wip?

.github/workflows/release.yaml

@@ -432,22 +432,6 @@ jobs:
           docker buildx imagetools inspect ollama/ollama:${{ steps.metadata.outputs.version }}
         working-directory: ${{ runner.temp }}
 
-  # Trigger downstream release process
-  trigger:
-    runs-on: ubuntu-latest
-    environment: release
-    needs: [darwin-build, windows-build, windows-depends]
-    steps:
-      - name: Trigger downstream release process
-        run: |
-          curl -L \
-            -X POST \
-            -H "Accept: application/vnd.github+json" \
-            -H "Authorization: Bearer ${{ secrets.RELEASE_TOKEN }}" \
-            -H "X-GitHub-Api-Version: 2022-11-28" \
-            https://api.github.com/repos/ollama/${{ vars.RELEASE_REPO }}/dispatches \
-            -d "{\"event_type\": \"trigger-workflow\", \"client_payload\": {\"run_id\": \"${GITHUB_RUN_ID}\", \"version\": \"${GITHUB_REF_NAME#v}\"}}"
-
   # Aggregate all the assets and ship a release
   release:
     needs: [darwin-sign, windows-sign, linux-build]

.golangci.yaml

@@ -19,8 +19,8 @@ linters:
     - nolintlint
     - nosprintfhostport
     - staticcheck
+    - tenv
     - unconvert
-    - usetesting
     - wastedassign
     - whitespace
   disable:

CMakeLists.txt

@@ -51,8 +51,6 @@ include_directories(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src/include
 include_directories(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src/ggml-cpu)
 include_directories(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src/ggml-cpu/amx)
 
-add_compile_definitions(NDEBUG)
-
 set(GGML_CPU ON)
 add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src)
 set_property(TARGET ggml PROPERTY EXCLUDE_FROM_ALL TRUE)

Makefile.sync

@@ -1,6 +1,6 @@
 UPSTREAM=https://github.com/ggerganov/llama.cpp.git
 WORKDIR=llama/vendor
-FETCH_HEAD=de4c07f93783a1a96456a44dc16b9db538ee1618
+FETCH_HEAD=2016f07bd106c73699ecbaace80f55db5ed95dac
 
 .PHONY: help
 help:
@@ -15,13 +15,11 @@ help:
 	@echo "    make -f $(lastword $(MAKEFILE_LIST)) clean sync"
 
 .PHONY: sync
-sync: llama/build-info.cpp ml/backend/ggml/ggml/src/ggml-metal/ggml-metal-embed.metal
+sync: llama/build-info.cpp llama/llama.cpp ml/backend/ggml/ggml
 
-llama/build-info.cpp: llama/build-info.cpp.in llama/llama.cpp
-	sed -e 's|@FETCH_HEAD@|$(FETCH_HEAD)|' <$< >$@
-
-ml/backend/ggml/ggml/src/ggml-metal/ggml-metal-embed.metal: ml/backend/ggml/ggml
-	go generate ./$(@D)
+.PHONY: llama/build-info.cpp
+llama/build-info.cpp: llama/build-info.cpp.in
+	sed -e 's|@FETCH_HEAD@|$(FETCH_HEAD)|' $< > $@
 
 .PHONY: llama/llama.cpp
 llama/llama.cpp: llama/vendor/
@@ -32,13 +30,12 @@ ml/backend/ggml/ggml: llama/vendor/ggml/
 	rsync -arvzc -f "merge $@/.rsync-filter" $< $@
 
 PATCHES=$(wildcard llama/patches/*.patch)
-PATCHED=$(join $(dir $(PATCHES)), $(addsuffix ed, $(addprefix ., $(notdir $(PATCHES)))))
 
 .PHONY: apply-patches
 .NOTPARALLEL:
-apply-patches: $(PATCHED)
+apply-patches: $(addsuffix ed, $(PATCHES))
 
-llama/patches/.%.patched: llama/patches/%.patch
+%.patched: %.patch
 	@if git -c user.name=nobody -c 'user.email=<>' -C $(WORKDIR) am -3 $(realpath $<); then touch $@; else git -C $(WORKDIR) am --abort; exit 1; fi
 
 .PHONY: checkout
@@ -60,4 +57,4 @@ format-patches: llama/patches
 
 .PHONE: clean
 clean: checkout
-	$(RM) llama/patches/.*.patched
+	$(RM) $(addsuffix ed, $(PATCHES))

README.md

@@ -61,8 +61,6 @@ Here are some example models that can be downloaded:
 | QwQ                | 32B        | 20GB  | `ollama run qwq`                 |
 | DeepSeek-R1        | 7B         | 4.7GB | `ollama run deepseek-r1`         |
 | DeepSeek-R1        | 671B       | 404GB | `ollama run deepseek-r1:671b`    |
-| Llama 4            | 109B       | 67GB  | `ollama run llama4:scout`        |
-| Llama 4            | 400B       | 245GB | `ollama run llama4:maverick`     |
 | Llama 3.3          | 70B        | 43GB  | `ollama run llama3.3`            |
 | Llama 3.2          | 3B         | 2.0GB | `ollama run llama3.2`            |
 | Llama 3.2          | 1B         | 1.3GB | `ollama run llama3.2:1b`         |
@@ -79,7 +77,7 @@ 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-3.2         | 8B         | 4.9GB | `ollama run granite3.2`          |
 
 > [!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.
@@ -287,7 +285,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Bionic GPT](https://github.com/bionic-gpt/bionic-gpt)
 - [HTML UI](https://github.com/rtcfirefly/ollama-ui)
 - [Saddle](https://github.com/jikkuatwork/saddle)
-- [TagSpaces](https://www.tagspaces.org) (A platform for file-based apps, [utilizing Ollama](https://docs.tagspaces.org/ai/) for the generation of tags and descriptions)
+- [TagSpaces](https://www.tagspaces.org) (A platform for file based apps, [utilizing Ollama](https://docs.tagspaces.org/ai/) for the generation of tags and descriptions)
 - [Chatbot UI](https://github.com/ivanfioravanti/chatbot-ollama)
 - [Chatbot UI v2](https://github.com/mckaywrigley/chatbot-ui)
 - [Typescript UI](https://github.com/ollama-interface/Ollama-Gui?tab=readme-ov-file)
@@ -314,8 +312,6 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Ollama Basic Chat: Uses HyperDiv Reactive UI](https://github.com/rapidarchitect/ollama_basic_chat)
 - [Ollama-chats RPG](https://github.com/drazdra/ollama-chats)
 - [IntelliBar](https://intellibar.app/) (AI-powered assistant for macOS)
-- [Jirapt](https://github.com/AliAhmedNada/jirapt) (Jira Integration to generate issues, tasks, epics)
-- [ojira](https://github.com/AliAhmedNada/ojira) (Jira chrome plugin to easily generate descriptions for tasks)
 - [QA-Pilot](https://github.com/reid41/QA-Pilot) (Interactive chat tool that can leverage Ollama models for rapid understanding and navigation of GitHub code repositories)
 - [ChatOllama](https://github.com/sugarforever/chat-ollama) (Open Source Chatbot based on Ollama with Knowledge Bases)
 - [CRAG Ollama Chat](https://github.com/Nagi-ovo/CRAG-Ollama-Chat) (Simple Web Search with Corrective RAG)
@@ -329,14 +325,14 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [RWKV-Runner](https://github.com/josStorer/RWKV-Runner) (RWKV offline LLM deployment tool, also usable as a client for ChatGPT and Ollama)
 - [Ollama Grid Search](https://github.com/dezoito/ollama-grid-search) (app to evaluate and compare models)
 - [Olpaka](https://github.com/Otacon/olpaka) (User-friendly Flutter Web App for Ollama)
-- [Casibase](https://casibase.org) (An open source AI knowledge base and dialogue system combining the latest RAG, SSO, ollama support, and multiple large language models.)
+- [Casibase](https://casibase.org) (An open source AI knowledge base and dialogue system combining the latest RAG, SSO, ollama support and multiple large language models.)
 - [OllamaSpring](https://github.com/CrazyNeil/OllamaSpring) (Ollama Client for macOS)
 - [LLocal.in](https://github.com/kartikm7/llocal) (Easy to use Electron Desktop Client for Ollama)
 - [Shinkai Desktop](https://github.com/dcSpark/shinkai-apps) (Two click install Local AI using Ollama + Files + RAG)
-- [AiLama](https://github.com/zeyoyt/ailama) (A Discord User App that allows you to interact with Ollama anywhere in Discord)
+- [AiLama](https://github.com/zeyoyt/ailama) (A Discord User App that allows you to interact with Ollama anywhere in discord )
 - [Ollama with Google Mesop](https://github.com/rapidarchitect/ollama_mesop/) (Mesop Chat Client implementation with Ollama)
 - [R2R](https://github.com/SciPhi-AI/R2R) (Open-source RAG engine)
-- [Ollama-Kis](https://github.com/elearningshow/ollama-kis) (A simple easy-to-use GUI with sample custom LLM for Drivers Education)
+- [Ollama-Kis](https://github.com/elearningshow/ollama-kis) (A simple easy to use GUI with sample custom LLM for Drivers Education)
 - [OpenGPA](https://opengpa.org) (Open-source offline-first Enterprise Agentic Application)
 - [Painting Droid](https://github.com/mateuszmigas/painting-droid) (Painting app with AI integrations)
 - [Kerlig AI](https://www.kerlig.com/) (AI writing assistant for macOS)
@@ -345,16 +341,16 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [LLMStack](https://github.com/trypromptly/LLMStack) (No-code multi-agent framework to build LLM agents and workflows)
 - [BoltAI for Mac](https://boltai.com) (AI Chat Client for Mac)
 - [Harbor](https://github.com/av/harbor) (Containerized LLM Toolkit with Ollama as default backend)
-- [PyGPT](https://github.com/szczyglis-dev/py-gpt) (AI desktop assistant for Linux, Windows, and Mac)
-- [Alpaca](https://github.com/Jeffser/Alpaca) (An Ollama client application for Linux and macOS made with GTK4 and Adwaita)
+- [PyGPT](https://github.com/szczyglis-dev/py-gpt) (AI desktop assistant for Linux, Windows and Mac)
+- [Alpaca](https://github.com/Jeffser/Alpaca) (An Ollama client application for linux and macos made with GTK4 and Adwaita)
 - [AutoGPT](https://github.com/Significant-Gravitas/AutoGPT/blob/master/docs/content/platform/ollama.md) (AutoGPT Ollama integration)
 - [Go-CREW](https://www.jonathanhecl.com/go-crew/) (Powerful Offline RAG in Golang)
 - [PartCAD](https://github.com/openvmp/partcad/) (CAD model generation with OpenSCAD and CadQuery)
-- [Ollama4j Web UI](https://github.com/ollama4j/ollama4j-web-ui) - Java-based Web UI for Ollama built with Vaadin, Spring Boot, and Ollama4j
+- [Ollama4j Web UI](https://github.com/ollama4j/ollama4j-web-ui) - Java-based Web UI for Ollama built with Vaadin, Spring Boot and Ollama4j
 - [PyOllaMx](https://github.com/kspviswa/pyOllaMx) - macOS application capable of chatting with both Ollama and Apple MLX models.
 - [Cline](https://github.com/cline/cline) - Formerly known as Claude Dev is a VSCode extension for multi-file/whole-repo coding
 - [Cherry Studio](https://github.com/kangfenmao/cherry-studio) (Desktop client with Ollama support)
-- [ConfiChat](https://github.com/1runeberg/confichat) (Lightweight, standalone, multi-platform, and privacy-focused LLM chat interface with optional encryption)
+- [ConfiChat](https://github.com/1runeberg/confichat) (Lightweight, standalone, multi-platform, and privacy focused LLM chat interface with optional encryption)
 - [Archyve](https://github.com/nickthecook/archyve) (RAG-enabling document library)
 - [crewAI with Mesop](https://github.com/rapidarchitect/ollama-crew-mesop) (Mesop Web Interface to run crewAI with Ollama)
 - [Tkinter-based client](https://github.com/chyok/ollama-gui) (Python tkinter-based Client for Ollama)
@@ -372,7 +368,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [DualMind](https://github.com/tcsenpai/dualmind) (Experimental app allowing two models to talk to each other in the terminal or in a web interface)
 - [ollamarama-matrix](https://github.com/h1ddenpr0cess20/ollamarama-matrix) (Ollama chatbot for the Matrix chat protocol)
 - [ollama-chat-app](https://github.com/anan1213095357/ollama-chat-app) (Flutter-based chat app)
-- [Perfect Memory AI](https://www.perfectmemory.ai/) (Productivity AI assists personalized by what you have seen on your screen, heard, and said in the meetings)
+- [Perfect Memory AI](https://www.perfectmemory.ai/) (Productivity AI assists personalized by what you have seen on your screen, heard and said in the meetings)
 - [Hexabot](https://github.com/hexastack/hexabot) (A conversational AI builder)
 - [Reddit Rate](https://github.com/rapidarchitect/reddit_analyzer) (Search and Rate Reddit topics with a weighted summation)
 - [OpenTalkGpt](https://github.com/adarshM84/OpenTalkGpt) (Chrome Extension to manage open-source models supported by Ollama, create custom models, and chat with models from a user-friendly UI)
@@ -390,7 +386,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [ChibiChat](https://github.com/CosmicEventHorizon/ChibiChat) (Kotlin-based Android app to chat with Ollama and Koboldcpp API endpoints)
 - [LocalLLM](https://github.com/qusaismael/localllm) (Minimal Web-App to run ollama models on it with a GUI)
 - [Ollamazing](https://github.com/buiducnhat/ollamazing) (Web extension to run Ollama models)
-- [OpenDeepResearcher-via-searxng](https://github.com/benhaotang/OpenDeepResearcher-via-searxng) (A Deep Research equivalent endpoint with Ollama support for running locally)
+- [OpenDeepResearcher-via-searxng](https://github.com/benhaotang/OpenDeepResearcher-via-searxng) (A Deep Research equivent endpoint with Ollama support for running locally)
 - [AntSK](https://github.com/AIDotNet/AntSK) (Out-of-the-box & Adaptable RAG Chatbot)
 - [MaxKB](https://github.com/1Panel-dev/MaxKB/) (Ready-to-use & flexible RAG Chatbot)
 - [yla](https://github.com/danielekp/yla) (Web interface to freely interact with your customized models)
@@ -398,15 +394,11 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [1Panel](https://github.com/1Panel-dev/1Panel/) (Web-based Linux Server Management Tool)
 - [AstrBot](https://github.com/Soulter/AstrBot/) (User-friendly LLM-based multi-platform chatbot with a WebUI, supporting RAG, LLM agents, and plugins integration)
 - [Reins](https://github.com/ibrahimcetin/reins) (Easily tweak parameters, customize system prompts per chat, and enhance your AI experiments with reasoning model support.)
-- [Flufy](https://github.com/Aharon-Bensadoun/Flufy) (A beautiful chat interface for interacting with Ollama's API. Built with React, TypeScript, and Material-UI.)
 - [Ellama](https://github.com/zeozeozeo/ellama) (Friendly native app to chat with an Ollama instance)
 - [screenpipe](https://github.com/mediar-ai/screenpipe) Build agents powered by your screen history
 - [Ollamb](https://github.com/hengkysteen/ollamb) (Simple yet rich in features, cross-platform built with Flutter and designed for Ollama. Try the [web demo](https://hengkysteen.github.io/demo/ollamb/).)
 - [Writeopia](https://github.com/Writeopia/Writeopia) (Text editor with integration with Ollama)
 - [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.) 
 
 ### Cloud
 
@@ -448,9 +440,8 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [PowershAI](https://github.com/rrg92/powershai) PowerShell module that brings AI to terminal on Windows, including support for Ollama
 - [DeepShell](https://github.com/Abyss-c0re/deepshell) Your self-hosted AI assistant. Interactive Shell, Files and Folders analysis.
 - [orbiton](https://github.com/xyproto/orbiton) Configuration-free text editor and IDE with support for tab completion with Ollama.
-- [orca-cli](https://github.com/molbal/orca-cli) Ollama Registry CLI Application - Browse, pull, and download models from Ollama Registry in your terminal.
+- [orca-cli](https://github.com/molbal/orca-cli) Ollama Registry CLI Application - Browse, pull and download models from Ollama Registry in your terminal.
 - [GGUF-to-Ollama](https://github.com/jonathanhecl/gguf-to-ollama) - Importing GGUF to Ollama made easy (multiplatform)
-- [AWS-Strands-With-Ollama](https://github.com/rapidarchitect/ollama_strands) - AWS Strands Agents with Ollama Examples
 
 ### Apple Vision Pro
 
@@ -477,7 +468,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 
 ### Libraries
 
-- [LangChain](https://python.langchain.com/docs/integrations/chat/ollama/) and [LangChain.js](https://js.langchain.com/docs/integrations/chat/ollama/) with [example](https://js.langchain.com/docs/tutorials/local_rag/)
+- [LangChain](https://python.langchain.com/docs/integrations/llms/ollama) and [LangChain.js](https://js.langchain.com/docs/integrations/chat/ollama/) with [example](https://js.langchain.com/docs/tutorials/local_rag/)
 - [Firebase Genkit](https://firebase.google.com/docs/genkit/plugins/ollama)
 - [crewAI](https://github.com/crewAIInc/crewAI)
 - [Yacana](https://remembersoftwares.github.io/yacana/) (User-friendly multi-agent framework for brainstorming and executing predetermined flows with built-in tool integration)
@@ -524,21 +515,20 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Swollama for Swift](https://github.com/marcusziade/Swollama) with [DocC](https://marcusziade.github.io/Swollama/documentation/swollama/)
 - [GoLamify](https://github.com/prasad89/golamify)
 - [Ollama for Haskell](https://github.com/tusharad/ollama-haskell)
-- [multi-llm-ts](https://github.com/nbonamy/multi-llm-ts) (A Typescript/JavaScript library allowing access to different LLM in a unified API)
+- [multi-llm-ts](https://github.com/nbonamy/multi-llm-ts) (A Typescript/JavaScript library allowing access to different LLM in unified API)
 - [LlmTornado](https://github.com/lofcz/llmtornado) (C# library providing a unified interface for major FOSS & Commercial inference APIs)
 - [Ollama for Zig](https://github.com/dravenk/ollama-zig)
 - [Abso](https://github.com/lunary-ai/abso) (OpenAI-compatible TypeScript SDK for any LLM provider)
 - [Nichey](https://github.com/goodreasonai/nichey) is a Python package for generating custom wikis for your research topic
 - [Ollama for D](https://github.com/kassane/ollama-d)
-- [OllamaPlusPlus](https://github.com/HardCodeDev777/OllamaPlusPlus) (Very simple C++ library for Ollama)
 
 ### Mobile
 
-- [SwiftChat](https://github.com/aws-samples/swift-chat) (Lightning-fast Cross-platform AI chat app with native UI for Android, iOS, and iPad)
+- [SwiftChat](https://github.com/aws-samples/swift-chat) (Lightning-fast Cross-platform AI chat app with native UI for Android, iOS and iPad)
 - [Enchanted](https://github.com/AugustDev/enchanted)
 - [Maid](https://github.com/Mobile-Artificial-Intelligence/maid)
 - [Ollama App](https://github.com/JHubi1/ollama-app) (Modern and easy-to-use multi-platform client for Ollama)
-- [ConfiChat](https://github.com/1runeberg/confichat) (Lightweight, standalone, multi-platform, and privacy-focused LLM chat interface with optional encryption)
+- [ConfiChat](https://github.com/1runeberg/confichat) (Lightweight, standalone, multi-platform, and privacy focused LLM chat interface with optional encryption)
 - [Ollama Android Chat](https://github.com/sunshine0523/OllamaServer) (No need for Termux, start the Ollama service with one click on an Android device)
 - [Reins](https://github.com/ibrahimcetin/reins) (Easily tweak parameters, customize system prompts per chat, and enhance your AI experiments with reasoning model support.)
 
