+	for i := startIdx; i < len(messages); i++ {
+		message := messages[i]
+		switch message.Role {
+		case "system":
+			// Additional system messages (after the first) are rendered normally
+			sb.WriteString("<|im_start|>system\n")
+			sb.WriteString(message.Content)
+			sb.WriteString("<|im_end|>\n")
+
+		case "user":
+			sb.WriteString("<|im_start|>user\n")
+			sb.WriteString(message.Content)
+			sb.WriteString("<|im_end|>\n")
+			needsGenerationPrompt = true
+
+		case "assistant":
+			sb.WriteString("<|im_start|>assistant\n")
+
+			// Check if this is the last assistant message
+			isLastAssistant := i == lastAssistantIndex
+
+			// Process content (may need thinking stripped)
+			content := message.Content
+
+			// Handle thinking tags in assistant content
+			keepPastThinking := r.IsThinking && (thinkValue != nil && thinkValue.Bool())
+			if strings.Contains(content, "</think>") {
+				parts := strings.SplitN(content, "</think>", 2)
+				if len(parts) > 1 {
+					if !isLastAssistant && !keepPastThinking {
+						// Strip thinking entirely for past assistant messages
+						content = strings.TrimSpace(parts[1])
+					} else {
+						// Preserve thinking but trim whitespace after </think>
+						content = parts[0] + "</think>" + strings.TrimLeft(parts[1], " \t\n\r")
+					}
+				}
+			}
+
+			if len(message.ToolCalls) > 0 {
+				// Assistant with tool calls - write content first (if any after stripping)
+				if content != "" {
+					sb.WriteString(content)
+				}
+
+				for _, toolCall := range message.ToolCalls {
+					sb.WriteString("<|tool_call_start|>")
+					toolCallJSON := map[string]any{
+						"name":      toolCall.Function.Name,
+						"arguments": toolCall.Function.Arguments,
+					}
+					callJSON, _ := json.Marshal(toolCallJSON)
+					sb.WriteString(string(callJSON))
+					sb.WriteString("<|tool_call_end|>")
+				}
+			} else {
+				sb.WriteString(content)
+			}
+
+			sb.WriteString("<|im_end|>\n")
+			needsGenerationPrompt = true // Always add gen prompt after assistant when add_generation_prompt=true
+
+		case "tool":
+			// Tool responses are rendered as plain messages per the chat template
+			sb.WriteString("<|im_start|>tool\n")
+			sb.WriteString(message.Content)
+			sb.WriteString("<|im_end|>\n")
+			needsGenerationPrompt = true
+		}
+	}
+
+	// Add generation prompt
+	if needsGenerationPrompt {
+		sb.WriteString("<|im_start|>assistant\n")
+		// Note: Model is a "thinking-only" model - it will output <think> itself
+		// We don't add <think> tag to the prompt
+	}
+
+	return sb.String(), nil
+}

model/renderers/lfm2_test.go

@@ -0,0 +1,427 @@
+package renderers
+
+import (
+	"testing"
+
+	"github.com/google/go-cmp/cmp"
+
+	"github.com/ollama/ollama/api"
+)
+
+func TestLFM2Renderer(t *testing.T) {
+	tests := []struct {
+		name       string
+		messages   []api.Message
+		tools      []api.Tool
+		thinkValue *api.ThinkValue
+		expected   string
+	}{
+		{
+			name: "basic user message",
+			messages: []api.Message{
+				{Role: "user", Content: "Hello!"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nHello!<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "basic with system message",
+			messages: []api.Message{
+				{Role: "system", Content: "You are a helpful assistant."},
+				{Role: "user", Content: "Hello!"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nHello!<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "multiple system messages rendered separately",
+			messages: []api.Message{
+				{Role: "system", Content: "First instruction."},
+				{Role: "system", Content: "Second instruction."},
+				{Role: "user", Content: "Hello!"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>system\nFirst instruction.<|im_end|>\n<|im_start|>system\nSecond instruction.<|im_end|>\n<|im_start|>user\nHello!<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "multi-turn conversation",
+			messages: []api.Message{
+				{Role: "user", Content: "What is 2+2?"},
+				{Role: "assistant", Content: "The answer is 4."},
+				{Role: "user", Content: "Thanks!"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nWhat is 2+2?<|im_end|>\n<|im_start|>assistant\nThe answer is 4.<|im_end|>\n<|im_start|>user\nThanks!<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "only system message",
+			messages: []api.Message{
+				{Role: "system", Content: "You are helpful."},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>system\nYou are helpful.<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			// When assistant is the LAST assistant, thinking is preserved (even with keep_past_thinking=false)
+			name: "user-assistant-user: last assistant preserves thinking",
+			messages: []api.Message{