@@ -562,7 +552,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Obsidian Local GPT plugin](https://github.com/pfrankov/obsidian-local-gpt)
 - [Open Interpreter](https://docs.openinterpreter.com/language-model-setup/local-models/ollama)
 - [Llama Coder](https://github.com/ex3ndr/llama-coder) (Copilot alternative using Ollama)
-- [Ollama Copilot](https://github.com/bernardo-bruning/ollama-copilot) (Proxy that allows you to use Ollama as a copilot like GitHub Copilot)
+- [Ollama Copilot](https://github.com/bernardo-bruning/ollama-copilot) (Proxy that allows you to use ollama as a copilot like Github copilot)
 - [twinny](https://github.com/rjmacarthy/twinny) (Copilot and Copilot chat alternative using Ollama)
 - [Wingman-AI](https://github.com/RussellCanfield/wingman-ai) (Copilot code and chat alternative using Ollama and Hugging Face)
 - [Page Assist](https://github.com/n4ze3m/page-assist) (Chrome Extension)
@@ -572,8 +562,8 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Discord-Ollama Chat Bot](https://github.com/kevinthedang/discord-ollama) (Generalized TypeScript Discord Bot w/ Tuning Documentation)
 - [ChatGPTBox: All in one browser extension](https://github.com/josStorer/chatGPTBox) with [Integrating Tutorial](https://github.com/josStorer/chatGPTBox/issues/616#issuecomment-1975186467)
 - [Discord AI chat/moderation bot](https://github.com/rapmd73/Companion) Chat/moderation bot written in python. Uses Ollama to create personalities.
-- [Headless Ollama](https://github.com/nischalj10/headless-ollama) (Scripts to automatically install ollama client & models on any OS for apps that depend on ollama server)
-- [Terraform AWS Ollama & Open WebUI](https://github.com/xuyangbocn/terraform-aws-self-host-llm) (A Terraform module to deploy on AWS a ready-to-use Ollama service, together with its front-end Open WebUI service.)
+- [Headless Ollama](https://github.com/nischalj10/headless-ollama) (Scripts to automatically install ollama client & models on any OS for apps that depends on ollama server)
+- [Terraform AWS Ollama & Open WebUI](https://github.com/xuyangbocn/terraform-aws-self-host-llm) (A Terraform module to deploy on AWS a ready-to-use Ollama service, together with its front end Open WebUI service.)
 - [node-red-contrib-ollama](https://github.com/jakubburkiewicz/node-red-contrib-ollama)
 - [Local AI Helper](https://github.com/ivostoykov/localAI) (Chrome and Firefox extensions that enable interactions with the active tab and customisable API endpoints. Includes secure storage for user prompts.)
 - [vnc-lm](https://github.com/jake83741/vnc-lm) (Discord bot for messaging with LLMs through Ollama and LiteLLM. Seamlessly move between local and flagship models.)
@@ -587,8 +577,6 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Simple-Discord-AI](https://github.com/zyphixor/simple-discord-ai)
 - [LLM Telegram Bot](https://github.com/innightwolfsleep/llm_telegram_bot) (telegram bot, primary for RP. Oobabooga-like buttons, [A1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui) API integration e.t.c)
 - [mcp-llm](https://github.com/sammcj/mcp-llm) (MCP Server to allow LLMs to call other LLMs)
-- [SimpleOllamaUnity](https://github.com/HardCodeDev777/SimpleOllamaUnity) (Unity Engine extension for communicating with Ollama in a few lines of code. Also works at runtime)
-- [UnityCodeLama](https://github.com/HardCodeDev777/UnityCodeLama) (Unity Edtior tool to analyze scripts via Ollama)
 
 ### Supported backends
 

api/client.go

@@ -24,10 +24,7 @@ import (
 	"net/http"
 	"net/url"
 	"runtime"
-	"strconv"
-	"time"
 
-	"github.com/ollama/ollama/auth"
 	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/format"
 	"github.com/ollama/ollama/version"
@@ -79,14 +76,6 @@ func NewClient(base *url.URL, http *http.Client) *Client {
 	}
 }
 
-func getAuthorizationToken(ctx context.Context, challenge string) (string, error) {
-	token, err := auth.Sign(ctx, []byte(challenge))
-	if err != nil {
-		return "", err
-	}
-	return token, nil
-}
-
 func (c *Client) do(ctx context.Context, method, path string, reqData, respData any) error {
 	var reqBody io.Reader
 	var data []byte
@@ -108,21 +97,6 @@ func (c *Client) do(ctx context.Context, method, path string, reqData, respData
 	}
 
 	requestURL := c.base.JoinPath(path)
-
-	var token string
-	if envconfig.UseAuth() || c.base.Hostname() == "ollama.com" {
-		now := strconv.FormatInt(time.Now().Unix(), 10)
-		chal := fmt.Sprintf("%s,%s?ts=%s", method, path, now)
-		token, err = getAuthorizationToken(ctx, chal)
-		if err != nil {
-			return err
-		}
-
-		q := requestURL.Query()
-		q.Set("ts", now)
-		requestURL.RawQuery = q.Encode()
-	}
-
 	request, err := http.NewRequestWithContext(ctx, method, requestURL.String(), reqBody)
 	if err != nil {
 		return err
@@ -132,10 +106,6 @@ func (c *Client) do(ctx context.Context, method, path string, reqData, respData
 	request.Header.Set("Accept", "application/json")
 	request.Header.Set("User-Agent", fmt.Sprintf("ollama/%s (%s %s) Go/%s", version.Version, runtime.GOARCH, runtime.GOOS, runtime.Version()))
 
-	if token != "" {
-		request.Header.Set("Authorization", token)
-	}
-
 	respObj, err := c.http.Do(request)
 	if err != nil {
 		return err
@@ -173,22 +143,6 @@ func (c *Client) stream(ctx context.Context, method, path string, data any, fn f
 	}
 
 	requestURL := c.base.JoinPath(path)
-
-	var token string
-	if envconfig.UseAuth() || c.base.Hostname() == "ollama.com" {
-		var err error
-		now := strconv.FormatInt(time.Now().Unix(), 10)
-		chal := fmt.Sprintf("%s,%s?ts=%s", method, path, now)
-		token, err = getAuthorizationToken(ctx, chal)
-		if err != nil {
-			return err
-		}
-
-		q := requestURL.Query()
-		q.Set("ts", now)
-		requestURL.RawQuery = q.Encode()
-	}
-
 	request, err := http.NewRequestWithContext(ctx, method, requestURL.String(), buf)
 	if err != nil {
 		return err
@@ -198,10 +152,6 @@ func (c *Client) stream(ctx context.Context, method, path string, data any, fn f
 	request.Header.Set("Accept", "application/x-ndjson")
 	request.Header.Set("User-Agent", fmt.Sprintf("ollama/%s (%s %s) Go/%s", version.Version, runtime.GOARCH, runtime.GOOS, runtime.Version()))
 
-	if token != "" {
-		request.Header.Set("Authorization", token)
-	}
-
 	response, err := c.http.Do(request)
 	if err != nil {
 		return err

api/client_test.go

@@ -1,6 +1,7 @@
 package api
 
 import (
+	"context"
 	"encoding/json"
 	"fmt"
 	"net/http"
@@ -136,7 +137,7 @@ func TestClientStream(t *testing.T) {
 			client := NewClient(&url.URL{Scheme: "http", Host: ts.Listener.Addr().String()}, http.DefaultClient)
 
 			var receivedChunks []ChatResponse
-			err := client.stream(t.Context(), http.MethodPost, "/v1/chat", nil, func(chunk []byte) error {
+			err := client.stream(context.Background(), http.MethodPost, "/v1/chat", nil, func(chunk []byte) error {
 				var resp ChatResponse
 				if err := json.Unmarshal(chunk, &resp); err != nil {
 					return fmt.Errorf("failed to unmarshal chunk: %w", err)
@@ -222,7 +223,7 @@ func TestClientDo(t *testing.T) {
 				ID      string `json:"id"`
 				Success bool   `json:"success"`
 			}
-			err := client.do(t.Context(), http.MethodPost, "/v1/messages", nil, &resp)
+			err := client.do(context.Background(), http.MethodPost, "/v1/messages", nil, &resp)
 
 			if tc.wantErr != "" {
 				if err == nil {

api/types.go

@@ -83,12 +83,6 @@ type GenerateRequest struct {
 	// Options lists model-specific options. For example, temperature can be
 	// set through this field, if the model supports it.
 	Options map[string]any `json:"options"`
-
-	// Think controls whether thinking/reasoning models will think before
-	// responding. Needs to be a pointer so we can distinguish between false
-	// (request that thinking _not_ be used) and unset (use the old behavior
-	// before this option was introduced)
-	Think *bool `json:"think,omitempty"`
 }
 
 // ChatRequest describes a request sent by [Client.Chat].
@@ -114,10 +108,6 @@ type ChatRequest struct {
 
 	// Options lists model-specific options.
 	Options map[string]any `json:"options"`
-
-	// Think controls whether thinking/reasoning models will think before
-	// responding
-	Think *bool `json:"think,omitempty"`
 }
 
 type Tools []Tool
@@ -136,11 +126,8 @@ func (t Tool) String() string {
 // role ("system", "user", or "assistant"), the content and an optional list
 // of images.
 type Message struct {
-	Role    string `json:"role"`
-	Content string `json:"content"`
-	// Thinking contains the text that was inside thinking tags in the
-	// original model output when ChatRequest.Think is enabled.
-	Thinking  string      `json:"thinking,omitempty"`
+	Role      string      `json:"role"`
+	Content   string      `json:"content"`
 	Images    []ImageData `json:"images,omitempty"`
 	ToolCalls []ToolCall  `json:"tool_calls,omitempty"`
 }
@@ -284,6 +271,9 @@ type Options struct {
 	RepeatPenalty    float32  `json:"repeat_penalty,omitempty"`
 	PresencePenalty  float32  `json:"presence_penalty,omitempty"`
 	FrequencyPenalty float32  `json:"frequency_penalty,omitempty"`
+	Mirostat         int      `json:"mirostat,omitempty"`
+	MirostatTau      float32  `json:"mirostat_tau,omitempty"`
+	MirostatEta      float32  `json:"mirostat_eta,omitempty"`
 	Stop             []string `json:"stop,omitempty"`
 }
 
@@ -293,7 +283,12 @@ type Runner struct {
 	NumBatch  int   `json:"num_batch,omitempty"`
 	NumGPU    int   `json:"num_gpu,omitempty"`
 	MainGPU   int   `json:"main_gpu,omitempty"`
+	LowVRAM   bool  `json:"low_vram,omitempty"`
+	F16KV     bool  `json:"f16_kv,omitempty"` // Deprecated: This option is ignored
+	LogitsAll bool  `json:"logits_all,omitempty"`
+	VocabOnly bool  `json:"vocab_only,omitempty"`
 	UseMMap   *bool `json:"use_mmap,omitempty"`
+	UseMLock  bool  `json:"use_mlock,omitempty"`
 	NumThread int   `json:"num_thread,omitempty"`
 }
 
@@ -457,13 +452,12 @@ type ProcessResponse struct {
 
 // ListModelResponse is a single model description in [ListResponse].
 type ListModelResponse struct {
-	Name         string             `json:"name"`
-	Model        string             `json:"model"`
-	ModifiedAt   time.Time          `json:"modified_at"`
-	Size         int64              `json:"size"`
-	Digest       string             `json:"digest"`
-	Capabilities []model.Capability `json:"capabilities,omitempty"`
-	Details      ModelDetails       `json:"details,omitempty"`
+	Name       string       `json:"name"`
+	Model      string       `json:"model"`
+	ModifiedAt time.Time    `json:"modified_at"`
+	Size       int64        `json:"size"`
+	Digest     string       `json:"digest"`
+	Details    ModelDetails `json:"details,omitempty"`
 }
 
 // ProcessModelResponse is a single model description in [ProcessResponse].
@@ -477,6 +471,13 @@ type ProcessModelResponse struct {
 	SizeVRAM  int64        `json:"size_vram"`
 }
 
+type RetrieveModelResponse struct {
+	Id      string `json:"id"`
+	Object  string `json:"object"`
+	Created int64  `json:"created"`
+	OwnedBy string `json:"owned_by"`
+}
+
 type TokenResponse struct {
 	Token string `json:"token"`
 }
@@ -492,10 +493,6 @@ type GenerateResponse struct {
 	// Response is the textual response itself.
 	Response string `json:"response"`
 
-	// Thinking contains the text that was inside thinking tags in the
-	// original model output when ChatRequest.Think is enabled.
-	Thinking string `json:"thinking,omitempty"`
-
 	// Done specifies if the response is complete.
 	Done bool `json:"done"`
 
@@ -663,6 +660,9 @@ func DefaultOptions() Options {
 		RepeatPenalty:    1.1,
 		PresencePenalty:  0.0,
 		FrequencyPenalty: 0.0,
+		Mirostat:         0,
+		MirostatTau:      5.0,
+		MirostatEta:      0.1,
 		Seed:             -1,
 
 		Runner: Runner{
@@ -671,6 +671,8 @@ func DefaultOptions() Options {
 			NumBatch:  512,
 			NumGPU:    -1, // -1 here indicates that NumGPU should be set dynamically
 			NumThread: 0,  // let the runtime decide
+			LowVRAM:   false,
+			UseMLock:  false,
 			UseMMap:   nil,
 		},
 	}

api/types_test.go

@@ -372,50 +372,3 @@ func TestPropertyType_MarshalJSON(t *testing.T) {
 		})
 	}
 }
-
-func TestThinking_UnmarshalJSON(t *testing.T) {
-	trueVal := true
-	falseVal := false
-
-	tests := []struct {
-		name             string
-		input            string
-		expectedThinking *bool
-		expectedError    bool
-	}{
-		{
-			name:             "true",
-			input:            `{ "think": true }`,
-			expectedThinking: &trueVal,
-		},
-		{
-			name:             "false",
-			input:            `{ "think": false }`,
-			expectedThinking: &falseVal,
-		},
-		{
-			name:             "unset",
-			input:            `{ }`,
-			expectedThinking: nil,
-		},
-		{
-			name:             "invalid",
-			input:            `{ "think": "true" }`,
-			expectedThinking: nil,
-			expectedError:    true,
-		},
-	}
-
-	for _, test := range tests {
-		t.Run(test.name, func(t *testing.T) {
-			var req GenerateRequest
-			err := json.Unmarshal([]byte(test.input), &req)
-			if test.expectedError {
-				require.Error(t, err)
-			} else {
-				require.NoError(t, err)
-				assert.Equal(t, test.expectedThinking, req.Think)
-			}
-		})
-	}
-}

app/lifecycle/logging.go

@@ -4,14 +4,20 @@ import (
 	"fmt"
 	"log/slog"
 	"os"
+	"path/filepath"
 	"strconv"
 	"strings"
 
 	"github.com/ollama/ollama/envconfig"
-	"github.com/ollama/ollama/logutil"
 )
 
 func InitLogging() {
+	level := slog.LevelInfo
+
+	if envconfig.Debug() {
+		level = slog.LevelDebug
+	}
+
 	var logFile *os.File
 	var err error
 	// Detect if we're a GUI app on windows, and if not, send logs to console
@@ -27,8 +33,20 @@ func InitLogging() {
 			return
 		}
 	}
+	handler := slog.NewTextHandler(logFile, &slog.HandlerOptions{
+		Level:     level,
+		AddSource: true,
+		ReplaceAttr: func(_ []string, attr slog.Attr) slog.Attr {
+			if attr.Key == slog.SourceKey {
+				source := attr.Value.Any().(*slog.Source)
+				source.File = filepath.Base(source.File)
+			}
+			return attr
+		},
+	})
+
+	slog.SetDefault(slog.New(handler))
 
-	slog.SetDefault(logutil.NewLogger(logFile, envconfig.LogLevel()))
 	slog.Info("ollama app started")
 }
 

benchmark/server_benchmark_test.go

@@ -78,7 +78,7 @@ func BenchmarkColdStart(b *testing.B) {
 
 	for _, tt := range tests {
 		b.Run(fmt.Sprintf("%s/cold/%s", m, tt.name), func(b *testing.B) {
-			ctx := b.Context()
+			ctx := context.Background()
 
 			// Set number of tokens as our throughput metric
 			b.SetBytes(int64(tt.maxTokens))
@@ -113,7 +113,7 @@ func BenchmarkWarmStart(b *testing.B) {
 
 	for _, tt := range tests {
 		b.Run(fmt.Sprintf("%s/warm/%s", m, tt.name), func(b *testing.B) {
-			ctx := b.Context()
+			ctx := context.Background()
 
 			// Pre-warm the model
 			warmup(client, m, tt.prompt, b)
@@ -140,7 +140,7 @@ func setup(b *testing.B) *api.Client {
 	if err != nil {
 		b.Fatal(err)
 	}
-	if _, err := client.Show(b.Context(), &api.ShowRequest{Model: modelName(b)}); err != nil {
+	if _, err := client.Show(context.Background(), &api.ShowRequest{Model: modelName(b)}); err != nil {
 		b.Fatalf("Model unavailable: %v", err)
 	}
 

cmd/cmd.go

@@ -31,7 +31,6 @@ import (
 	"github.com/olekukonko/tablewriter"
 	"github.com/spf13/cobra"
 	"golang.org/x/crypto/ssh"
-	"golang.org/x/sync/errgroup"
 	"golang.org/x/term"
 
 	"github.com/ollama/ollama/api"
@@ -39,31 +38,12 @@ import (
 	"github.com/ollama/ollama/format"
 	"github.com/ollama/ollama/parser"
 	"github.com/ollama/ollama/progress"
-	"github.com/ollama/ollama/readline"
 	"github.com/ollama/ollama/runner"
 	"github.com/ollama/ollama/server"
 	"github.com/ollama/ollama/types/model"
-	"github.com/ollama/ollama/types/syncmap"
 	"github.com/ollama/ollama/version"
 )
 
-// ensureThinkingSupport emits a warning if the model does not advertise thinking support
-func ensureThinkingSupport(ctx context.Context, client *api.Client, name string) {
-	if name == "" {
-		return
-	}
-	resp, err := client.Show(ctx, &api.ShowRequest{Model: name})
-	if err != nil {
-		return
-	}
-	for _, cap := range resp.Capabilities {
-		if cap == model.CapabilityThinking {
-			return
-		}
-	}
-	fmt.Fprintf(os.Stderr, "warning: model %q does not support thinking output\n", name)
-}
-
 var errModelfileNotFound = errors.New("specified Modelfile wasn't found")
 
 func getModelfileName(cmd *cobra.Command) (string, error) {
@@ -126,7 +106,7 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 	}
 	spinner.Stop()
 
-	req.Model = args[0]
+	req.Name = args[0]
 	quantize, _ := cmd.Flags().GetString("quantize")
 	if quantize != "" {
 		req.Quantize = quantize
@@ -137,54 +117,34 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 		return err
 	}
 
-	var g errgroup.Group
-	g.SetLimit(max(runtime.GOMAXPROCS(0)-1, 1))
-
-	files := syncmap.NewSyncMap[string, string]()
-	for f, digest := range req.Files {
-		g.Go(func() error {
+	if len(req.Files) > 0 {
+		fileMap := map[string]string{}
+		for f, digest := range req.Files {
 			if _, err := createBlob(cmd, client, f, digest, p); err != nil {
 				return err
 			}
-
-			// TODO: this is incorrect since the file might be in a subdirectory
-			//       instead this should take the path relative to the model directory
-			//       but the current implementation does not allow this
-			files.Store(filepath.Base(f), digest)
-			return nil
-		})
+			fileMap[filepath.Base(f)] = digest
+		}
+		req.Files = fileMap
 	}
 
-	adapters := syncmap.NewSyncMap[string, string]()
-	for f, digest := range req.Adapters {
-		g.Go(func() error {
+	if len(req.Adapters) > 0 {
+		fileMap := map[string]string{}
+		for f, digest := range req.Adapters {
 			if _, err := createBlob(cmd, client, f, digest, p); err != nil {
 				return err
 			}
-
-			// TODO: same here
-			adapters.Store(filepath.Base(f), digest)
-			return nil
-		})
+			fileMap[filepath.Base(f)] = digest
+		}
+		req.Adapters = fileMap
 	}
 
-	if err := g.Wait(); err != nil {
-		return err
-	}
-
-	req.Files = files.Items()
-	req.Adapters = adapters.Items()
-
 	bars := make(map[string]*progress.Bar)
 	fn := func(resp api.ProgressResponse) error {
 		if resp.Digest != "" {
 			bar, ok := bars[resp.Digest]
 			if !ok {
-				msg := resp.Status
-				if msg == "" {
-					msg = fmt.Sprintf("pulling %s...", resp.Digest[7:19])
-				}
-				bar = progress.NewBar(msg, resp.Total, resp.Completed)
+				bar = progress.NewBar(fmt.Sprintf("pulling %s...", resp.Digest[7:19]), resp.Total, resp.Completed)
 				bars[resp.Digest] = bar
 				p.Add(resp.Digest, bar)
 			}
@@ -253,7 +213,7 @@ func createBlob(cmd *cobra.Command, client *api.Client, path string, digest stri
 		}
 	}()
 