+				{Role: "user", Content: "Q1"},
+				{Role: "assistant", Content: "<think>reasoning</think>A1"},
+				{Role: "user", Content: "Q2"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nQ1<|im_end|>\n<|im_start|>assistant\n<think>reasoning</think>A1<|im_end|>\n<|im_start|>user\nQ2<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			// With two assistants, first is stripped (not last), second preserved (is last)
+			name: "multi-turn thinking: first stripped, second preserved",
+			messages: []api.Message{
+				{Role: "user", Content: "Q1"},
+				{Role: "assistant", Content: "<think>reason1</think>A1"},
+				{Role: "user", Content: "Q2"},
+				{Role: "assistant", Content: "<think>reason2</think>A2"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nQ1<|im_end|>\n<|im_start|>assistant\nA1<|im_end|>\n<|im_start|>user\nQ2<|im_end|>\n<|im_start|>assistant\n<think>reason2</think>A2<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			// With thinking enabled (keep_past_thinking=true), both preserved
+			name: "multi-turn thinking: both preserved when thinking enabled",
+			messages: []api.Message{
+				{Role: "user", Content: "Q1"},
+				{Role: "assistant", Content: "<think>reason1</think>A1"},
+				{Role: "user", Content: "Q2"},
+				{Role: "assistant", Content: "<think>reason2</think>A2"},
+			},
+			thinkValue: &api.ThinkValue{Value: true},
+			expected:   "<|im_start|>user\nQ1<|im_end|>\n<|im_start|>assistant\n<think>reason1</think>A1<|im_end|>\n<|im_start|>user\nQ2<|im_end|>\n<|im_start|>assistant\n<think>reason2</think>A2<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "assistant with tool calls",
+			messages: []api.Message{
+				{Role: "user", Content: "What's the weather?"},
+				{
+					Role: "assistant",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name: "get_weather",
+								Arguments: testArgs(map[string]any{
+									"location": "Paris",
+								}),
+							},
+						},
+					},
+				},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   `<|im_start|>user` + "\n" + `What's the weather?<|im_end|>` + "\n" + `<|im_start|>assistant` + "\n" + `<|tool_call_start|>{"arguments":{"location":"Paris"},"name":"get_weather"}<|tool_call_end|><|im_end|>` + "\n" + `<|im_start|>assistant` + "\n",
+		},
+		{
+			name: "assistant with content and tool calls",
+			messages: []api.Message{
+				{Role: "user", Content: "What's the weather in Paris?"},
+				{
+					Role:    "assistant",
+					Content: "Let me check.",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name: "get_weather",
+								Arguments: testArgs(map[string]any{
+									"location": "Paris",
+								}),
+							},
+						},
+					},
+				},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   `<|im_start|>user` + "\n" + `What's the weather in Paris?<|im_end|>` + "\n" + `<|im_start|>assistant` + "\n" + `Let me check.<|tool_call_start|>{"arguments":{"location":"Paris"},"name":"get_weather"}<|tool_call_end|><|im_end|>` + "\n" + `<|im_start|>assistant` + "\n",
+		},
+		{
+			name: "tool response",
+			messages: []api.Message{
+				{Role: "user", Content: "What's the weather?"},
+				{Role: "assistant", Content: "Let me check."},
+				{Role: "tool", Content: "22C, Sunny"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nWhat's the weather?<|im_end|>\n<|im_start|>assistant\nLet me check.<|im_end|>\n<|im_start|>tool\n22C, Sunny<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "multiple tool calls",
+			messages: []api.Message{
+				{Role: "user", Content: "Get weather for Paris and London"},
+				{
+					Role: "assistant",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name: "get_weather",
+								Arguments: testArgs(map[string]any{
+									"location": "Paris",
+								}),
+							},
+						},
+						{
+							Function: api.ToolCallFunction{
+								Name: "get_weather",
+								Arguments: testArgs(map[string]any{
+									"location": "London",
+								}),
+							},
+						},
+					},
+				},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   `<|im_start|>user` + "\n" + `Get weather for Paris and London<|im_end|>` + "\n" + `<|im_start|>assistant` + "\n" + `<|tool_call_start|>{"arguments":{"location":"Paris"},"name":"get_weather"}<|tool_call_end|><|tool_call_start|>{"arguments":{"location":"London"},"name":"get_weather"}<|tool_call_end|><|im_end|>` + "\n" + `<|im_start|>assistant` + "\n",
+		},
+		{
+			name: "tools definitions with system message",
+			messages: []api.Message{
+				{Role: "system", Content: "You are helpful."},
+				{Role: "user", Content: "What's the weather?"},
+			},
+			tools: []api.Tool{
+				{
+					Type: "function",
+					Function: api.ToolFunction{
+						Name:        "get_weather",
+						Description: "Get current weather",
+						Parameters: api.ToolFunctionParameters{
+							Type: "object",
+							Properties: testPropsMap(map[string]api.ToolProperty{
+								"location": {
+									Type:        api.PropertyType{"string"},
+									Description: "City name",
+								},
+							}),
+							Required: []string{"location"},
+						},
+					},
+				},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   `<|im_start|>system` + "\n" + `You are helpful.` + "\n" + `List of tools: [{"type":"function","function":{"name":"get_weather","description":"Get current weather","parameters":{"type":"object","required":["location"],"properties":{"location":{"type":"string","description":"City name"}}}}}]<|im_end|>` + "\n" + `<|im_start|>user` + "\n" + `What's the weather?<|im_end|>` + "\n" + `<|im_start|>assistant` + "\n",
+		},
+		{
+			name: "tools definitions without system message",
+			messages: []api.Message{
+				{Role: "user", Content: "What's the weather?"},
+			},
+			tools: []api.Tool{
+				{
+					Type: "function",
+					Function: api.ToolFunction{
+						Name:        "get_weather",
+						Description: "Get current weather",
+						Parameters: api.ToolFunctionParameters{
+							Type: "object",
+							Properties: testPropsMap(map[string]api.ToolProperty{
+								"location": {
+									Type:        api.PropertyType{"string"},
+									Description: "City name",
+								},
+							}),
+							Required: []string{"location"},
+						},
+					},
+				},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   `<|im_start|>system` + "\n" + `List of tools: [{"type":"function","function":{"name":"get_weather","description":"Get current weather","parameters":{"type":"object","required":["location"],"properties":{"location":{"type":"string","description":"City name"}}}}}]<|im_end|>` + "\n" + `<|im_start|>user` + "\n" + `What's the weather?<|im_end|>` + "\n" + `<|im_start|>assistant` + "\n",
+		},
+		{
+			name: "multiple tools without system message",
+			messages: []api.Message{
+				{Role: "user", Content: "Hello"},
+			},
+			tools: []api.Tool{
+				{
+					Type: "function",
+					Function: api.ToolFunction{
+						Name:        "get_weather",
+						Description: "Get weather",
+					},
+				},
+				{
+					Type: "function",
+					Function: api.ToolFunction{
+						Name:        "get_time",
+						Description: "Get time",
+					},
+				},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>system\nList of tools: [{\"type\":\"function\",\"function\":{\"name\":\"get_weather\",\"description\":\"Get weather\",\"parameters\":{\"type\":\"\",\"properties\":null}}}, {\"type\":\"function\",\"function\":{\"name\":\"get_time\",\"description\":\"Get time\",\"parameters\":{\"type\":\"\",\"properties\":null}}}]<|im_end|>\n<|im_start|>user\nHello<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "user-tool sequence",
+			messages: []api.Message{
+				{Role: "user", Content: "Check weather"},
+				{Role: "tool", Content: "22C"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nCheck weather<|im_end|>\n<|im_start|>tool\n22C<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "full tool call cycle",
+			messages: []api.Message{
+				{Role: "user", Content: "Check weather"},
+				{Role: "assistant", Content: "Let me check"},
+				{Role: "tool", Content: "22C"},
+				{Role: "assistant", Content: "It's 22C"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nCheck weather<|im_end|>\n<|im_start|>assistant\nLet me check<|im_end|>\n<|im_start|>tool\n22C<|im_end|>\n<|im_start|>assistant\nIt's 22C<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "unicode content",
+			messages: []api.Message{
+				{Role: "user", Content: "你好世界! مرحبا 🌍"},
+				{Role: "assistant", Content: "Hello! 👋"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\n你好世界! مرحبا 🌍<|im_end|>\n<|im_start|>assistant\nHello! 👋<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "newlines in content",
+			messages: []api.Message{
+				{Role: "user", Content: "Line 1\nLine 2\n\nLine 4"},
+				{Role: "assistant", Content: "Response with\nmultiple\nlines"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nLine 1\nLine 2\n\nLine 4<|im_end|>\n<|im_start|>assistant\nResponse with\nmultiple\nlines<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "empty assistant content",
+			messages: []api.Message{
+				{Role: "user", Content: "Hello"},
+				{Role: "assistant", Content: ""},
+				{Role: "user", Content: "OK"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nHello<|im_end|>\n<|im_start|>assistant\n<|im_end|>\n<|im_start|>user\nOK<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			// Generation prompt does NOT include <think> - model outputs it