-	if err := client.CreateBlob(cmd.Context(), digest, io.TeeReader(bin, &pw)); err != nil {
+	if err = client.CreateBlob(cmd.Context(), digest, io.TeeReader(bin, &pw)); err != nil {
 		return "", err
 	}
 	return digest, nil
@@ -283,9 +243,6 @@ func loadOrUnloadModel(cmd *cobra.Command, opts *runOptions) error {
 	req := &api.GenerateRequest{
 		Model:     opts.Model,
 		KeepAlive: opts.KeepAlive,
-
-		// pass Think here so we fail before getting to the chat prompt if the model doesn't support it
-		Think: opts.Think,
 	}
 
 	return client.Generate(cmd.Context(), req, func(api.GenerateResponse) error { return nil })
@@ -320,22 +277,6 @@ func RunHandler(cmd *cobra.Command, args []string) error {
 	}
 	opts.Format = format
 
-	thinkFlag := cmd.Flags().Lookup("think")
-	if thinkFlag.Changed {
-		think, err := cmd.Flags().GetBool("think")
-		if err != nil {
-			return err
-		}
-		opts.Think = &think
-	} else {
-		opts.Think = nil
-	}
-	hidethinking, err := cmd.Flags().GetBool("hidethinking")
-	if err != nil {
-		return err
-	}
-	opts.HideThinking = hidethinking
-
 	keepAlive, err := cmd.Flags().GetString("keepalive")
 	if err != nil {
 		return err
@@ -399,11 +340,6 @@ func RunHandler(cmd *cobra.Command, args []string) error {
 		return err
 	}
 
-	opts.Think, err = inferThinkingOption(&info.Capabilities, &opts, thinkFlag.Changed)
-	if err != nil {
-		return err
-	}
-
 	opts.MultiModal = slices.Contains(info.Capabilities, model.CapabilityVision)
 
 	// TODO: remove the projector info and vision info checks below,
@@ -789,38 +725,11 @@ func showInfo(resp *api.ShowResponse, verbose bool, w io.Writer) error {
 				case float64:
 					v = fmt.Sprintf("%g", vData)
 				case []any:
-					targetWidth := 10 // Small width where we are displaying the data in a column
-
-					var itemsToShow int
-					totalWidth := 1 // Start with 1 for opening bracket
-
-					// Find how many we can fit
-					for i := range vData {
-						itemStr := fmt.Sprintf("%v", vData[i])
-						width := runewidth.StringWidth(itemStr)
-
-						// Add separator width (", ") for all items except the first
-						if i > 0 {
-							width += 2
-						}
-
-						// Check if adding this item would exceed our width limit
-						if totalWidth+width > targetWidth && i > 0 {
-							break
-						}
-
-						totalWidth += width
-						itemsToShow++
-					}
-
-					// Format the output
-					if itemsToShow < len(vData) {
-						v = fmt.Sprintf("%v", vData[:itemsToShow])
-						v = strings.TrimSuffix(v, "]")
-						v += fmt.Sprintf(" ...+%d more]", len(vData)-itemsToShow)
-					} else {
-						v = fmt.Sprintf("%v", vData)
+					n := 3
+					if len(vData) < n {
+						n = len(vData)
 					}
+					v = fmt.Sprintf("%v", vData[:n])
 				default:
 					v = fmt.Sprintf("%T", vData)
 				}
@@ -841,19 +750,10 @@ func showInfo(resp *api.ShowResponse, verbose bool, w io.Writer) error {
 
 	head := func(s string, n int) (rows [][]string) {
 		scanner := bufio.NewScanner(strings.NewReader(s))
-		count := 0
-		for scanner.Scan() {
-			text := strings.TrimSpace(scanner.Text())
-			if text == "" {
-				continue
+		for scanner.Scan() && (len(rows) < n || n < 0) {
+			if text := scanner.Text(); text != "" {
+				rows = append(rows, []string{"", strings.TrimSpace(text)})
 			}
-			count++
-			if n < 0 || count <= n {
-				rows = append(rows, []string{"", text})
-			}
-		}
-		if n >= 0 && count > n {
-			rows = append(rows, []string{"", "..."})
 		}
 		return
 	}
@@ -965,19 +865,17 @@ func PullHandler(cmd *cobra.Command, args []string) error {
 type generateContextKey string
 
 type runOptions struct {
-	Model        string
-	ParentModel  string
-	Prompt       string
-	Messages     []api.Message
-	WordWrap     bool
-	Format       string
-	System       string
-	Images       []api.ImageData
-	Options      map[string]any
-	MultiModal   bool
-	KeepAlive    *api.Duration
-	Think        *bool
-	HideThinking bool
+	Model       string
+	ParentModel string
+	Prompt      string
+	Messages    []api.Message
+	WordWrap    bool
+	Format      string
+	System      string
+	Images      []api.ImageData
+	Options     map[string]any
+	MultiModal  bool
+	KeepAlive   *api.Duration
 }
 
 type displayResponseState struct {
@@ -1033,26 +931,6 @@ func displayResponse(content string, wordWrap bool, state *displayResponseState)
 	}
 }
 
-func thinkingOutputOpeningText(plainText bool) string {
-	text := "Thinking...\n"
-
-	if plainText {
-		return text
-	}
-
-	return readline.ColorGrey + readline.ColorBold + text + readline.ColorDefault + readline.ColorGrey
-}
-
-func thinkingOutputClosingText(plainText bool) string {
-	text := "...done thinking.\n\n"
-
-	if plainText {
-		return text
-	}
-
-	return readline.ColorGrey + readline.ColorBold + text + readline.ColorDefault
-}
-
 func chat(cmd *cobra.Command, opts runOptions) (*api.Message, error) {
 	client, err := api.ClientFromEnvironment()
 	if err != nil {
@@ -1080,34 +958,14 @@ func chat(cmd *cobra.Command, opts runOptions) (*api.Message, error) {
 	var latest api.ChatResponse
 	var fullResponse strings.Builder
 	var role string
-	var thinkTagOpened bool = false
-	var thinkTagClosed bool = false
 
 	fn := func(response api.ChatResponse) error {
-		if response.Message.Content != "" || !opts.HideThinking {
-			p.StopAndClear()
-		}
+		p.StopAndClear()
 
 		latest = response
 
 		role = response.Message.Role
-		if response.Message.Thinking != "" && !opts.HideThinking {
-			if !thinkTagOpened {
-				fmt.Print(thinkingOutputOpeningText(false))
-				thinkTagOpened = true
-			}
-			displayResponse(response.Message.Thinking, opts.WordWrap, state)
-		}
-
 		content := response.Message.Content
-		if thinkTagOpened && !thinkTagClosed && content != "" {
-			fmt.Print(thinkingOutputClosingText(false))
-			thinkTagClosed = true
-		}
-		// purposefully not putting thinking blocks in the response, which would
-		// only be needed if we later added tool calling to the cli (they get
-		// filtered out anyway since current models don't expect them unless you're
-		// about to finish some tool calls)
 		fullResponse.WriteString(content)
 
 		displayResponse(content, opts.WordWrap, state)
@@ -1124,7 +982,6 @@ func chat(cmd *cobra.Command, opts runOptions) (*api.Message, error) {
 		Messages: opts.Messages,
 		Format:   json.RawMessage(opts.Format),
 		Options:  opts.Options,
-		Think:    opts.Think,
 	}
 
 	if opts.KeepAlive != nil {
@@ -1186,32 +1043,13 @@ func generate(cmd *cobra.Command, opts runOptions) error {
 	}()
 
 	var state *displayResponseState = &displayResponseState{}
-	var thinkTagOpened bool = false
-	var thinkTagClosed bool = false
-
-	plainText := !term.IsTerminal(int(os.Stdout.Fd()))
 
 	fn := func(response api.GenerateResponse) error {
+		p.StopAndClear()
+
 		latest = response
 		content := response.Response
 
-		if response.Response != "" || !opts.HideThinking {
-			p.StopAndClear()
-		}
-
-		if response.Thinking != "" && !opts.HideThinking {
-			if !thinkTagOpened {
-				fmt.Print(thinkingOutputOpeningText(plainText))
-				thinkTagOpened = true
-			}
-			displayResponse(response.Thinking, opts.WordWrap, state)
-		}
-
-		if thinkTagOpened && !thinkTagClosed && content != "" {
-			fmt.Print(thinkingOutputClosingText(plainText))
-			thinkTagClosed = true
-		}
-
 		displayResponse(content, opts.WordWrap, state)
 
 		return nil
@@ -1237,7 +1075,6 @@ func generate(cmd *cobra.Command, opts runOptions) error {
 		System:    opts.System,
 		Options:   opts.Options,
 		KeepAlive: opts.KeepAlive,
-		Think:     opts.Think,
 	}
 
 	if err := client.Generate(ctx, &request, fn); err != nil {
@@ -1341,11 +1178,11 @@ func checkServerHeartbeat(cmd *cobra.Command, _ []string) error {
 		return err
 	}
 	if err := client.Heartbeat(cmd.Context()); err != nil {
-		if !(strings.Contains(err.Error(), " refused") || strings.Contains(err.Error(), "could not connect")) {
+		if !strings.Contains(err.Error(), " refused") {
 			return err
 		}
 		if err := startApp(cmd.Context(), client); err != nil {
-			return fmt.Errorf("ollama server not responding - %w", err)
+			return errors.New("could not connect to ollama app, is it running?")
 		}
 	}
 	return nil
@@ -1423,7 +1260,7 @@ func NewCLI() *cobra.Command {
 	}
 
 	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().StringP("quantize", "q", "", "Quantize model to this level (e.g. q4_0)")
 
 	showCmd := &cobra.Command{
 		Use:     "show MODEL",
@@ -1453,8 +1290,6 @@ func NewCLI() *cobra.Command {
 	runCmd.Flags().Bool("insecure", false, "Use an insecure registry")
 	runCmd.Flags().Bool("nowordwrap", false, "Don't wrap words to the next line automatically")
 	runCmd.Flags().String("format", "", "Response format (e.g. json)")
-	runCmd.Flags().Bool("think", false, "Whether to use thinking mode for supported models")
-	runCmd.Flags().Bool("hidethinking", false, "Hide thinking output (if provided)")
 
 	stopCmd := &cobra.Command{
 		Use:     "stop MODEL",
@@ -1506,6 +1341,7 @@ func NewCLI() *cobra.Command {
 		PreRunE: checkServerHeartbeat,
 		RunE:    ListRunningHandler,
 	}
+
 	copyCmd := &cobra.Command{
 		Use:     "cp SOURCE DESTINATION",
 		Short:   "Copy a model",
@@ -1571,6 +1407,7 @@ func NewCLI() *cobra.Command {
 				envVars["OLLAMA_LLM_LIBRARY"],
 				envVars["OLLAMA_GPU_OVERHEAD"],
 				envVars["OLLAMA_LOAD_TIMEOUT"],
+				envVars["OLLAMA_CONTEXT_LENGTH"],
 			})
 		default:
 			appendEnvDocs(cmd, envs)
@@ -1594,45 +1431,3 @@ func NewCLI() *cobra.Command {
 
 	return rootCmd
 }
-
-// If the user has explicitly set thinking options, either through the CLI or
-// through the `/set think` or `set nothink` interactive options, then we
-// respect them. Otherwise, we check model capabilities to see if the model
-// supports thinking. If the model does support thinking, we enable it.
-// Otherwise, we unset the thinking option (which is different than setting it
-// to false).
-//
-// If capabilities are not provided, we fetch them from the server.
-func inferThinkingOption(caps *[]model.Capability, runOpts *runOptions, explicitlySetByUser bool) (*bool, error) {
-	if explicitlySetByUser {
-		return runOpts.Think, nil
-	}
-
-	if caps == nil {
-		client, err := api.ClientFromEnvironment()
-		if err != nil {
-			return nil, err
-		}
-		ret, err := client.Show(context.Background(), &api.ShowRequest{
-			Model: runOpts.Model,
-		})
-		if err != nil {
-			return nil, err
-		}
-		caps = &ret.Capabilities
-	}
-
-	thinkingSupported := false
-	for _, cap := range *caps {
-		if cap == model.CapabilityThinking {
-			thinkingSupported = true
-		}
-	}
-
-	if thinkingSupported {
-		thinking := true
-		return &thinking, nil
-	}
-
-	return nil, nil
-}

cmd/cmd_test.go

@@ -2,6 +2,7 @@ package cmd
 
 import (
 	"bytes"
+	"context"
 	"encoding/json"
 	"io"
 	"net/http"
@@ -225,7 +226,6 @@ Weigh anchor!
   System
     You are a pirate!    
     Ahoy, matey!         
-    ...                  
 
 `
 		if diff := cmp.Diff(expect, b.String()); diff != "" {
@@ -337,7 +337,7 @@ func TestDeleteHandler(t *testing.T) {
 	t.Cleanup(mockServer.Close)
 
 	cmd := &cobra.Command{}
-	cmd.SetContext(t.Context())
+	cmd.SetContext(context.TODO())
 	if err := DeleteHandler(cmd, []string{"test-model"}); err != nil {
 		t.Fatalf("DeleteHandler failed: %v", err)
 	}
@@ -399,6 +399,11 @@ func TestGetModelfileName(t *testing.T) {
 			var expectedFilename string
 
 			if tt.fileExists {
+				tempDir, err := os.MkdirTemp("", "modelfiledir")
+				defer os.RemoveAll(tempDir)
+				if err != nil {
+					t.Fatalf("temp modelfile dir creation failed: %v", err)
+				}
 				var fn string
 				if tt.modelfileName != "" {
 					fn = tt.modelfileName
@@ -406,7 +411,7 @@ func TestGetModelfileName(t *testing.T) {
 					fn = "Modelfile"
 				}
 
-				tempFile, err := os.CreateTemp(t.TempDir(), fn)
+				tempFile, err := os.CreateTemp(tempDir, fn)
 				if err != nil {
 					t.Fatalf("temp modelfile creation failed: %v", err)
 				}
@@ -525,7 +530,7 @@ func TestPushHandler(t *testing.T) {
 
 			cmd := &cobra.Command{}
 			cmd.Flags().Bool("insecure", false, "")
-			cmd.SetContext(t.Context())
+			cmd.SetContext(context.TODO())
 
 			// Redirect stderr to capture progress output
 			oldStderr := os.Stderr
@@ -630,7 +635,7 @@ func TestListHandler(t *testing.T) {
 			t.Setenv("OLLAMA_HOST", mockServer.URL)
 
 			cmd := &cobra.Command{}
-			cmd.SetContext(t.Context())
+			cmd.SetContext(context.TODO())
 
 			// Capture stdout
 			oldStdout := os.Stdout
@@ -685,7 +690,7 @@ func TestCreateHandler(t *testing.T) {
 						return
 					}
 
-					if req.Model != "test-model" {
+					if req.Name != "test-model" {
 						t.Errorf("expected model name 'test-model', got %s", req.Name)
 					}
 
@@ -725,7 +730,7 @@ func TestCreateHandler(t *testing.T) {
 			}))
 			t.Setenv("OLLAMA_HOST", mockServer.URL)
 			t.Cleanup(mockServer.Close)
-			tempFile, err := os.CreateTemp(t.TempDir(), "modelfile")
+			tempFile, err := os.CreateTemp("", "modelfile")
 			if err != nil {
 				t.Fatal(err)
 			}
@@ -745,7 +750,7 @@ func TestCreateHandler(t *testing.T) {
 			}
 
 			cmd.Flags().Bool("insecure", false, "")
-			cmd.SetContext(t.Context())
+			cmd.SetContext(context.TODO())
 
 			// Redirect stderr to capture progress output
 			oldStderr := os.Stderr

cmd/interactive.go

@@ -44,7 +44,7 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 		fmt.Fprintln(os.Stderr, "Use \"\"\" to begin a multi-line message.")
 
 		if opts.MultiModal {
-			fmt.Fprintf(os.Stderr, "Use %s to include .jpg, .png, or .webp images.\n", filepath.FromSlash("/path/to/file"))
+			fmt.Fprintf(os.Stderr, "Use %s to include .jpg or .png images.\n", filepath.FromSlash("/path/to/file"))
 		}
 
 		fmt.Fprintln(os.Stderr, "")
@@ -62,8 +62,6 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 		fmt.Fprintln(os.Stderr, "  /set noformat          Disable formatting")
 		fmt.Fprintln(os.Stderr, "  /set verbose           Show LLM stats")
 		fmt.Fprintln(os.Stderr, "  /set quiet             Disable LLM stats")
-		fmt.Fprintln(os.Stderr, "  /set think             Enable thinking")
-		fmt.Fprintln(os.Stderr, "  /set nothink           Disable thinking")
 		fmt.Fprintln(os.Stderr, "")
 	}
 
@@ -130,7 +128,6 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 
 	var sb strings.Builder
 	var multiline MultilineState
-	var thinkExplicitlySet bool = opts.Think != nil
 
 	for {
 		line, err := scanner.Readline()
@@ -198,19 +195,11 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 			opts.Model = args[1]
 			opts.Messages = []api.Message{}
 			fmt.Printf("Loading model '%s'\n", opts.Model)
-			opts.Think, err = inferThinkingOption(nil, &opts, thinkExplicitlySet)
-			if err != nil {
-				return err
-			}
 			if err := loadOrUnloadModel(cmd, &opts); err != nil {
 				if strings.Contains(err.Error(), "not found") {
 					fmt.Printf("error: %v\n", err)
 					continue
 				}
-				if strings.Contains(err.Error(), "does not support thinking") {
-					fmt.Printf("error: %v\n", err)
-					continue
-				}
 				return err
 			}
 			continue
@@ -271,22 +260,6 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 						return err
 					}
 					fmt.Println("Set 'quiet' mode.")
-				case "think":
-					think := true
-					opts.Think = &think
-					thinkExplicitlySet = true
-					if client, err := api.ClientFromEnvironment(); err == nil {
-						ensureThinkingSupport(cmd.Context(), client, opts.Model)
-					}
-					fmt.Println("Set 'think' mode.")
-				case "nothink":
-					think := false
-					opts.Think = &think
-					thinkExplicitlySet = true
-					if client, err := api.ClientFromEnvironment(); err == nil {
-						ensureThinkingSupport(cmd.Context(), client, opts.Model)
-					}
-					fmt.Println("Set 'nothink' mode.")
 				case "format":
 					if len(args) < 3 || args[2] != "json" {
 						fmt.Println("Invalid or missing format. For 'json' mode use '/set format json'")
@@ -475,11 +448,6 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 
 			assistant, err := chat(cmd, opts)
 			if err != nil {
-				if strings.Contains(err.Error(), "does not support thinking") {
-					fmt.Printf("error: %v\n", err)
-					sb.Reset()
-					continue
-				}
 				return err
 			}
 			if assistant != nil {
@@ -543,7 +511,7 @@ func extractFileNames(input string) []string {
 	// Regex to match file paths starting with optional drive letter, / ./ \ or .\ and include escaped or unescaped spaces (\ or %20)
 	// and followed by more characters and a file extension
 	// This will capture non filename strings, but we'll check for file existence to remove mismatches
-	regexPattern := `(?:[a-zA-Z]:)?(?:\./|/|\\)[\S\\ ]+?\.(?i:jpg|jpeg|png|webp)\b`
+	regexPattern := `(?:[a-zA-Z]:)?(?:\./|/|\\)[\S\\ ]+?\.(?i:jpg|jpeg|png)\b`
 	re := regexp.MustCompile(regexPattern)
 
 	return re.FindAllString(input, -1)
@@ -563,8 +531,6 @@ func extractFileData(input string) (string, []api.ImageData, error) {
 			return "", imgs, err
 		}
 		fmt.Fprintf(os.Stderr, "Added image '%s'\n", nfp)
-		input = strings.ReplaceAll(input, "'"+nfp+"'", "")
-		input = strings.ReplaceAll(input, "'"+fp+"'", "")
 		input = strings.ReplaceAll(input, fp, "")
 		imgs = append(imgs, data)
 	}
@@ -585,7 +551,7 @@ func getImageData(filePath string) ([]byte, error) {
 	}
 
 	contentType := http.DetectContentType(buf)
-	allowedTypes := []string{"image/jpeg", "image/jpg", "image/png", "image/webp"}
+	allowedTypes := []string{"image/jpeg", "image/jpg", "image/png"}
 	if !slices.Contains(allowedTypes, contentType) {
 		return nil, fmt.Errorf("invalid image type: %s", contentType)
 	}

cmd/interactive_test.go

@@ -1,8 +1,6 @@
 package cmd
 
 import (
-	"os"
-	"path/filepath"
 	"testing"
 