+			name: "generation prompt has no think tag",
+			messages: []api.Message{
+				{Role: "user", Content: "Think hard"},
+			},
+			thinkValue: &api.ThinkValue{Value: true},
+			expected:   "<|im_start|>user\nThink hard<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			// Interleaved: thinking before tool call - last assistant preserves thinking
+			name: "thinking before tool call (last assistant)",
+			messages: []api.Message{
+				{Role: "user", Content: "What's the weather?"},
+				{
+					Role:    "assistant",
+					Content: "<think>I need to check the weather</think>",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name: "get_weather",
+								Arguments: testArgs(map[string]any{
+									"location": "Paris",
+								}),
+							},
+						},
+					},
+				},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nWhat's the weather?<|im_end|>\n<|im_start|>assistant\n<think>I need to check the weather</think><|tool_call_start|>{\"arguments\":{\"location\":\"Paris\"},\"name\":\"get_weather\"}<|tool_call_end|><|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			// Two assistants with tool calls - first has thinking stripped
+			name: "two assistants with tools: first thinking stripped",
+			messages: []api.Message{
+				{Role: "user", Content: "What's the weather?"},
+				{
+					Role:    "assistant",
+					Content: "<think>checking</think>",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name: "get_weather",
+								Arguments: testArgs(map[string]any{
+									"location": "Paris",
+								}),
+							},
+						},
+					},
+				},
+				{Role: "tool", Content: "22C"},
+				{Role: "assistant", Content: "<think>got result</think>It's 22C!"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nWhat's the weather?<|im_end|>\n<|im_start|>assistant\n<|tool_call_start|>{\"arguments\":{\"location\":\"Paris\"},\"name\":\"get_weather\"}<|tool_call_end|><|im_end|>\n<|im_start|>tool\n22C<|im_end|>\n<|im_start|>assistant\n<think>got result</think>It's 22C!<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			// Two assistants with tools - both preserved when thinking enabled
+			name: "two assistants with tools: both preserved when thinking enabled",
+			messages: []api.Message{
+				{Role: "user", Content: "What's the weather?"},
+				{
+					Role:    "assistant",
+					Content: "<think>checking</think>",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name: "get_weather",
+								Arguments: testArgs(map[string]any{
+									"location": "Paris",
+								}),
+							},
+						},
+					},
+				},
+				{Role: "tool", Content: "22C"},
+				{Role: "assistant", Content: "<think>got result</think>It's 22C!"},
+			},
+			thinkValue: &api.ThinkValue{Value: true},
+			expected:   "<|im_start|>user\nWhat's the weather?<|im_end|>\n<|im_start|>assistant\n<think>checking</think><|tool_call_start|>{\"arguments\":{\"location\":\"Paris\"},\"name\":\"get_weather\"}<|tool_call_end|><|im_end|>\n<|im_start|>tool\n22C<|im_end|>\n<|im_start|>assistant\n<think>got result</think>It's 22C!<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			// Content before thinking before tool call
+			name: "content then thinking then tool call",
+			messages: []api.Message{
+				{Role: "user", Content: "What's the weather?"},
+				{
+					Role:    "assistant",
+					Content: "Let me check.<think>Using weather API</think>",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name: "get_weather",
+								Arguments: testArgs(map[string]any{
+									"location": "Paris",
+								}),
+							},
+						},
+					},
+				},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nWhat's the weather?<|im_end|>\n<|im_start|>assistant\nLet me check.<think>Using weather API</think><|tool_call_start|>{\"arguments\":{\"location\":\"Paris\"},\"name\":\"get_weather\"}<|tool_call_end|><|im_end|>\n<|im_start|>assistant\n",
+		},
+	}
+
+	renderer := &LFM2Renderer{IsThinking: true}
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			rendered, err := renderer.Render(tt.messages, tt.tools, tt.thinkValue)
+			if err != nil {
+				t.Fatalf("Render() error = %v", err)
+			}
+			if diff := cmp.Diff(tt.expected, rendered); diff != "" {
+				t.Errorf("Render() mismatch (-want +got):\n%s", diff)
+			}
+		})
+	}
+}

model/renderers/renderer.go

@@ -80,6 +80,12 @@ func rendererForName(name string) Renderer {
 		return &Nemotron3NanoRenderer{}
 	case "functiongemma":
 		return &FunctionGemmaRenderer{}
+	case "glm-4.7":
+		return &GLM47Renderer{}
+	case "lfm2":
+		return &LFM2Renderer{IsThinking: false}
+	case "lfm2-thinking":
+		return &LFM2Renderer{IsThinking: true}
 	default:
 		return nil
 	}