 	"github.com/stretchr/testify/assert"
@@ -12,17 +10,14 @@ func TestExtractFilenames(t *testing.T) {
 	// Unix style paths
 	input := ` some preamble 
  ./relative\ path/one.png inbetween1 ./not a valid two.jpg inbetween2 ./1.svg
-/unescaped space /three.jpeg inbetween3 /valid\ path/dir/four.png "./quoted with spaces/five.JPG
-/unescaped space /six.webp inbetween6 /valid\ path/dir/seven.WEBP`
+/unescaped space /three.jpeg inbetween3 /valid\ path/dir/four.png "./quoted with spaces/five.JPG`
 	res := extractFileNames(input)
-	assert.Len(t, res, 7)
+	assert.Len(t, res, 5)
 	assert.Contains(t, res[0], "one.png")
 	assert.Contains(t, res[1], "two.jpg")
 	assert.Contains(t, res[2], "three.jpeg")
 	assert.Contains(t, res[3], "four.png")
 	assert.Contains(t, res[4], "five.JPG")
-	assert.Contains(t, res[5], "six.webp")
-	assert.Contains(t, res[6], "seven.WEBP")
 	assert.NotContains(t, res[4], '"')
 	assert.NotContains(t, res, "inbetween1")
 	assert.NotContains(t, res, "./1.svg")
@@ -33,12 +28,10 @@ func TestExtractFilenames(t *testing.T) {
  /absolute/nospace/three.jpeg inbetween3 /absolute/with space/four.png inbetween4
 ./relative\ path/five.JPG inbetween5 "./relative with/spaces/six.png inbetween6
 d:\path with\spaces\seven.JPEG inbetween7 c:\users\jdoe\eight.png inbetween8 
- d:\program files\someplace\nine.png inbetween9 "E:\program files\someplace\ten.PNG
-c:/users/jdoe/eleven.webp inbetween11 c:/program files/someplace/twelve.WebP inbetween12
-d:\path with\spaces\thirteen.WEBP some ending
+ d:\program files\someplace\nine.png inbetween9 "E:\program files\someplace\ten.PNG some ending
 `
 	res = extractFileNames(input)
-	assert.Len(t, res, 13)
+	assert.Len(t, res, 10)
 	assert.NotContains(t, res, "inbetween2")
 	assert.Contains(t, res[0], "one.png")
 	assert.Contains(t, res[0], "c:")
@@ -56,31 +49,4 @@ d:\path with\spaces\thirteen.WEBP some ending
 	assert.Contains(t, res[8], "d:")
 	assert.Contains(t, res[9], "ten.PNG")
 	assert.Contains(t, res[9], "E:")
-	assert.Contains(t, res[10], "eleven.webp")
-	assert.Contains(t, res[10], "c:")
-	assert.Contains(t, res[11], "twelve.WebP")
-	assert.Contains(t, res[11], "c:")
-	assert.Contains(t, res[12], "thirteen.WEBP")
-	assert.Contains(t, res[12], "d:")
-}
-
-// Ensure that file paths wrapped in single quotes are removed with the quotes.
-func TestExtractFileDataRemovesQuotedFilepath(t *testing.T) {
-	dir := t.TempDir()
-	fp := filepath.Join(dir, "img.jpg")
-	data := make([]byte, 600)
-	copy(data, []byte{
-		0xff, 0xd8, 0xff, 0xe0, 0x00, 0x10, 'J', 'F', 'I', 'F',
-		0x00, 0x01, 0x01, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-		0xff, 0xd9,
-	})
-	if err := os.WriteFile(fp, data, 0o600); err != nil {
-		t.Fatalf("failed to write test image: %v", err)
-	}
-
-	input := "before '" + fp + "' after"
-	cleaned, imgs, err := extractFileData(input)
-	assert.NoError(t, err)
-	assert.Len(t, imgs, 1)
-	assert.Equal(t, cleaned, "before  after")
 }

cmd/start_darwin.go

@@ -23,7 +23,7 @@ func startApp(ctx context.Context, client *api.Client) error {
 		return errors.New("could not find ollama app")
 	}
 	path := strings.Split(link, "Ollama.app")
-	if err := exec.Command("/usr/bin/open", "-j", "-a", path[0]+"Ollama.app").Run(); err != nil {
+	if err := exec.Command("/usr/bin/open", "-a", path[0]+"Ollama.app").Run(); err != nil {
 		return err
 	}
 	return waitForServer(ctx, client)

cmd/start_windows.go

@@ -4,27 +4,17 @@ import (
 	"context"
 	"errors"
 	"fmt"
-	"log/slog"
 	"os"
 	"os/exec"
-	"path"
 	"path/filepath"
 	"strings"
 	"syscall"
-	"unsafe"
 
 	"github.com/ollama/ollama/api"
-	"golang.org/x/sys/windows"
-)
-
-const (
-	Installer = "OllamaSetup.exe"
 )
 
 func startApp(ctx context.Context, client *api.Client) error {
-	if len(isProcRunning(Installer)) > 0 {
-		return fmt.Errorf("upgrade in progress...")
-	}
+	// log.Printf("XXX Attempting to find and start ollama app")
 	AppName := "ollama app.exe"
 	exe, err := os.Executable()
 	if err != nil {
@@ -45,11 +35,14 @@ func startApp(ctx context.Context, client *api.Client) error {
 			}
 		}
 	}
+	// log.Printf("XXX attempting to start app %s", appExe)
 
 	cmd_path := "c:\\Windows\\system32\\cmd.exe"
-	cmd := exec.Command(cmd_path, "/c", appExe, "hidden")
+	cmd := exec.Command(cmd_path, "/c", appExe)
+	// TODO - these hide flags aren't working - still pops up a command window for some reason
 	cmd.SysProcAttr = &syscall.SysProcAttr{CreationFlags: 0x08000000, HideWindow: true}
 
+	// TODO this didn't help either...
 	cmd.Stdin = strings.NewReader("")
 	cmd.Stdout = os.Stdout
 	cmd.Stderr = os.Stderr
@@ -63,50 +56,3 @@ func startApp(ctx context.Context, client *api.Client) error {
 	}
 	return waitForServer(ctx, client)
 }
-
-func isProcRunning(procName string) []uint32 {
-	pids := make([]uint32, 2048)
-	var ret uint32
-	if err := windows.EnumProcesses(pids, &ret); err != nil || ret == 0 {
-		slog.Debug("failed to check for running installers", "error", err)
-		return nil
-	}
-	if ret > uint32(len(pids)) {
-		pids = make([]uint32, ret+10)
-		if err := windows.EnumProcesses(pids, &ret); err != nil || ret == 0 {
-			slog.Debug("failed to check for running installers", "error", err)
-			return nil
-		}
-	}
-	if ret < uint32(len(pids)) {
-		pids = pids[:ret]
-	}
-	var matches []uint32
-	for _, pid := range pids {
-		if pid == 0 {
-			continue
-		}
-		hProcess, err := windows.OpenProcess(windows.PROCESS_QUERY_INFORMATION|windows.PROCESS_VM_READ, false, pid)
-		if err != nil {
-			continue
-		}
-		defer windows.CloseHandle(hProcess)
-		var module windows.Handle
-		var cbNeeded uint32
-		cb := (uint32)(unsafe.Sizeof(module))
-		if err := windows.EnumProcessModules(hProcess, &module, cb, &cbNeeded); err != nil {
-			continue
-		}
-		var sz uint32 = 1024 * 8
-		moduleName := make([]uint16, sz)
-		cb = uint32(len(moduleName)) * (uint32)(unsafe.Sizeof(uint16(0)))
-		if err := windows.GetModuleBaseName(hProcess, module, &moduleName[0], cb); err != nil && err != syscall.ERROR_INSUFFICIENT_BUFFER {
-			continue
-		}
-		exeFile := path.Base(strings.ToLower(syscall.UTF16ToString(moduleName)))
-		if strings.EqualFold(exeFile, procName) {
-			matches = append(matches, pid)
-		}
-	}
-	return matches
-}

cmd/warn_thinking_test.go

@@ -1,63 +0,0 @@
-package cmd
-
-import (
-	"encoding/json"
-	"io"
-	"net/http"
-	"net/http/httptest"
-	"os"
-	"strings"
-	"testing"
-
-	"github.com/ollama/ollama/api"
-	"github.com/ollama/ollama/types/model"
-)
-
-// Test that a warning is printed when thinking is requested but not supported.
-func TestWarnMissingThinking(t *testing.T) {
-	cases := []struct {
-		capabilities []model.Capability
-		expectWarn   bool
-	}{
-		{capabilities: []model.Capability{model.CapabilityThinking}, expectWarn: false},
-		{capabilities: []model.Capability{}, expectWarn: true},
-	}
-
-	for _, tc := range cases {
-		srv := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
-			if r.URL.Path != "/api/show" || r.Method != http.MethodPost {
-				t.Fatalf("unexpected request to %s %s", r.URL.Path, r.Method)
-			}
-			var req api.ShowRequest
-			if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
-				t.Fatalf("decode request: %v", err)
-			}
-			resp := api.ShowResponse{Capabilities: tc.capabilities}
-			if err := json.NewEncoder(w).Encode(resp); err != nil {
-				t.Fatalf("encode response: %v", err)
-			}
-		}))
-		defer srv.Close()
-
-		t.Setenv("OLLAMA_HOST", srv.URL)
-		client, err := api.ClientFromEnvironment()
-		if err != nil {
-			t.Fatal(err)
-		}
-		oldStderr := os.Stderr
-		r, w, _ := os.Pipe()
-		os.Stderr = w
-		ensureThinkingSupport(t.Context(), client, "m")
-		w.Close()
-		os.Stderr = oldStderr
-		out, _ := io.ReadAll(r)
-
-		warned := strings.Contains(string(out), "warning:")
-		if tc.expectWarn && !warned {
-			t.Errorf("expected warning, got none")
-		}
-		if !tc.expectWarn && warned {
-			t.Errorf("did not expect warning, got: %s", string(out))
-		}
-	}
-}

convert/convert.go

@@ -1,13 +1,12 @@
 package convert
 
 import (
-	"cmp"
 	"encoding/json"
 	"errors"
 	"fmt"
+	"io"
 	"io/fs"
 	"log/slog"
-	"os"
 	"slices"
 	"strings"
 
@@ -15,12 +14,13 @@ import (
 )
 
 type ModelParameters struct {
-	Architectures []string `json:"architectures"`
-	VocabSize     uint32   `json:"vocab_size"`
+	Architectures []string       `json:"architectures"`
+	VocabSize     uint32         `json:"vocab_size"`
+	TextModel     TextParameters `json:"text_config"`
+}
 
-	TextModel struct {
-		VocabSize uint32 `json:"vocab_size"`
-	} `json:"text_config"`
+type TextParameters struct {
+	VocabSize uint32 `json:"vocab_size"`
 }
 
 type AdapterParameters struct {
@@ -53,11 +53,8 @@ func (ModelParameters) KV(t *Tokenizer) ggml.KV {
 	}
 
 	for _, sv := range t.SpecialVocabulary {
-		kv[fmt.Sprintf("tokenizer.ggml.add_%s_token", sv.Key())] = sv.AddToken
 		kv[fmt.Sprintf("tokenizer.ggml.%s_token_id", sv.Key())] = uint32(sv.ID)
-		if len(sv.IDs) > 0 {
-			kv[fmt.Sprintf("tokenizer.ggml.%s_token_ids", sv.Key())] = sv.IDs
-		}
+		kv[fmt.Sprintf("tokenizer.ggml.add_%s_token", sv.Key())] = sv.AddToken
 	}
 
 	return kv
@@ -92,7 +89,7 @@ type ModelConverter interface {
 	// KV maps parameters to LLM key-values
 	KV(*Tokenizer) ggml.KV
 	// Tensors maps input tensors to LLM tensors. Model specific modifications can be done here.
-	Tensors([]Tensor) []*ggml.Tensor
+	Tensors([]Tensor) []ggml.Tensor
 	// Replacements returns a list of string pairs to replace in tensor names.
 	// See [strings.Replacer](https://pkg.go.dev/strings#Replacer) for details
 	Replacements() []string
@@ -109,13 +106,13 @@ type AdapterConverter interface {
 	// KV maps parameters to LLM key-values
 	KV(ggml.KV) ggml.KV
 	// Tensors maps input tensors to LLM tensors. Adapter specific modifications can be done here.
-	Tensors([]Tensor) []*ggml.Tensor
+	Tensors([]Tensor) []ggml.Tensor
 	// Replacements returns a list of string pairs to replace in tensor names.
 	// See [strings.Replacer](https://pkg.go.dev/strings#Replacer) for details
 	Replacements() []string
 }
 
-func ConvertAdapter(fsys fs.FS, f *os.File, baseKV ggml.KV) error {
+func ConvertAdapter(fsys fs.FS, ws io.WriteSeeker, baseKV ggml.KV) error {
 	bts, err := fs.ReadFile(fsys, "adapter_config.json")
 	if err != nil {
 		return err
@@ -150,14 +147,14 @@ func ConvertAdapter(fsys fs.FS, f *os.File, baseKV ggml.KV) error {
 		return err
 	}
 
-	return writeFile(f, conv.KV(baseKV), conv.Tensors(ts))
+	return writeFile(ws, 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 ConvertModel(fsys fs.FS, ws io.WriteSeeker) error {
 	bts, err := fs.ReadFile(fsys, "config.json")
 	if err != nil {
 		return err
@@ -176,8 +173,6 @@ func ConvertModel(fsys fs.FS, f *os.File) error {
 	switch p.Architectures[0] {
 	case "LlamaForCausalLM":
 		conv = &llamaModel{}
-	case "MllamaForConditionalGeneration":
-		conv = &mllamaModel{}
 	case "Llama4ForConditionalGeneration":
 		conv = &llama4Model{}
 	case "Mistral3ForConditionalGeneration":
@@ -194,8 +189,6 @@ func ConvertModel(fsys fs.FS, f *os.File) error {
 		conv = &phi3Model{}
 	case "Qwen2ForCausalLM":
 		conv = &qwen2Model{}
-	case "Qwen2_5_VLForConditionalGeneration":
-		conv = &qwen25VLModel{}
 	case "BertModel":
 		conv = &bertModel{}
 	case "CohereForCausalLM":
@@ -219,22 +212,24 @@ func ConvertModel(fsys fs.FS, f *os.File) error {
 		return err
 	}
 
-	vocabSize := int(cmp.Or(p.VocabSize, p.TextModel.VocabSize))
+	vocabSize := int(p.VocabSize)
+	if vocabSize == 0 {
+		tVocabSize := int(p.TextModel.VocabSize)
+		vocabSize = tVocabSize
+	}
 
 	switch {
 	case vocabSize == 0:
-		slog.Debug("vocabulary size was not explicitly set by the model", "default size", len(t.Vocabulary.Tokens))
+		slog.Warn("vocabulary size was not explicitly set by the model", "default size", len(t.Vocabulary.Tokens))
 	case vocabSize > len(t.Vocabulary.Tokens):
-		slog.Debug("vocabulary is smaller than expected, padding with dummy tokens", "expect", vocabSize, "actual", len(t.Vocabulary.Tokens))
+		slog.Warn("vocabulary is smaller than expected, padding with dummy tokens", "expect", vocabSize, "actual", len(t.Vocabulary.Tokens))
 		for i := range vocabSize - len(t.Vocabulary.Tokens) {
 			t.Vocabulary.Tokens = append(t.Vocabulary.Tokens, fmt.Sprintf("[PAD%d]", i))
 			t.Vocabulary.Scores = append(t.Vocabulary.Scores, -1)
 			t.Vocabulary.Types = append(t.Vocabulary.Types, tokenTypeUserDefined)
 		}
 	case vocabSize < len(t.Vocabulary.Tokens):
-		slog.Debug("vocabulary is larger than expected", "want", vocabSize, "got", len(t.Vocabulary.Tokens))
-		p.VocabSize = uint32(len(t.Vocabulary.Tokens))
-		p.TextModel.VocabSize = uint32(len(t.Vocabulary.Tokens))
+		return fmt.Errorf("vocabulary is larger than expected '%d' instead of '%d'", len(t.Vocabulary.Tokens), vocabSize)
 	default:
 		slog.Debug("vocabulary", "size", len(t.Vocabulary.Tokens))
 	}
@@ -244,13 +239,13 @@ func ConvertModel(fsys fs.FS, f *os.File) error {
 		return err
 	}
 
-	return writeFile(f, conv.KV(t), conv.Tensors(ts))
+	return writeFile(ws, conv.KV(t), conv.Tensors(ts))
 }
 
-func writeFile(f *os.File, kv ggml.KV, ts []*ggml.Tensor) error {
+func writeFile(ws io.WriteSeeker, kv ggml.KV, ts []ggml.Tensor) error {
 	for i := range ts {
 		ts[i].Shape = slices.Clone(ts[i].Shape)
 		slices.Reverse(ts[i].Shape)
 	}
-	return ggml.WriteGGUF(f, kv, ts)
+	return ggml.WriteGGUF(ws, kv, ts)
 }

convert/convert_bert.go

@@ -132,8 +132,8 @@ func (p *bertModel) KV(t *Tokenizer) ggml.KV {
 	return kv
 }
 
-func (p *bertModel) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []*ggml.Tensor
+func (p *bertModel) Tensors(ts []Tensor) []ggml.Tensor {
+	var out []ggml.Tensor
 	for _, t := range ts {
 		if slices.Contains([]string{
 			"embeddings.position_ids",
@@ -143,7 +143,7 @@ func (p *bertModel) Tensors(ts []Tensor) []*ggml.Tensor {
 			continue
 		}
 
-		out = append(out, &ggml.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_commandr.go

@@ -43,10 +43,10 @@ func (p *commandrModel) KV(t *Tokenizer) ggml.KV {
 	return kv
 }
 
-func (p *commandrModel) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []*ggml.Tensor
+func (p *commandrModel) Tensors(ts []Tensor) []ggml.Tensor {
+	var out []ggml.Tensor
 	for _, t := range ts {
-		out = append(out, &ggml.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_gemma.go

@@ -42,14 +42,14 @@ func (p *gemmaModel) KV(t *Tokenizer) ggml.KV {
 	return kv
 }
 
-func (p *gemmaModel) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []*ggml.Tensor
+func (p *gemmaModel) Tensors(ts []Tensor) []ggml.Tensor {
+	var out []ggml.Tensor
 	for _, t := range ts {
 		if !strings.HasPrefix(t.Name(), "v.") && strings.HasSuffix(t.Name(), "_norm.weight") {
 			t.SetRepacker(p.addOne)
 		}
 
-		out = append(out, &ggml.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_gemma2_adapter.go

@@ -21,8 +21,8 @@ func (p *gemma2Adapter) KV(baseKV ggml.KV) ggml.KV {
 	return kv
 }
 
-func (p *gemma2Adapter) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []*ggml.Tensor
+func (p *gemma2Adapter) Tensors(ts []Tensor) []ggml.Tensor {
+	var out []ggml.Tensor
 	for _, t := range ts {
 		shape := t.Shape()
 		if (strings.HasSuffix(t.Name(), "weight.lora_a") && shape[0] > shape[1]) ||
@@ -31,7 +31,7 @@ func (p *gemma2Adapter) Tensors(ts []Tensor) []*ggml.Tensor {
 			t.SetRepacker(p.repack)
 		}
 
-		out = append(out, &ggml.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_llama.go

@@ -126,11 +126,11 @@ func (p *llamaModel) KV(t *Tokenizer) ggml.KV {
 	return kv
 }
 
-func (p *llamaModel) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []*ggml.Tensor
+func (p *llamaModel) Tensors(ts []Tensor) []ggml.Tensor {
+	var out []ggml.Tensor
 
 	if p.RopeScaling.factors != nil {
-		out = append(out, &ggml.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     "rope_freqs.weight",
 			Kind:     0,
 			Shape:    []uint64{uint64(len(p.RopeScaling.factors))},
@@ -139,14 +139,13 @@ func (p *llamaModel) Tensors(ts []Tensor) []*ggml.Tensor {
 	}
 
 	for _, t := range ts {
-		if strings.HasSuffix(t.Name(), "attn_q.weight") || strings.HasSuffix(t.Name(), "attn_k.weight") ||
-			strings.HasSuffix(t.Name(), "attn_q_proj.weight") || strings.HasSuffix(t.Name(), "attn_k_proj.weight") {
+		if strings.HasSuffix(t.Name(), "attn_q.weight") || strings.HasSuffix(t.Name(), "attn_k.weight") {
 			if !p.skipRepack {
 				t.SetRepacker(p.repack)
 			}
 		}
 
-		out = append(out, &ggml.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),
@@ -182,9 +181,9 @@ func (p *llamaModel) repack(name string, data []float32, shape []uint64) ([]floa
 	}
 
 	var heads uint32
-	if strings.HasSuffix(name, "attn_q.weight") || strings.HasSuffix(name, "attn_q_proj.weight") {
+	if strings.HasSuffix(name, "attn_q.weight") {
 		heads = p.NumAttentionHeads
-	} else if strings.HasSuffix(name, "attn_k.weight") || strings.HasSuffix(name, "attn_k_proj.weight") {
+	} else if strings.HasSuffix(name, "attn_k.weight") {
 		heads = cmp.Or(p.NumKeyValueHeads, p.NumAttentionHeads)
 	} else {
 		return nil, fmt.Errorf("unknown tensor for repack: %s", name)

convert/convert_llama4.go

@@ -88,13 +88,13 @@ func (p *llama4Model) Replacements() []string {
 }
 
 // Tensors implements ModelConverter.
-func (p *llama4Model) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []*ggml.Tensor
+func (p *llama4Model) Tensors(ts []Tensor) []ggml.Tensor {
+	var out []ggml.Tensor
 
 	var textTensors []Tensor
 	for _, t := range ts {
 		if strings.HasPrefix(t.Name(), "v.") || strings.HasPrefix(t.Name(), "mm.") {
-			out = append(out, &ggml.Tensor{
+			out = append(out, ggml.Tensor{
 				Name:     t.Name(),
 				Kind:     t.Kind(),
 				Shape:    t.Shape(),
@@ -112,7 +112,7 @@ func (p *llama4Model) Tensors(ts []Tensor) []*ggml.Tensor {
 				// clone tensor since we need separate repackers
 				tt := t.Clone()
 				tt.SetRepacker(p.repack(nil, nil, tensor.S(i*halfDim, (i+1)*halfDim)))
-				out = append(out, &ggml.Tensor{
+				out = append(out, ggml.Tensor{
 					Name:     strings.ReplaceAll(tt.Name(), "ffn_gate_up_exps", name),
 					Kind:     tt.Kind(),
 					Shape:    newShape,
@@ -125,7 +125,7 @@ func (p *llama4Model) Tensors(ts []Tensor) []*ggml.Tensor {
 			t.SetRepacker(p.repack())
 			newShape := slices.Clone(t.Shape())
 			newShape[1], newShape[2] = newShape[2], newShape[1]
-			out = append(out, &ggml.Tensor{
+			out = append(out, ggml.Tensor{
 				Name:     t.Name(),
 				Kind:     t.Kind(),
 				Shape:    newShape,

convert/convert_llama_adapter.go

@@ -29,8 +29,8 @@ func (p *llamaAdapter) KV(baseKV ggml.KV) ggml.KV {
 	return kv
 }
 
-func (p *llamaAdapter) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []*ggml.Tensor
+func (p *llamaAdapter) Tensors(ts []Tensor) []ggml.Tensor {
+	var out []ggml.Tensor
 	for _, t := range ts {
 		shape := t.Shape()
 		if (strings.HasSuffix(t.Name(), "weight.lora_a") && shape[0] > shape[1]) ||
@@ -41,7 +41,7 @@ func (p *llamaAdapter) Tensors(ts []Tensor) []*ggml.Tensor {
 			t.SetRepacker(p.repack)
 		}
 
-		out = append(out, &ggml.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    shape,

convert/convert_mistral.go

@@ -89,8 +89,8 @@ func (p *mistral3Model) KV(t *Tokenizer) ggml.KV {
 	return kv
 }
 
-func (p *mistral3Model) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []*ggml.Tensor
+func (p *mistral3Model) Tensors(ts []Tensor) []ggml.Tensor {
+	var out []ggml.Tensor
 
 	for _, t := range ts {
 		if !strings.HasPrefix(t.Name(), "v.") {
@@ -100,7 +100,7 @@ func (p *mistral3Model) Tensors(ts []Tensor) []*ggml.Tensor {
 			}
 		}
 
-		out = append(out, &ggml.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_mixtral.go

@@ -29,7 +29,7 @@ func (p *mixtralModel) KV(t *Tokenizer) ggml.KV {
 	return kv
 }
 
-func (p *mixtralModel) Tensors(ts []Tensor) []*ggml.Tensor {
+func (p *mixtralModel) Tensors(ts []Tensor) []ggml.Tensor {
 	oldnew := []string{
 		"model.layers", "blk",
 		"w1", "ffn_gate_exps",
@@ -56,10 +56,10 @@ func (p *mixtralModel) Tensors(ts []Tensor) []*ggml.Tensor {
 		return true
 	})
 
-	var out []*ggml.Tensor
+	var out []ggml.Tensor
 	for n, e := range experts {
 		// TODO(mxyng): sanity check experts
-		out = append(out, &ggml.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     n,
 			Kind:     e[0].Kind(),
 			Shape:    append([]uint64{uint64(len(e))}, e[0].Shape()...),

convert/convert_mllama.go

@@ -1,179 +0,0 @@
-package convert
-
-import (
-	"strings"
-
-	"github.com/ollama/ollama/fs/ggml"
-	"github.com/pdevine/tensor"
-	"github.com/pdevine/tensor/native"
-)
-
-type mllamaModel struct {
-	ModelParameters
-	TextModel struct {
-		llamaModel
-
-		CrossAttentionLayers []int32 `json:"cross_attention_layers"`
-	} `json:"text_config"`
-	VisionModel struct {
-		NumHiddenLayers           uint32  `json:"num_hidden_layers"`
-		NumGlobalLayers           uint32  `json:"num_global_layers"`
-		IntermediateLayersIndices []int32 `json:"intermediate_layers_indices"`
-
-		HiddenSize       uint32 `json:"hidden_size"`
-		IntermediateSize uint32 `json:"intermediate_size"`
-
-		AttentionHeads uint32 `json:"attention_heads"`
-
-		ImageSize   uint32  `json:"image_size"`
-		PatchSize   uint32  `json:"patch_size"`
-		NumChannels uint32  `json:"num_channels"`
-		MaxNumTiles uint32  `json:"max_num_tiles"`
-		NormEpsilon float32 `json:"norm_eps"`
-		RopeTheta   float32 `json:"rope.freq_base"`
-	} `json:"vision_config"`
-}
-
-func (m *mllamaModel) KV(t *Tokenizer) ggml.KV {
-	kv := m.ModelParameters.KV(t)
-	kv["general.architecture"] = "mllama"
-
-	for k, v := range m.TextModel.KV(t) {
-		if strings.HasPrefix(k, "llama.") {
-			kv[strings.ReplaceAll(k, "llama.", "mllama.")] = v
-		}
-	}
-
-	kv["mllama.attention.cross_attention_layers"] = m.TextModel.CrossAttentionLayers
-
-	kv["mllama.vision.block_count"] = m.VisionModel.NumHiddenLayers
-	kv["mllama.vision.global.block_count"] = m.VisionModel.NumGlobalLayers
-	kv["mllama.vision.intermediate_layers_indices"] = m.VisionModel.IntermediateLayersIndices
-
-	kv["mllama.vision.embedding_length"] = m.VisionModel.HiddenSize
-	kv["mllama.vision.feed_forward_length"] = m.VisionModel.IntermediateSize
-
-	kv["mllama.vision.attention.head_count"] = m.VisionModel.AttentionHeads
-	kv["mllama.vision.attention.layer_norm_epsilon"] = m.VisionModel.NormEpsilon
-
-	kv["mllama.vision.image_size"] = m.VisionModel.ImageSize
-	kv["mllama.vision.patch_size"] = m.VisionModel.PatchSize
-	kv["mllama.vision.max_num_tiles"] = m.VisionModel.MaxNumTiles
-	kv["mllama.vision.num_channels"] = m.VisionModel.NumChannels
-
-	return kv
-}
-
-func (m *mllamaModel) Replacements() []string {
-	return append(
-		m.TextModel.Replacements(),
-		"language_model.", "",
-		"gate_attn", "attn_gate",
-		"gate_ffn", "ffn_gate",
-		"cross_attn.", "cross_attn_",
-		"vision_model", "v",
-		"class_embedding", "class_embd",
-		"patch_embedding", "patch_embd",
-		"gated_positional_embedding.tile_embedding", "tile_position_embd",
-		"gated_positional_embedding.embedding", "position_embd.weight",
-		"gated_positional_embedding", "position_embd",
-		"embedding.weight", "weight",
-		"pre_tile_positional_embedding", "pre_tile_position_embd",
-		"post_tile_positional_embedding", "post_tile_position_embd",
-		"layernorm_pre", "pre_ln",
-		"layernorm_post", "post_ln",
-		"global_transformer.layers", "global.blk",
-		"transformer.layers", "blk",
-		"mlp.fc1", "ffn_up",
-		"mlp.fc2", "ffn_down",
-		"multi_modal_projector", "mm.0",
-	)
-}
-
-func (m *mllamaModel) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []*ggml.Tensor
-	var text []Tensor
-	for _, t := range ts {
-		if !strings.HasPrefix(t.Name(), "v.") && !strings.HasPrefix(t.Name(), "mm.") {
-			text = append(text, t)
-		} else if t.Name() == "v.position_embd.gate" {
-			for _, name := range []string{"v.position_embd.gate", "v.tile_position_embd.gate"} {
-				tt := t.Clone()
-				tt.SetRepacker(m.repack(name))
-				out = append(out, &ggml.Tensor{
-					Name:     name,
-					Kind:     t.Kind(),
-					Shape:    t.Shape(),
-					WriterTo: tt,
-				})
-			}
-		} else {
-			if t.Name() == "v.pre_tile_position_embd.gate" || t.Name() == "v.post_tile_position_embd.gate" {
-				t.SetRepacker(m.repack(t.Name()))
-			} else if strings.HasSuffix(t.Name(), "attn_q.weight") || strings.HasSuffix(t.Name(), "attn_k.weight") {
-				t.SetRepacker(m.repack(t.Name()))
-			} else if strings.HasSuffix(t.Name(), "attn_gate") || strings.HasSuffix(t.Name(), "ffn_gate") {
-				t.SetRepacker(m.repack(t.Name()))
-			}
-
-			out = append(out, &ggml.Tensor{
-				Name:     t.Name(),
-				Kind:     t.Kind(),
-				Shape:    t.Shape(),
-				WriterTo: t,
-			})
-		}
-	}
-
-	return append(out, m.TextModel.Tensors(text)...)
-}
-
-func (m *mllamaModel) repack(name string) Repacker {
-	return func(_ string, data []float32, shape []uint64) (_ []float32, err error) {
-		dims := make([]int, len(shape))
-		for i, dim := range shape {
-			dims[i] = int(dim)
-		}
-
-		var t tensor.Tensor = tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
-
-		if strings.HasSuffix(name, "attn_q.weight") || strings.HasSuffix(name, "attn_k.weight") {
-			heads := m.VisionModel.AttentionHeads
-			if err := t.Reshape(append([]int{int(heads), 2, dims[0] / int(heads) / 2}, dims[1:]...)...); err != nil {
-				return nil, err
-			}
-
-			if err := t.T(0, 2, 1, 3); err != nil {
-				return nil, err
-			}
-
-			if err := t.Reshape(dims...); err != nil {
-				return nil, err
-			}
-
-			if err := t.Transpose(); err != nil {
-				return nil, err
-			}
-		} else {
-			t, err = tensor.Tanh(t)
-			if err != nil {
-				return nil, err
-			}
-
-			if name == "v.position_embd.gate" {
-				t, err = tensor.Sub(float32(1), t)
-				if err != nil {
-					return nil, err
-				}
-			}
-		}
-
-		t = tensor.Materialize(t)
-		// flatten tensor so it can be return as a vector
-		if err := t.Reshape(t.Shape().TotalSize()); err != nil {
-			return nil, err
-		}
-
-		return native.VectorF32(t.(*tensor.Dense))
-	}
-}

convert/convert_phi3.go

@@ -68,19 +68,19 @@ func (p *phi3Model) KV(t *Tokenizer) ggml.KV {
 	return kv
 }
 
-func (p *phi3Model) Tensors(ts []Tensor) []*ggml.Tensor {
+func (p *phi3Model) Tensors(ts []Tensor) []ggml.Tensor {
 	var addRopeFactors sync.Once
 
-	out := make([]*ggml.Tensor, 0, len(ts)+2)
+	out := make([]ggml.Tensor, 0, len(ts)+2)
 	for _, t := range ts {
 		if strings.HasPrefix(t.Name(), "blk.0.") {
 			addRopeFactors.Do(func() {
-				out = append(out, &ggml.Tensor{
+				out = append(out, ggml.Tensor{
 					Name:     "rope_factors_long.weight",
 					Kind:     0,
 					Shape:    []uint64{uint64(len(p.RopeScaling.LongFactor))},
 					WriterTo: p.RopeScaling.LongFactor,
-				}, &ggml.Tensor{
+				}, ggml.Tensor{
 					Name:     "rope_factors_short.weight",
 					Kind:     0,
 					Shape:    []uint64{uint64(len(p.RopeScaling.ShortFactor))},
@@ -89,7 +89,7 @@ func (p *phi3Model) Tensors(ts []Tensor) []*ggml.Tensor {
 			})
 		}
 
-		out = append(out, &ggml.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_qwen2.go

@@ -15,7 +15,6 @@ type qwen2Model struct {
 		Type                          string     `json:"type"`
 		Factor                        ropeFactor `json:"factor"`
 		OriginalMaxPositionEmbeddings uint32     `json:"original_max_position_embeddings"`
-		MropeSection                  []int32    `json:"mrope_section"`
 	} `json:"rope_scaling"`
 	RMSNormEPS float32 `json:"rms_norm_eps"`
 }
@@ -40,18 +39,16 @@ func (q *qwen2Model) KV(t *Tokenizer) ggml.KV {
 	case "yarn":
 		kv["qwen2.rope.scaling.type"] = q.RopeScaling.Type
 		kv["qwen2.rope.scaling.factor"] = q.RopeScaling.Factor
-	case "mrope", "default":
-		kv["qwen2.rope.mrope_section"] = q.RopeScaling.MropeSection
 	default:
 		panic("unknown rope scaling type")
 	}
 	return kv
 }
 
-func (q *qwen2Model) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []*ggml.Tensor
+func (q *qwen2Model) Tensors(ts []Tensor) []ggml.Tensor {
+	var out []ggml.Tensor
 	for _, t := range ts {
-		out = append(out, &ggml.Tensor{
+		out = append(out, ggml.Tensor{
 			Name:     t.Name(),
 			Kind:     t.Kind(),
 			Shape:    t.Shape(),

convert/convert_qwen25vl.go

@@ -1,102 +0,0 @@
-package convert
-
-import (
-	"cmp"
-	"slices"
-	"strings"
-
-	"github.com/ollama/ollama/fs/ggml"
-)
-
-type qwen25VLModel struct {
-	qwen2Model
-
-	VisionModel struct {
-		Depth               uint32  `json:"depth"`
-		HiddenSize          uint32  `json:"hidden_size"`
-		NumHeads            uint32  `json:"num_heads"`
-		InChannels          uint32  `json:"in_chans"`
-		PatchSize           uint32  `json:"patch_size"`
-		SpatialMergeSize    uint32  `json:"spatial_merge_size"`
-		SpatialPatchSize    uint32  `json:"spatial_patch_size"`
-		WindowSize          uint32  `json:"window_size"`
-		RMSNormEps          float32 `json:"layer_norm_epsilon"`
-		RopeTheta           float32 `json:"rope_theta"`
-		FullAttentionBlocks []int32 `json:"fullatt_block_indexes"`
-		TemporalPatchSize   uint32  `json:"temporal_patch_size"`
-	} `json:"vision_config"`
-}
-
-var _ ModelConverter = (*qwen25VLModel)(nil)
-
-func (q *qwen25VLModel) KV(t *Tokenizer) ggml.KV {
-	kv := q.ModelParameters.KV(t)
-	kv["general.architecture"] = "qwen25vl"
-
-	for k, v := range q.qwen2Model.KV(t) {
-		if strings.HasPrefix(k, "qwen2.") {
-			kv[strings.Replace(k, "qwen2.", "qwen25vl.", 1)] = v
-		}
-	}
-
-	if q.VisionModel.FullAttentionBlocks == nil {
-		kv["qwen25vl.vision.fullatt_block_indexes"] = []int32{7, 15, 23, 31}
-	}
-
-	kv["qwen25vl.vision.block_count"] = cmp.Or(q.VisionModel.Depth, 32)
-	kv["qwen25vl.vision.embedding_length"] = q.VisionModel.HiddenSize
-	kv["qwen25vl.vision.attention.head_count"] = cmp.Or(q.VisionModel.NumHeads, 16)
-	kv["qwen25vl.vision.num_channels"] = q.VisionModel.InChannels
-	kv["qwen25vl.vision.patch_size"] = cmp.Or(q.VisionModel.PatchSize, 14)
-	kv["qwen25vl.vision.spatial_merge_size"] = cmp.Or(q.VisionModel.SpatialMergeSize, 2)
-	kv["qwen25vl.vision.spatial_patch_size"] = q.VisionModel.SpatialPatchSize
-	kv["qwen25vl.vision.window_size"] = cmp.Or(q.VisionModel.WindowSize, 112)
-	kv["qwen25vl.vision.attention.layer_norm_epsilon"] = cmp.Or(q.VisionModel.RMSNormEps, 1e-6)
-	kv["qwen25vl.vision.rope.freq_base"] = cmp.Or(q.VisionModel.RopeTheta, 1e4)
-	kv["qwen25vl.vision.fullatt_block_indexes"] = q.VisionModel.FullAttentionBlocks
-	kv["qwen25vl.vision.temporal_patch_size"] = cmp.Or(q.VisionModel.TemporalPatchSize, 2)
-
-	return kv
-}
-
-func (q *qwen25VLModel) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []*ggml.Tensor
-
-	for _, t := range ts {
-		if strings.Contains(t.Name(), "patch_embed.proj") {
-			for t := range splitDim(t, 2,
-				strings.NewReplacer("patch_embed.proj", "patch_embd_0"),
-				strings.NewReplacer("patch_embed.proj", "patch_embd_1"),
-			) {
-				t.Shape = slices.DeleteFunc(t.Shape, func(i uint64) bool { return i == 1 })
-				out = append(out, t)
-			}
-		} else if strings.Contains(t.Name(), "attn.qkv") {
-			out = append(out, slices.Collect(splitDim(t, 0,
-				strings.NewReplacer("attn.qkv", "attn_q"),
-				strings.NewReplacer("attn.qkv", "attn_k"),
-				strings.NewReplacer("attn.qkv", "attn_v"),
-			))...)
-		} else {
-			out = append(out, &ggml.Tensor{
-				Name:     t.Name(),
-				Kind:     t.Kind(),
-				Shape:    t.Shape(),
-				WriterTo: t,
-			})
-		}
-	}
-
-	return out
-}
-
-func (p *qwen25VLModel) Replacements() []string {
-	return append(
-		p.qwen2Model.Replacements(),
-		"visual", "v",
-		"blocks", "blk",
-		"attn.proj", "attn_out",
-		"norm1", "ln1",
-		"norm2", "ln2",
-	)
-}

convert/convert_test.go

@@ -47,7 +47,7 @@ func convertFull(t *testing.T, fsys fs.FS) (*os.File, ggml.KV, ggml.Tensors) {
 	}
 	t.Cleanup(func() { r.Close() })
 
-	m, err := ggml.Decode(r, -1)
+	m, _, err := ggml.Decode(r, -1)
 	if err != nil {
 		t.Fatal(err)
 	}
@@ -130,7 +130,6 @@ func TestConvertModel(t *testing.T) {
 			if err != nil {
 				t.Fatal(err)
 			}
-			defer expectFile.Close()
 
 			var expect map[string]string
 			if err := json.NewDecoder(expectFile).Decode(&expect); err != nil {
@@ -332,7 +331,7 @@ func TestConvertAdapter(t *testing.T) {
 			}
 			defer r.Close()
 
-			m, err := ggml.Decode(r, -1)
+			m, _, err := ggml.Decode(r, -1)
 			if err != nil {
 				t.Fatal(err)
 			}

convert/fs.go

@@ -0,0 +1,58 @@
+package convert
+
+import (
+	"archive/zip"
+	"errors"
+	"io"
+	"io/fs"
+	"os"
+	"path/filepath"
+)
+
+type ZipReader struct {
+	r *zip.Reader
+	p string
+
+	// limit is the maximum size of a file that can be read directly
+	// from the zip archive. Files larger than this size will be extracted
+	limit int64
+}
+
+func NewZipReader(r *zip.Reader, p string, limit int64) fs.FS {
+	return &ZipReader{r, p, limit}
+}
+
+func (z *ZipReader) Open(name string) (fs.File, error) {
+	r, err := z.r.Open(name)
+	if err != nil {
+		return nil, err
+	}
+	defer r.Close()
+
+	if fi, err := r.Stat(); err != nil {
+		return nil, err
+	} else if fi.Size() < z.limit {
+		return r, nil
+	}
+
+	if !filepath.IsLocal(name) {
+		return nil, zip.ErrInsecurePath
+	}
+
+	n := filepath.Join(z.p, name)
+	if _, err := os.Stat(n); errors.Is(err, os.ErrNotExist) {
+		w, err := os.Create(n)
+		if err != nil {
+			return nil, err
+		}
+		defer w.Close()
+
+		if _, err := io.Copy(w, r); err != nil {
+			return nil, err
+		}
+	} else if err != nil {
+		return nil, err
+	}
+
+	return os.Open(n)
+}

convert/reader.go

@@ -38,10 +38,7 @@ const (
 func (t tensorBase) Kind() uint32 {
 	if strings.HasSuffix(t.name, ".ffn_gate_inp.weight") ||
 		t.name == "token_types.weight" ||
-		t.name == "v.positional_embedding_vlm" ||
-		t.name == "v.tile_position_embd.weight" ||
-		t.name == "v.pre_tile_position_embd.weight" ||
-		t.name == "v.post_tile_position_embd.weight" {
+		t.name == "v.positional_embedding_vlm" {
 		// these tensors are always F32
 		return 0
 	}

convert/tensor.go

@@ -1,56 +0,0 @@
-package convert
-
-import (
-	"iter"
-	"slices"
-	"strings"
-
-	"github.com/ollama/ollama/fs/ggml"
-	"github.com/pdevine/tensor"
-	"github.com/pdevine/tensor/native"
-)
-
-// splitDim splits a tensor along a specified dimension into multiple tensors. The dimension
-// is split evenly based on the number of replacers provided.
-func splitDim(t Tensor, dim int, replacers ...*strings.Replacer) iter.Seq[*ggml.Tensor] {
-	return func(yield func(*ggml.Tensor) bool) {
-		for i, replacer := range replacers {
-			shape := slices.Clone(t.Shape())
-			shape[dim] = shape[dim] / uint64(len(replacers))
-
-			slice := slices.Repeat([]tensor.Slice{nil}, len(shape))
-			slice[dim] = tensor.S(i*int(shape[dim]), (i+1)*int(shape[dim]))
-
-			tt := t.Clone()
-			tt.SetRepacker(func(_ string, data []float32, shape []uint64) ([]float32, error) {
-				dims := make([]int, len(shape))
-				for i := range shape {
-					dims[i] = int(shape[i])
-				}
-
-				var t tensor.Tensor = tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
-				t, err := t.Slice(slice...)
-				if err != nil {
-					return nil, err
-				}
-
-				t = tensor.Materialize(t)
-				// flatten tensor so it can be written as a vector
-				if err := t.Reshape(t.Shape().TotalSize()); err != nil {
-					return nil, err
-				}
-
-				return native.VectorF32(t.(*tensor.Dense))
-			})
-
-			if !yield(&ggml.Tensor{
-				Name:     replacer.Replace(t.Name()),
-				Kind:     t.Kind(),
-				Shape:    shape,
-				WriterTo: tt,
-			}) {
-				break
-			}
-		}
-	}
-}

convert/tokenizer.go

@@ -110,7 +110,6 @@ func parseTokenizer(fsys fs.FS, specialTokenTypes []string) (*Tokenizer, error)
 	}
 
 	if f, err := fsys.Open("tokenizer_config.json"); errors.Is(err, os.ErrNotExist) {
-		// noop
 	} else if err != nil {
 		return nil, err
 	} else {
@@ -172,34 +171,6 @@ func parseTokenizer(fsys fs.FS, specialTokenTypes []string) (*Tokenizer, error)
 		}
 	}
 
-	if f, err := fsys.Open("generation_config.json"); errors.Is(err, os.ErrNotExist) {
-	} else if err != nil {
-		return nil, err
-	} else {
-		defer f.Close()
-
-		var p map[string]json.RawMessage
-		if err := json.NewDecoder(f).Decode(&p); err != nil {
-			return nil, err
-		}
-
-		for _, st := range specialTokenTypes {
-			if bts, ok := p[fmt.Sprintf("%s_token_id", st)]; ok {
-				var ids []int32
-				if err := json.Unmarshal(bts, &ids); err != nil {
-					// value is not a list so the existing ID is used
-					continue
-				}
-
-				if i := slices.IndexFunc(t.SpecialVocabulary, func(sv *SpecialVocabulary) bool {
-					return sv.Type == st
-				}); i >= 0 {
-					t.SpecialVocabulary[i].IDs = ids
-				}
-			}
-		}
-	}
-
 	return t, nil
 }
 
@@ -309,9 +280,6 @@ type SpecialVocabulary struct {
 	ID       int
 	Content  string
 	AddToken bool
-
-	// IDs is populated by generation_config.json
-	IDs []int32
 }
 
 func (sv SpecialVocabulary) Key() string {

convert/tokenizer_test.go

@@ -247,67 +247,6 @@ func TestParseTokenizer(t *testing.T) {
 				Pre: "default",
 			},
 		},
-		{
-			name: "generation config eos token ids",
-			fsys: createTokenizerFS(t, t.TempDir(), map[string]io.Reader{
-				"tokenizer.json": strings.NewReader(`{
-					"added_tokens": [
-						{
-							"id": 0,
-							"content": "<bos>",
-							"special": true
-						},
-						{
-							"id": 1,
-							"content": "<eos>",
-							"special": true
-						},
-						{
-							"id": 2,
-							"content": "<eot>",
-							"special": true
-						},
-						{
-							"id": 3,
-							"content": "<eom>",
-							"special": true
-						}
-					],
-					"model": {
-						"vocab": {
-							"<bos>": 0,
-							"<eos>": 1,
-							"<eot>": 2,
-							"<eom>": 3
-						}
-					}
-				}`),
-				"tokenizer_config.json": strings.NewReader(`{
-					"add_bos_token": true,
-					"add_eos_token": false,
-					"bos_token": "<bos>",
-					"eos_token": "<eos>"
-				}`),
-				"generation_config.json": strings.NewReader(`{
-					"bos_token_id": 0,
-					"eos_token_id": [1, 2, 3]
-				}`),
-			}),
-			specialTokenTypes: []string{"pad", "eos", "bos", "unk"},
-			want: &Tokenizer{
-				Vocabulary: &Vocabulary{
-					Model:  "gpt2",
-					Tokens: []string{"<bos>", "<eos>", "<eot>", "<eom>"},
-					Scores: []float32{0, 1, 2, 3},
-					Types:  []int32{3, 3, 3, 3},
-				},
-				SpecialVocabulary: []*SpecialVocabulary{
-					{Type: "eos", Content: "<eos>", ID: 1, IDs: []int32{1, 2, 3}, AddToken: false},
-					{Type: "bos", Content: "<bos>", ID: 0, AddToken: true},
-				},
-				Pre: "default",
-			},
-		},
 	}
 
 	for _, tt := range cases {

discover/gpu.go

@@ -670,7 +670,7 @@ func loadOneapiMgmt(oneapiLibPaths []string) (int, *C.oneapi_handle_t, string, e
 }
 
 func getVerboseState() C.uint16_t {
-	if envconfig.LogLevel() < slog.LevelInfo {
+	if envconfig.Debug() {
 		return C.uint16_t(1)
 	}
 	return C.uint16_t(0)

discover/gpu_info.h

@@ -27,14 +27,12 @@
 
 #endif
 
-#ifndef LOG
 #define LOG(verbose, ...) \
   do { \
     if (verbose) { \
       fprintf(stderr, __VA_ARGS__); \
     } \
   } while (0)
-#endif
 
 #ifdef __cplusplus
 extern "C" {

discover/gpu_info_cudart.c

@@ -1,7 +1,6 @@
 #ifndef __APPLE__  // TODO - maybe consider nvidia support on intel macs?
 
 #include <string.h>
-#include <inttypes.h>
 #include "gpu_info_cudart.h"
 
 void cudart_init(char *cudart_lib_path, cudart_init_resp_t *resp) {
@@ -59,7 +58,7 @@ void cudart_init(char *cudart_lib_path, cudart_init_resp_t *resp) {
     LOG(resp->ch.verbose, "cudaSetDevice err: %d\n", ret);
     UNLOAD_LIBRARY(resp->ch.handle);
     resp->ch.handle = NULL;
-    if (ret == CUDART_ERROR_INSUFFICIENT_DRIVER) {
+    if (ret == CUDA_ERROR_INSUFFICIENT_DRIVER) {
       resp->err = strdup("your nvidia driver is too old or missing.  If you have a CUDA GPU please upgrade to run ollama");
       return;
     }
@@ -169,9 +168,9 @@ void cudart_bootstrap(cudart_handle_t h, int i, mem_info_t *resp) {
   resp->free = memInfo.free;
   resp->used = memInfo.used;
 
-  LOG(h.verbose, "[%s] CUDA totalMem %" PRId64 "\n", resp->gpu_id, resp->total);
-  LOG(h.verbose, "[%s] CUDA freeMem %" PRId64 "\n", resp->gpu_id, resp->free);
-  LOG(h.verbose, "[%s] CUDA usedMem %" PRId64 "\n", resp->gpu_id, resp->used);
+  LOG(h.verbose, "[%s] CUDA totalMem %lu\n", resp->gpu_id, resp->total);
+  LOG(h.verbose, "[%s] CUDA freeMem %lu\n", resp->gpu_id, resp->free);
+  LOG(h.verbose, "[%s] CUDA usedMem %lu\n", resp->gpu_id, resp->used);
   LOG(h.verbose, "[%s] Compute Capability %d.%d\n", resp->gpu_id, resp->major, resp->minor);
 }
 
@@ -181,4 +180,4 @@ void cudart_release(cudart_handle_t h) {
   h.handle = NULL;
 }
 
-#endif  // __APPLE__
+#endif  // __APPLE__

discover/gpu_info_nvcuda.c

@@ -1,7 +1,6 @@
 #ifndef __APPLE__  // TODO - maybe consider nvidia support on intel macs?
 
 #include <string.h>
-#include <inttypes.h>
 #include "gpu_info_nvcuda.h"
 
 void nvcuda_init(char *nvcuda_lib_path, nvcuda_init_resp_t *resp) {
@@ -194,8 +193,8 @@ void nvcuda_bootstrap(nvcuda_handle_t h, int i, mem_info_t *resp) {
   resp->total = memInfo.total;
   resp->free = memInfo.free;
 
-  LOG(h.verbose, "[%s] CUDA totalMem %" PRId64 "mb\n", resp->gpu_id, resp->total / 1024 / 1024);
-  LOG(h.verbose, "[%s] CUDA freeMem %" PRId64 "mb\n", resp->gpu_id, resp->free / 1024 / 1024);
+  LOG(h.verbose, "[%s] CUDA totalMem %lu mb\n", resp->gpu_id, resp->total / 1024 / 1024);
+  LOG(h.verbose, "[%s] CUDA freeMem %lu mb\n", resp->gpu_id, resp->free / 1024 / 1024);
   LOG(h.verbose, "[%s] Compute Capability %d.%d\n", resp->gpu_id, resp->major, resp->minor);
 
   
@@ -248,4 +247,4 @@ void nvcuda_release(nvcuda_handle_t h) {
   h.handle = NULL;
 }
 
-#endif  // __APPLE__
+#endif  // __APPLE__

docs/api.md

@@ -19,7 +19,7 @@
 
 ### Model names
 
-Model names follow a `model:tag` format, where `model` can have an optional namespace such as `example/model`. Some examples are `orca-mini:3b-q8_0` and `llama3:70b`. The tag is optional and, if not provided, will default to `latest`. The tag is used to identify a specific version.
+Model names follow a `model:tag` format, where `model` can have an optional namespace such as `example/model`. Some examples are `orca-mini:3b-q4_1` and `llama3:70b`. The tag is optional and, if not provided, will default to `latest`. The tag is used to identify a specific version.
 
 ### Durations
 
@@ -43,7 +43,6 @@ Generate a response for a given prompt with a provided model. This is a streamin
 - `prompt`: the prompt to generate a response for
 - `suffix`: the text after the model response
 - `images`: (optional) a list of base64-encoded images (for multimodal models such as `llava`)
-- `think`: (for thinking models) should the model think before responding?
 
 Advanced parameters (optional):
 
@@ -395,6 +394,9 @@ curl http://localhost:11434/api/generate -d '{
     "repeat_penalty": 1.2,
     "presence_penalty": 1.5,
     "frequency_penalty": 1.0,
+    "mirostat": 1,
+    "mirostat_tau": 0.8,
+    "mirostat_eta": 0.6,
     "penalize_newline": true,
     "stop": ["\n", "user:"],
     "numa": false,
@@ -402,7 +404,10 @@ curl http://localhost:11434/api/generate -d '{
     "num_batch": 2,
     "num_gpu": 1,
     "main_gpu": 0,
+    "low_vram": false,
+    "vocab_only": false,
     "use_mmap": true,
+    "use_mlock": false,
     "num_thread": 8
   }
 }'
@@ -491,13 +496,11 @@ Generate the next message in a chat with a provided model. This is a streaming e
 - `model`: (required) the [model name](#model-names)
 - `messages`: the messages of the chat, this can be used to keep a chat memory
 - `tools`: list of tools in JSON for the model to use if supported
-- `think`: (for thinking models) should the model think before responding?
 
 The `message` object has the following fields:
 
 - `role`: the role of the message, either `system`, `user`, `assistant`, or `tool`
 - `content`: the content of the message
-- `thinking`: (for thinking models) the model's thinking process
 - `images` (optional): a list of images to include in the message (for multimodal models such as `llava`)
 - `tool_calls` (optional): a list of tools in JSON that the model wants to use
 
@@ -955,8 +958,19 @@ If you are creating a model from a safetensors directory or from a GGUF file, yo
 
 | Type | Recommended |
 | --- | :-: |
+| q2_K | |
+| q3_K_L | |
+| q3_K_M | |
+| q3_K_S | |
+| q4_0 | |
+| q4_1 | |
 | q4_K_M | * |
 | q4_K_S | |
+| q5_0 | |
+| q5_1 | |
+| q5_K_M | |
+| q5_K_S | |
+| q6_K | |
 | q8_0 | * |
 
 ### Examples
@@ -1001,8 +1015,8 @@ Quantize a non-quantized model.
 
 ```shell
 curl http://localhost:11434/api/create -d '{
-  "model": "llama3.2:quantized",
-  "from": "llama3.2:3b-instruct-fp16",
+  "model": "llama3.1:quantized",
+  "from": "llama3.1:8b-instruct-fp16",
   "quantize": "q4_K_M"
 }'
 ```
@@ -1012,14 +1026,12 @@ curl http://localhost:11434/api/create -d '{
 A stream of JSON objects is returned:
 
 ```json
-{"status":"quantizing F16 model to Q4_K_M","digest":"0","total":6433687776,"completed":12302}
-{"status":"quantizing F16 model to Q4_K_M","digest":"0","total":6433687776,"completed":6433687552}
-{"status":"verifying conversion"}
-{"status":"creating new layer sha256:fb7f4f211b89c6c4928ff4ddb73db9f9c0cfca3e000c3e40d6cf27ddc6ca72eb"}
-{"status":"using existing layer sha256:966de95ca8a62200913e3f8bfbf84c8494536f1b94b49166851e76644e966396"}
-{"status":"using existing layer sha256:fcc5a6bec9daf9b561a68827b67ab6088e1dba9d1fa2a50d7bbcc8384e0a265d"}
-{"status":"using existing layer sha256:a70ff7e570d97baaf4e62ac6e6ad9975e04caa6d900d3742d37698494479e0cd"}
+{"status":"quantizing F16 model to Q4_K_M"}
+{"status":"creating new layer sha256:667b0c1932bc6ffc593ed1d03f895bf2dc8dc6df21db3042284a6f4416b06a29"}
+{"status":"using existing layer sha256:11ce4ee3e170f6adebac9a991c22e22ab3f8530e154ee669954c4bc73061c258"}
+{"status":"using existing layer sha256:0ba8f0e314b4264dfd19df045cde9d4c394a52474bf92ed6a3de22a4ca31a177"}
 {"status":"using existing layer sha256:56bb8bd477a519ffa694fc449c2413c6f0e1d3b1c88fa7e3c9d88d3ae49d4dcb"}
+{"status":"creating new layer sha256:455f34728c9b5dd3376378bfb809ee166c145b0b4c1f1a6feca069055066ef9a"}
 {"status":"writing manifest"}
 {"status":"success"}
 ```
@@ -1157,46 +1169,29 @@ A single JSON object will be returned.
 {
   "models": [
     {
-
-      "model": "codellama:13b",
+      "name": "codellama:13b",
       "modified_at": "2023-11-04T14:56:49.277302595-07:00",
       "size": 7365960935,
       "digest": "9f438cb9cd581fc025612d27f7c1a6669ff83a8bb0ed86c94fcf4c5440555697",
-      "capabilities": [
-        "completion"
-      ],
-
       "details": {
-        "parent_model": "",
         "format": "gguf",
-        "family": "qwen2",
-        "families": [
-          "qwen2"
-        ],
-        "parameter_size": "7.6B",
-        "quantization_level": "Q4_K_M"
+        "family": "llama",
+        "families": null,
+        "parameter_size": "13B",
+        "quantization_level": "Q4_0"
       }
     },
     {
-
-      "model": "llama4:latest",
+      "name": "llama3:latest",
       "modified_at": "2023-12-07T09:32:18.757212583-08:00",
       "size": 3825819519,
       "digest": "fe938a131f40e6f6d40083c9f0f430a515233eb2edaa6d72eb85c50d64f2300e",
-      "capabilities": [
-        "completion",
-        "vision"
-      ],
-
       "details": {
-        "parent_model": "",
         "format": "gguf",
         "family": "llama",
-        "families": [
-          "llama"
-        ],
-        "parameter_size": "3.2B",
-        "quantization_level": "Q4_K_M"
+        "families": null,
+        "parameter_size": "7B",
+        "quantization_level": "Q4_0"
       }
     }
   ]

docs/development.md

@@ -118,7 +118,7 @@ To run tests, use `go test`:
 go test ./...
 ```
 
-> NOTE: In rare cirumstances, you may need to change a package using the new
+> NOTE: In rare cirumstances, you may nedd to change a package using the new
 > "synctest" package in go1.24.
 >
 > If you do not have the "synctest" package enabled, you will not see build or

docs/faq.md

@@ -20,7 +20,7 @@ Please refer to the [GPU docs](./gpu.md).
 
 ## How can I specify the context window size?
 
-By default, Ollama uses a context window size of 4096 tokens. 
+By default, Ollama uses a context window size of 4096 tokens, unless you have a single GPU with <= 4 GB of VRAM, in which case it will default to 2048 tokens. 
 
 This can be overridden with the `OLLAMA_CONTEXT_LENGTH` environment variable. For example, to set the default context window to 8K, use: 
 
@@ -31,7 +31,7 @@ OLLAMA_CONTEXT_LENGTH=8192 ollama serve
 To change this when using `ollama run`, use `/set parameter`:
 
 ```shell
-/set parameter num_ctx 4096
+/set parameter num_ctx 8192
 ```
 
 When using the API, specify the `num_ctx` parameter:
@@ -41,7 +41,7 @@ curl http://localhost:11434/api/generate -d '{
   "model": "llama3.2",
   "prompt": "Why is the sky blue?",
   "options": {
-    "num_ctx": 4096
+    "num_ctx": 8192
   }
 }'
 ```

docs/import.md

@@ -132,12 +132,22 @@ success
 
 ### Supported Quantizations
 
+- `q4_0`
+- `q4_1`
+- `q5_0`
+- `q5_1`
 - `q8_0`
 
 #### K-means Quantizations
 
+- `q3_K_S`
+- `q3_K_M`
+- `q3_K_L`
 - `q4_K_S`
 - `q4_K_M`
+- `q5_K_S`
+- `q5_K_M`
+- `q6_K`
 
 
 ## Sharing your model on ollama.com

docs/modelfile.md

@@ -150,6 +150,9 @@ PARAMETER <parameter> <parametervalue>
 
 | Parameter      | Description                                                                                                                                                                                                                                             | Value Type | Example Usage        |
 | -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------- | -------------------- |
+| mirostat       | Enable Mirostat sampling for controlling perplexity. (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)                                                                                                                                         | int        | mirostat 0           |
+| mirostat_eta   | Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. (Default: 0.1)                        | float      | mirostat_eta 0.1     |
+| mirostat_tau   | Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. (Default: 5.0)                                                                                                         | float      | mirostat_tau 5.0     |
 | num_ctx        | Sets the size of the context window used to generate the next token. (Default: 2048)                                                                                                                                                                    | int        | num_ctx 4096         |
 | repeat_last_n  | Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)                                                                                                                                           | int        | repeat_last_n 64     |
 | repeat_penalty | Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1)                                                                     | float      | repeat_penalty 1.1   |

envconfig/config.go

@@ -149,22 +149,9 @@ func Bool(k string) func() bool {
 	}
 }
 
-// LogLevel returns the log level for the application.
-// Values are 0 or false INFO (Default), 1 or true DEBUG, 2 TRACE
-func LogLevel() slog.Level {
-	level := slog.LevelInfo
-	if s := Var("OLLAMA_DEBUG"); s != "" {
-		if b, _ := strconv.ParseBool(s); b {
-			level = slog.LevelDebug
-		} else if i, _ := strconv.ParseInt(s, 10, 64); i != 0 {
-			level = slog.Level(i * -4)
-		}
-	}
-
-	return level
-}
-
 var (
+	// Debug enabled additional debug information.
+	Debug = Bool("OLLAMA_DEBUG")
 	// FlashAttention enables the experimental flash attention feature.
 	FlashAttention = Bool("OLLAMA_FLASH_ATTENTION")
 	// KvCacheType is the quantization type for the K/V cache.
@@ -182,9 +169,7 @@ var (
 	// Enable the new Ollama engine
 	NewEngine = Bool("OLLAMA_NEW_ENGINE")
 	// ContextLength sets the default context length
-	ContextLength = Uint("OLLAMA_CONTEXT_LENGTH", 4096)
-	// Auth enables authentication between the Ollama client and server
-	UseAuth = Bool("OLLAMA_AUTH")
+	ContextLength = Int64("OLLAMA_CONTEXT_LENGTH", -1)
 )
 
 func String(s string) func() string {
@@ -224,6 +209,8 @@ var (
 	MaxRunners = Uint("OLLAMA_MAX_LOADED_MODELS", 0)
 	// MaxQueue sets the maximum number of queued requests. MaxQueue can be configured via the OLLAMA_MAX_QUEUE environment variable.
 	MaxQueue = Uint("OLLAMA_MAX_QUEUE", 512)
+	// MaxVRAM sets a maximum VRAM override in bytes. MaxVRAM can be configured via the OLLAMA_MAX_VRAM environment variable.
+	MaxVRAM = Uint("OLLAMA_MAX_VRAM", 0)
 )
 
 func Uint64(key string, defaultValue uint64) func() uint64 {
@@ -240,6 +227,20 @@ func Uint64(key string, defaultValue uint64) func() uint64 {
 	}
 }
 
+func Int64(key string, defaultValue int64) func() int64 {
+	return func() int64 {
+		if s := Var(key); s != "" {
+			if n, err := strconv.ParseInt(s, 10, 64); err != nil {
+				slog.Warn("invalid environment variable, using default", "key", key, "value", s, "default", defaultValue)
+			} else {
+				return n
+			}
+		}
+
+		return defaultValue
+	}
+}
+
 // Set aside VRAM per GPU
 var GpuOverhead = Uint64("OLLAMA_GPU_OVERHEAD", 0)
 
@@ -251,7 +252,7 @@ type EnvVar struct {
 
 func AsMap() map[string]EnvVar {
 	ret := map[string]EnvVar{
-		"OLLAMA_DEBUG":             {"OLLAMA_DEBUG", LogLevel(), "Show additional debug information (e.g. OLLAMA_DEBUG=1)"},
+		"OLLAMA_DEBUG":             {"OLLAMA_DEBUG", Debug(), "Show additional debug information (e.g. OLLAMA_DEBUG=1)"},
 		"OLLAMA_FLASH_ATTENTION":   {"OLLAMA_FLASH_ATTENTION", FlashAttention(), "Enabled flash attention"},
 		"OLLAMA_KV_CACHE_TYPE":     {"OLLAMA_KV_CACHE_TYPE", KvCacheType(), "Quantization type for the K/V cache (default: f16)"},
 		"OLLAMA_GPU_OVERHEAD":      {"OLLAMA_GPU_OVERHEAD", GpuOverhead(), "Reserve a portion of VRAM per GPU (bytes)"},
@@ -268,7 +269,7 @@ func AsMap() map[string]EnvVar {
 		"OLLAMA_ORIGINS":           {"OLLAMA_ORIGINS", AllowedOrigins(), "A comma separated list of allowed origins"},
 		"OLLAMA_SCHED_SPREAD":      {"OLLAMA_SCHED_SPREAD", SchedSpread(), "Always schedule model across all GPUs"},
 		"OLLAMA_MULTIUSER_CACHE":   {"OLLAMA_MULTIUSER_CACHE", MultiUserCache(), "Optimize prompt caching for multi-user scenarios"},
-		"OLLAMA_CONTEXT_LENGTH":    {"OLLAMA_CONTEXT_LENGTH", ContextLength(), "Context length to use unless otherwise specified (default: 4096)"},
+		"OLLAMA_CONTEXT_LENGTH":    {"OLLAMA_CONTEXT_LENGTH", ContextLength(), "Context length to use unless otherwise specified (default 4096 or 2048 with low VRAM)"},
 		"OLLAMA_NEW_ENGINE":        {"OLLAMA_NEW_ENGINE", NewEngine(), "Enable the new Ollama engine"},
 
 		// Informational

envconfig/config_test.go

@@ -1,13 +1,11 @@
 package envconfig
 
 import (
-	"log/slog"
 	"math"
 	"testing"
 	"time"
 
 	"github.com/google/go-cmp/cmp"
-	"github.com/ollama/ollama/logutil"
 )
 
 func TestHost(t *testing.T) {
@@ -280,9 +278,9 @@ func TestVar(t *testing.T) {
 }
 
 func TestContextLength(t *testing.T) {
-	cases := map[string]uint{
-		"":     4096,
-		"2048": 2048,
+	cases := map[string]int64{
+		"":     -1,
+		"4096": 4096,
 	}
 
 	for k, v := range cases {
@@ -294,34 +292,3 @@ func TestContextLength(t *testing.T) {
 		})
 	}
 }
-
-func TestLogLevel(t *testing.T) {
-	cases := map[string]slog.Level{
-		// Default to INFO
-		"":      slog.LevelInfo,
-		"false": slog.LevelInfo,
-		"f":     slog.LevelInfo,
-		"0":     slog.LevelInfo,
-
-		// True values enable Debug
-		"true": slog.LevelDebug,
-		"t":    slog.LevelDebug,
-
-		// Positive values increase verbosity
-		"1": slog.LevelDebug,
-		"2": logutil.LevelTrace,
-
-		// Negative values decrease verbosity
-		"-1": slog.LevelWarn,
-		"-2": slog.LevelError,
-	}
-
-	for k, v := range cases {
-		t.Run(k, func(t *testing.T) {
-			t.Setenv("OLLAMA_DEBUG", k)
-			if i := LogLevel(); i != v {
-				t.Errorf("%s: expected %d, got %d", k, v, i)
-			}
-		})
-	}
-}

fs/ggml/ggml.go

@@ -15,7 +15,6 @@ import (
 type GGML struct {
 	container
 	model
-	Length int64
 }
 
 type model interface {
@@ -37,12 +36,12 @@ func (kv KV) ParameterCount() uint64 {
 	return keyValue(kv, "general.parameter_count", uint64(0))
 }
 
-func (kv KV) FileType() FileType {
+func (kv KV) FileType() fileType {
 	if t := kv.Uint("general.file_type"); t > 0 {
-		return FileType(t)
+		return fileType(t)
 	}
 
-	return FileTypeUnknown
+	return fileTypeUnknown
 }
 
 func (kv KV) BlockCount() uint64 {
@@ -126,8 +125,6 @@ func (kv KV) OllamaEngineRequired() bool {
 		"gemma3",
 		"mistral3",
 		"llama4",
-		"mllama",
-		"qwen25vl",
 	}, kv.Architecture())
 }
 
@@ -152,7 +149,7 @@ func keyValue[T valueTypes | arrayValueTypes](kv KV, key string, defaultValue ..
 		return val.(T)
 	}
 
-	slog.Debug("key not found", "key", key, "default", defaultValue[0])
+	slog.Warn("key not found", "key", key, "default", defaultValue[0])
 	return defaultValue[0]
 }
 
@@ -229,11 +226,7 @@ func (t Tensor) block() (n int) {
 }
 
 func (t Tensor) blockSize() uint64 {
-	return (TensorType)(t.Kind).BlockSize()
-}
-
-func (t TensorType) BlockSize() uint64 {
-	switch t {
+	switch t.Kind {
 	case
 		0,  // F32
 		1,  // F16
@@ -259,77 +252,73 @@ func (t TensorType) BlockSize() uint64 {
 }
 
 func (t Tensor) typeSize() uint64 {
-	return TensorType(t.Kind).TypeSize()
-}
+	blockSize := t.blockSize()
 
-func (t TensorType) TypeSize() uint64 {
-	blockSize := t.BlockSize()
-
-	switch t {
-	case TensorTypeF32:
+	switch t.Kind {
+	case 0: // FP32
 		return 4
-	case TensorTypeF16:
+	case 1: // FP16
 		return 2
-	case TensorTypeQ4_0:
+	case 2: // Q4_0
 		return 2 + blockSize/2
-	case TensorTypeQ4_1:
+	case 3: // Q4_1
 		return 2 + 2 + blockSize/2
-	case TensorTypeQ5_0:
+	case 6: // Q5_0
 		return 2 + 4 + blockSize/2
-	case TensorTypeQ5_1:
+	case 7: // Q5_1
 		return 2 + 2 + 4 + blockSize/2
-	case TensorTypeQ8_0:
+	case 8: // Q8_0
 		return 2 + blockSize
-	case TensorTypeQ8_1:
+	case 9: // Q8_1
 		return 2 + 2 + blockSize
-	case TensorTypeQ2_K:
+	case 10: // Q2_K
 		return blockSize/16 + blockSize/4 + 2 + 2
-	case TensorTypeQ3_K:
+	case 11: // Q3_K
 		return blockSize/8 + blockSize/4 + 12 + 2
-	case TensorTypeQ4_K:
+	case 12: // Q4_K
 		return 2 + 2 + 12 + blockSize/2
-	case TensorTypeQ5_K:
+	case 13: // Q5_K
 		return 2 + 2 + 12 + blockSize/8 + blockSize/2
-	case TensorTypeQ6_K:
+	case 14: // Q6_K
 		return blockSize/2 + blockSize/4 + blockSize/16 + 2
-	case TensorTypeQ8_K:
+	case 15: // Q8_K
 		return 4 + blockSize + 2*blockSize/16
-	case tensorTypeIQ2_XXS:
+	case 16: // IQ2_XXS
 		return 2 + 2*blockSize/8
-	case tensorTypeIQ2_XS:
+	case 17: // IQ2_XS
 		return 2 + 2*blockSize/8 + blockSize/32
-	case tensorTypeIQ3_XXS:
+	case 18: // IQ3_XXS
 		return 2 + blockSize/4 + blockSize/8
-	case tensorTypeIQ1_S:
+	case 19: // IQ1_S
 		return 2 + blockSize/8 + blockSize/16
-	case tensorTypeIQ4_NL:
+	case 20: // IQ4_NL
 		return 2 + blockSize/2
-	case tensorTypeIQ3_S:
+	case 21: // IQ3_S
 		return 2 + blockSize/4 + blockSize/8 + blockSize/32 + 4
-	case tensorTypeIQ2_S:
+	case 22: // IQ2_S
 		return 2 + blockSize/4 + blockSize/16
-	case tensorTypeIQ4_XS:
+	case 23: // IQ4_XS
 		return 2 + 2 + blockSize/2 + blockSize/64
-	case TensorTypeI8:
+	case 24: // I8
 		return 1
-	case TensorTypeI16:
+	case 25: // I16
 		return 2
-	case TensorTypeI32:
+	case 26: // I32
 		return 4
-	case TensorTypeI64:
+	case 27: // I64
 		return 8
-	case TensorTypeF64:
+	case 28: // F64
 		return 8
-	case tensorTypeIQ1_M:
+	case 29: // IQ1_M
 		return blockSize/8 + blockSize/16 + blockSize/32
-	case TensorTypeBF16:
+	case 30: // BF16
 		return 2
 	default:
 		return 0
 	}
 }
 
-func (t Tensor) Elements() uint64 {
+func (t Tensor) parameters() uint64 {
 	var count uint64 = 1
 	for _, n := range t.Shape {
 		count *= n
@@ -338,11 +327,11 @@ func (t Tensor) Elements() uint64 {
 }
 
 func (t Tensor) Size() uint64 {
-	return t.Elements() * t.typeSize() / t.blockSize()
+	return t.parameters() * t.typeSize() / t.blockSize()
 }
 
 func (t Tensor) Type() string {
-	return TensorType(t.Kind).String()
+	return fileType(t.Kind).String()
 }
 
 type container interface {
@@ -387,12 +376,12 @@ func DetectContentType(b []byte) string {
 //
 // It collects array values for arrays with a size less than or equal to
 // maxArraySize. If the maxArraySize is negative, all arrays are collected.
-func Decode(rs io.ReadSeeker, maxArraySize int) (*GGML, error) {
+func Decode(rs io.ReadSeeker, maxArraySize int) (*GGML, int64, error) {
 	rs = bufioutil.NewBufferedSeeker(rs, 32<<10)
 
 	var magic uint32
 	if err := binary.Read(rs, binary.LittleEndian, &magic); err != nil {
-		return nil, err
+		return nil, 0, err
 	}
 
 	var c container
@@ -402,25 +391,24 @@ func Decode(rs io.ReadSeeker, maxArraySize int) (*GGML, error) {
 	case FILE_MAGIC_GGUF_BE:
 		c = &containerGGUF{ByteOrder: binary.BigEndian, maxArraySize: maxArraySize}
 	default:
-		return nil, errors.New("invalid file magic")
+		return nil, 0, errors.New("invalid file magic")
 	}
 
 	model, err := c.Decode(rs)
 	if err != nil {
-		return nil, err
+		return nil, 0, err
 	}
 
 	offset, err := rs.Seek(0, io.SeekCurrent)
 	if err != nil {
-		return nil, err
+		return nil, 0, err
 	}
 
 	// final model type
 	return &GGML{
 		container: c,
 		model:     model,
-		Length:    offset,
-	}, nil
+	}, offset, nil
 }
 
 func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType string) (kv []uint64, partialOffload, fullOffload uint64) {
@@ -492,7 +480,7 @@ func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType stri
 		var ropeFreqsCount uint64
 		if ropeFreqs, ok := f.Tensors().GroupLayers()["rope_freqs"]; ok {
 			if ropeFreqsWeights, ok := ropeFreqs["weights"]; ok {
-				ropeFreqsCount = ropeFreqsWeights.Elements()
+				ropeFreqsCount = ropeFreqsWeights.parameters()
 			}
 		}
 
@@ -652,20 +640,6 @@ func (llm GGML) VisionGraphSize() (weights, graphSize uint64) {
 		graphSize = 4 * (imageSize*imageSize*numChannels +
 			embeddingLength*patchSize +
 			numPatches*numPatches*headCount)
-	case "qwen25vl":
-		maxPixels := uint64(llm.KV().Uint("vision.max_pixels", 28*28*1280))
-
-		numPatches := maxPixels / (patchSize * patchSize)
-
-		graphSize = 4 * (maxPixels*numChannels + // Original image storage
-			// Normalized pixels
-			maxPixels*numChannels +
-			// Patches storage (numPatches * channels * patchSize^2)
-			numPatches*numChannels*patchSize*patchSize +
-			// Self-attention calculations
-			numPatches*numPatches*headCount +
-			// Additional buffer for processing
-			embeddingLength*numPatches)
 	case "llama4":
 		// vision graph is computed independently in the same schedule
 		// and is negligible compared to the worst case text graph

fs/ggml/gguf.go

@@ -9,12 +9,8 @@ import (
 	"io"
 	"log/slog"
 	"maps"
-	"os"
-	"runtime"
 	"slices"
 	"strings"
-
-	"golang.org/x/sync/errgroup"
 )
 
 type containerGGUF struct {
@@ -229,7 +225,7 @@ func (llm *gguf) Decode(rs io.ReadSeeker) error {
 		}
 
 		llm.tensors = append(llm.tensors, &tensor)
-		llm.parameters += tensor.Elements()
+		llm.parameters += tensor.parameters()
 	}
 
 	// patch KV with parameter count
@@ -492,38 +488,25 @@ func writeGGUFArray[S ~[]E, E any](w io.Writer, t uint32, s S) error {
 		return err
 	}
 
-	if t == ggufTypeString {
-		for _, e := range any(s).([]string) {
-			if err := binary.Write(w, binary.LittleEndian, uint64(len(e))); err != nil {
-				return err
-			}
-
-			if err := binary.Write(w, binary.LittleEndian, []byte(e)); err != nil {
-				return err
-			}
-		}
-		return nil
-	}
-
 	return binary.Write(w, binary.LittleEndian, s)
 }
 
-func WriteGGUF(f *os.File, kv KV, ts []*Tensor) error {
+func WriteGGUF(ws io.WriteSeeker, kv KV, ts []Tensor) error {
 	alignment := kv.Uint("general.alignment", 32)
 
-	if err := binary.Write(f, binary.LittleEndian, []byte("GGUF")); err != nil {
+	if err := binary.Write(ws, binary.LittleEndian, []byte("GGUF")); err != nil {
 		return err
 	}
 
-	if err := binary.Write(f, binary.LittleEndian, uint32(3)); err != nil {
+	if err := binary.Write(ws, binary.LittleEndian, uint32(3)); err != nil {
 		return err
 	}
 
-	if err := binary.Write(f, binary.LittleEndian, uint64(len(ts))); err != nil {
+	if err := binary.Write(ws, binary.LittleEndian, uint64(len(ts))); err != nil {
 		return err
 	}
 
-	if err := binary.Write(f, binary.LittleEndian, uint64(len(kv))); err != nil {
+	if err := binary.Write(ws, binary.LittleEndian, uint64(len(kv))); err != nil {
 		return err
 	}
 
@@ -531,12 +514,12 @@ func WriteGGUF(f *os.File, kv KV, ts []*Tensor) error {
 	slices.Sort(keys)
 
 	for _, key := range keys {
-		if err := ggufWriteKV(f, key, kv[key]); err != nil {
+		if err := ggufWriteKV(ws, key, kv[key]); err != nil {
 			return err
 		}
 	}
 
-	slices.SortStableFunc(ts, func(a, b *Tensor) int {
+	slices.SortStableFunc(ts, func(a, b Tensor) int {
 		if i, j := a.block(), b.block(); i < 0 && j > 0 {
 			return 1
 		} else if i > 0 && j < 0 {
@@ -547,34 +530,21 @@ func WriteGGUF(f *os.File, kv KV, ts []*Tensor) error {
 	})
 
 	var s uint64
-	for i := range ts {
-		ts[i].Offset = s
-		if err := ggufWriteTensorInfo(f, ts[i]); err != nil {
+	for _, t := range ts {
+		t.Offset = s + uint64(ggufPadding(int64(s), int64(alignment)))
+		if err := ggufWriteTensorInfo(ws, t); err != nil {
 			return err
 		}
-		s += ts[i].Size()
-		s += uint64(ggufPadding(int64(s), int64(alignment)))
+		s += t.Size()
 	}
 
-	offset, err := f.Seek(0, io.SeekCurrent)
-	if err != nil {
-		return err
-	}
-	offset += ggufPadding(offset, int64(alignment))
-
-	var g errgroup.Group
-	g.SetLimit(runtime.GOMAXPROCS(0))
-	// TODO consider reducing if tensors size * gomaxprocs is larger than free memory
 	for _, t := range ts {
-		t := t
-		w := io.NewOffsetWriter(f, offset+int64(t.Offset))
-		g.Go(func() error {
-			_, err := t.WriteTo(w)
+		if err := ggufWriteTensor(ws, t, int64(alignment)); err != nil {
 			return err
-		})
+		}
 	}
 
-	return g.Wait()
+	return nil
 }
 
 func ggufWriteKV(ws io.WriteSeeker, k string, v any) error {
@@ -589,10 +559,8 @@ func ggufWriteKV(ws io.WriteSeeker, k string, v any) error {
 
 	var err error
 	switch v := v.(type) {
-	case uint32, FileType:
+	case uint32:
 		err = writeGGUF(ws, ggufTypeUint32, v)
-	case uint64:
-		err = writeGGUF(ws, ggufTypeUint64, v)
 	case float32:
 		err = writeGGUF(ws, ggufTypeFloat32, v)
 	case bool:
@@ -601,20 +569,32 @@ func ggufWriteKV(ws io.WriteSeeker, k string, v any) error {
 		err = writeGGUFString(ws, v)
 	case []int32:
 		err = writeGGUFArray(ws, ggufTypeInt32, v)
-	case *array[int32]:
-		err = writeGGUFArray(ws, ggufTypeInt32, v.values)
 	case []uint32:
 		err = writeGGUFArray(ws, ggufTypeUint32, v)
-	case *array[uint32]:
-		err = writeGGUFArray(ws, ggufTypeUint32, v.values)
 	case []float32:
 		err = writeGGUFArray(ws, ggufTypeFloat32, v)
-	case *array[float32]:
-		err = writeGGUFArray(ws, ggufTypeFloat32, v.values)
 	case []string:
-		err = writeGGUFArray(ws, ggufTypeString, v)
-	case *array[string]:
-		err = writeGGUFArray(ws, ggufTypeString, v.values)
+		if err := binary.Write(ws, binary.LittleEndian, ggufTypeArray); err != nil {
+			return err
+		}
+
+		if err := binary.Write(ws, binary.LittleEndian, ggufTypeString); err != nil {
+			return err
+		}
+
+		if err := binary.Write(ws, binary.LittleEndian, uint64(len(v))); err != nil {
+			return err
+		}
+
+		for _, e := range v {
+			if err := binary.Write(ws, binary.LittleEndian, uint64(len(e))); err != nil {
+				return err
+			}
+
+			if err := binary.Write(ws, binary.LittleEndian, []byte(e)); err != nil {
+				return err
+			}
+		}
 	default:
 		return fmt.Errorf("improper type for '%s'", k)
 	}
@@ -622,7 +602,7 @@ func ggufWriteKV(ws io.WriteSeeker, k string, v any) error {
 	return err
 }
 
-func ggufWriteTensorInfo(ws io.WriteSeeker, t *Tensor) error {
+func ggufWriteTensorInfo(ws io.WriteSeeker, t Tensor) error {
 	slog.Debug(t.Name, "kind", t.Kind, "shape", t.Shape, "offset", t.Offset)
 	if err := binary.Write(ws, binary.LittleEndian, uint64(len(t.Name))); err != nil {
 		return err
@@ -649,6 +629,20 @@ func ggufWriteTensorInfo(ws io.WriteSeeker, t *Tensor) error {
 	return binary.Write(ws, binary.LittleEndian, t.Offset)
 }
 
+func ggufWriteTensor(ws io.WriteSeeker, t Tensor, alignment int64) error {
+	offset, err := ws.Seek(0, io.SeekCurrent)
+	if err != nil {
+		return err
+	}
+
+	if err := binary.Write(ws, binary.LittleEndian, bytes.Repeat([]byte{0}, int(ggufPadding(offset, alignment)))); err != nil {
+		return err
+	}
+
+	_, err = t.WriteTo(ws)
+	return err
+}
+
 func ggufPadding(offset, align int64) int64 {
 	return (align - offset%align) % align
 }

fs/ggml/gguf_test.go

@@ -1,63 +0,0 @@
-package ggml
-
-import (
-	"bytes"
-	"os"
-	"slices"
-	"testing"
-
-	"github.com/google/go-cmp/cmp"
-)
-
-func TestWriteGGUF(t *testing.T) {
-	w, err := os.CreateTemp(t.TempDir(), "*.bin")
-	if err != nil {
-		t.Fatal(err)
-	}
-	defer w.Close()
-
-	if err := WriteGGUF(w, KV{
-		"general.alignment": uint32(16),
-	}, []*Tensor{
-		{Name: "test.0", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(slices.Repeat([]byte{0}, 2*3*4))},
-		{Name: "test.1", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(slices.Repeat([]byte{0}, 2*3*4))},
-		{Name: "test.2", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(slices.Repeat([]byte{0}, 2*3*4))},
-		{Name: "test.3", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(slices.Repeat([]byte{0}, 2*3*4))},
-		{Name: "test.4", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(slices.Repeat([]byte{0}, 2*3*4))},
-		{Name: "test.5", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(slices.Repeat([]byte{0}, 2*3*4))},
-	}); err != nil {
-		t.Fatal(err)
-	}
-
-	r, err := os.Open(w.Name())
-	if err != nil {
-		t.Fatal(err)
-	}
-	defer r.Close()
-
-	ff, err := Decode(r, 0)
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	if diff := cmp.Diff(ff.KV(), KV{
-		"general.alignment":       uint32(16),
-		"general.parameter_count": uint64(36),
-	}); diff != "" {
-		t.Errorf("Mismatch (-want +got):\n%s", diff)
-	}
-
-	if diff := cmp.Diff(ff.Tensors(), Tensors{
-		Offset: 336,
-		items: []*Tensor{
-			{Name: "test.0", Offset: 0, Shape: []uint64{2, 3}},
-			{Name: "test.1", Offset: 32, Shape: []uint64{2, 3}},
-			{Name: "test.2", Offset: 64, Shape: []uint64{2, 3}},
-			{Name: "test.3", Offset: 96, Shape: []uint64{2, 3}},
-			{Name: "test.4", Offset: 128, Shape: []uint64{2, 3}},
-			{Name: "test.5", Offset: 160, Shape: []uint64{2, 3}},
-		},
-	}, cmp.AllowUnexported(Tensors{})); diff != "" {
-		t.Errorf("Mismatch (-want +got):\n%s", diff)
-	}
-}

fs/ggml/type.go

@@ -1,31 +1,26 @@
 package ggml
 
-import (
-	"fmt"
-	"log/slog"
-	"strings"
-)
+import "fmt"
 
-// FileType is the Go equivalent to llama_ftype used for gguf file typing
-type FileType uint32
+type fileType uint32
 
 const (
-	FileTypeF32 FileType = iota
-	FileTypeF16
+	fileTypeF32 fileType = iota
+	fileTypeF16
 	fileTypeQ4_0
 	fileTypeQ4_1
-	fileTypeQ4_1_F16 // unused by GGML
-	fileTypeQ4_2     // unused by GGML
-	fileTypeQ4_3     // unused by GGML
-	FileTypeQ8_0
+	fileTypeQ4_1_F16
+	fileTypeQ4_2 // unused
+	fileTypeQ4_3 // unused
+	fileTypeQ8_0
 	fileTypeQ5_0
 	fileTypeQ5_1
 	fileTypeQ2_K
 	fileTypeQ3_K_S
 	fileTypeQ3_K_M
 	fileTypeQ3_K_L
-	FileTypeQ4_K_S
-	FileTypeQ4_K_M
+	fileTypeQ4_K_S
+	fileTypeQ4_K_M
 	fileTypeQ5_K_S
 	fileTypeQ5_K_M
 	fileTypeQ6_K
@@ -42,62 +37,93 @@ const (
 	fileTypeIQ2_M
 	fileTypeIQ4_XS
 	fileTypeIQ1_M
-	FileTypeBF16
-	fileTypeQ4_0_4_4 // unused by GGML
-	fileTypeQ4_0_4_8 // unused by GGML
-	fileTypeQ4_0_8_8 // unused by GGML
-	fileTypeTQ1_0
-	fileTypeTQ2_0
+	fileTypeBF16
 
-	FileTypeUnknown = 1024
+	fileTypeUnknown
 )
 
-// ParseFileType parses the provided GGUF file type
-// Only Ollama supported types are considered valid
-func ParseFileType(s string) (FileType, error) {
+func ParseFileType(s string) (fileType, error) {
 	switch s {
 	case "F32":
-		return FileTypeF32, nil
+		return fileTypeF32, nil
 	case "F16":
-		return FileTypeF16, nil
+		return fileTypeF16, nil
+	case "Q4_0":
+		return fileTypeQ4_0, nil
+	case "Q4_1":
+		return fileTypeQ4_1, nil
+	case "Q4_1_F16":
+		return fileTypeQ4_1_F16, nil
 	case "Q8_0":
-		return FileTypeQ8_0, nil
+		return fileTypeQ8_0, nil
+	case "Q5_0":
+		return fileTypeQ5_0, nil
+	case "Q5_1":
+		return fileTypeQ5_1, nil
+	case "Q2_K":
+		return fileTypeQ2_K, nil
+	case "Q3_K_S":
+		return fileTypeQ3_K_S, nil
+	case "Q3_K_M":
+		return fileTypeQ3_K_M, nil
+	case "Q3_K_L":
+		return fileTypeQ3_K_L, nil
 	case "Q4_K_S":
-		return FileTypeQ4_K_S, nil
-	case "Q4_K_M", "Q4_K":
-		return FileTypeQ4_K_M, nil
+		return fileTypeQ4_K_S, nil
+	case "Q4_K_M":
+		return fileTypeQ4_K_M, nil
+	case "Q5_K_S":
+		return fileTypeQ5_K_S, nil
+	case "Q5_K_M":
+		return fileTypeQ5_K_M, nil
+	case "Q6_K":
+		return fileTypeQ6_K, nil
+	case "IQ2_XXS":
+		return fileTypeIQ2_XXS, nil
+	case "IQ2_XS":
+		return fileTypeIQ2_XS, nil
+	case "Q2_K_S":
+		return fileTypeQ2_K_S, nil
+	case "IQ3_XS":
+		return fileTypeIQ3_XS, nil
+	case "IQ3_XXS":
+		return fileTypeIQ3_XXS, nil
+	case "IQ1_S":
+		return fileTypeIQ1_S, nil
+	case "IQ4_NL":
+		return fileTypeIQ4_NL, nil
+	case "IQ3_S":
+		return fileTypeIQ3_S, nil
+	case "IQ3_M":
+		return fileTypeIQ3_M, nil
+	case "IQ2_S":
+		return fileTypeIQ2_S, nil
+	case "IQ2_M":
+		return fileTypeIQ2_M, nil
+	case "IQ4_XS":
+		return fileTypeIQ4_XS, nil
+	case "IQ1_M":
+		return fileTypeIQ1_M, nil
 	case "BF16":
-		return FileTypeBF16, nil
+		return fileTypeBF16, nil
 	default:
-		supportedFileTypes := []FileType{
-			FileTypeF32,
-			FileTypeF16,
-			FileTypeQ4_K_S,
-			FileTypeQ4_K_M,
-			FileTypeQ8_0,
-			// fsggml.FileTypeBF16, // TODO
-		}
-		strs := make([]string, len(supportedFileTypes))
-		for i := range supportedFileTypes {
-			strs[i] = supportedFileTypes[i].String()
-		}
-
-		return FileTypeUnknown, fmt.Errorf("unsupported quantization type %s - supported types are %s", s, strings.Join(strs, ", "))
+		return fileTypeUnknown, fmt.Errorf("unknown fileType: %s", s)
 	}
 }
 
-func (t FileType) String() string {
-	// Note: this routine will return a broader set of file types for existing models
+func (t fileType) String() string {
 	switch t {
-	case FileTypeF32:
+	case fileTypeF32:
 		return "F32"
-	case FileTypeF16:
+	case fileTypeF16:
 		return "F16"
 	case fileTypeQ4_0:
 		return "Q4_0"
 	case fileTypeQ4_1:
 		return "Q4_1"
-	case FileTypeQ8_0:
+	case fileTypeQ4_1_F16:
+		return "Q4_1_F16"
+	case fileTypeQ8_0:
 		return "Q8_0"
 	case fileTypeQ5_0:
 		return "Q5_0"
@@ -111,9 +137,9 @@ func (t FileType) String() string {
 		return "Q3_K_M"
 	case fileTypeQ3_K_L:
 		return "Q3_K_L"
-	case FileTypeQ4_K_S:
+	case fileTypeQ4_K_S:
 		return "Q4_K_S"
-	case FileTypeQ4_K_M:
+	case fileTypeQ4_K_M:
 		return "Q4_K_M"
 	case fileTypeQ5_K_S:
 		return "Q5_K_S"
@@ -121,198 +147,39 @@ func (t FileType) String() string {
 		return "Q5_K_M"
 	case fileTypeQ6_K:
 		return "Q6_K"
+	case fileTypeIQ2_XXS:
+		return "IQ2_XXS"
+	case fileTypeIQ2_XS:
+		return "IQ2_XS"
 	case fileTypeQ2_K_S:
 		return "Q2_K_S"
-	case FileTypeBF16:
+	case fileTypeIQ3_XS:
+		return "IQ3_XS"
+	case fileTypeIQ3_XXS:
+		return "IQ3_XXS"
+	case fileTypeIQ1_S:
+		return "IQ1_S"
+	case fileTypeIQ4_NL:
+		return "IQ4_NL"
+	case fileTypeIQ3_S:
+		return "IQ3_S"
+	case fileTypeIQ3_M:
+		return "IQ3_M"
+	case fileTypeIQ2_S:
+		return "IQ2_S"
+	case fileTypeIQ4_XS:
+		return "IQ4_XS"
+	case fileTypeIQ2_M:
+		return "IQ2_M"
+	case fileTypeIQ1_M:
+		return "IQ1_M"
+	case fileTypeBF16:
 		return "BF16"
 	default:
 		return "unknown"
 	}
 }
 
-func (t FileType) Value() uint32 {
+func (t fileType) Value() uint32 {
 	return uint32(t)
 }
-
-func (ftype FileType) ToTensorType() TensorType {
-	switch ftype {
-	case FileTypeF32:
-		return TensorTypeF32
-	case FileTypeF16:
-		return TensorTypeF16
-	case fileTypeQ4_0:
-		return TensorTypeQ4_0
-	case fileTypeQ4_1:
-		return TensorTypeQ4_1
-	case FileTypeQ8_0:
-		return TensorTypeQ8_0
-	case fileTypeQ5_0:
-		return TensorTypeQ5_0
-	case fileTypeQ5_1:
-		return TensorTypeQ5_1
-	case fileTypeQ2_K:
-		return TensorTypeQ2_K
-	case fileTypeQ3_K_S:
-		return TensorTypeQ3_K
-	case fileTypeQ3_K_M:
-		return TensorTypeQ3_K
-	case fileTypeQ3_K_L:
-		return TensorTypeQ3_K
-	case FileTypeQ4_K_S:
-		return TensorTypeQ4_K
-	case FileTypeQ4_K_M:
-		return TensorTypeQ4_K
-	case fileTypeQ5_K_S:
-		return TensorTypeQ5_K
-	case fileTypeQ5_K_M:
-		return TensorTypeQ5_K
-	case fileTypeQ6_K:
-		return TensorTypeQ6_K
-	case fileTypeQ2_K_S:
-		return TensorTypeQ2_K
-	case FileTypeBF16:
-		return TensorTypeBF16
-	default:
-		slog.Warn("unsupported file type", "type", ftype)
-		return 0 // F32
-	}
-}
-
-// TensorType is equivalent to ggml_type for individual tensor types
-// Note: these are not the same as FileType
-type TensorType uint32
-
-const (
-	TensorTypeF32 TensorType = iota
-	TensorTypeF16
-	TensorTypeQ4_0
-	TensorTypeQ4_1
-	tensorTypeQ4_2 // unused by GGML
-	tensorTypeQ4_3 // unused by GGML
-	TensorTypeQ5_0
-	TensorTypeQ5_1
-	TensorTypeQ8_0
-	TensorTypeQ8_1
-	TensorTypeQ2_K
-	TensorTypeQ3_K
-	TensorTypeQ4_K
-	TensorTypeQ5_K
-	TensorTypeQ6_K
-	TensorTypeQ8_K
-	tensorTypeIQ2_XXS // not supported by ollama
-	tensorTypeIQ2_XS  // not supported by ollama
-	tensorTypeIQ3_XXS // not supported by ollama
-	tensorTypeIQ1_S   // not supported by ollama
-	tensorTypeIQ4_NL  // not supported by ollama
-	tensorTypeIQ3_S   // not supported by ollama
-	tensorTypeIQ2_S   // not supported by ollama
-	tensorTypeIQ4_XS  // not supported by ollama
-	TensorTypeI8
-	TensorTypeI16
-	TensorTypeI32
-	TensorTypeI64
-	TensorTypeF64
-	tensorTypeIQ1_M // not supported by ollama
-	TensorTypeBF16
-	tensorTypeQ4_0_4_4   // unused by GGML
-	tensorTypeQ4_0_4_8   // unused by GGML
-	tensorTypeQ4_0_8_8   // unused by GGML
-	tensorTypeTQ1_0      // not supported by ollama
-	tensorTypeTQ2_0      // not supported by ollama
-	tensorTypeIQ4_NL_4_4 // unused by GGML
-	tensorTypeIQ4_NL_4_8 // unused by GGML
-	tensorTypeIQ4_NL_8_8 // unused by GGML
-)
-
-// ParseFileType parses the provided GGUF file type
-// Only Ollama supported types are considered valid
-func ParseTensorType(s string) (TensorType, error) {
-	switch s {
-	case "F32":
-		return TensorTypeF32, nil
-	case "F16":
-		return TensorTypeF16, nil
-	case "Q4_0":
-		return TensorTypeQ4_0, nil
-	case "Q4_1":
-		return TensorTypeQ4_1, nil
-	case "Q5_0":
-		return TensorTypeQ5_0, nil
-	case "Q5_1":
-		return TensorTypeQ5_1, nil
-	case "Q8_0":
-		return TensorTypeQ8_0, nil
-	case "Q8_1":
-		return TensorTypeQ8_1, nil
-	case "Q2_K":
-		return TensorTypeQ2_K, nil
-	case "Q3_K":
-		return TensorTypeQ3_K, nil
-	case "Q4_K":
-		return TensorTypeQ4_K, nil
-	case "Q5_K":
-		return TensorTypeQ5_K, nil
-	case "Q6_K":
-		return TensorTypeQ6_K, nil
-	case "Q8_K":
-		return TensorTypeQ8_K, nil
-	case "F64":
-		return TensorTypeF64, nil
-	case "BF16":
-		return TensorTypeBF16, nil
-	default:
-		return 0, fmt.Errorf("unsupported quantization type %s", s)
-	}
-}
-
-func (t TensorType) IsQuantized() bool {
-	switch t {
-	case TensorTypeF32, TensorTypeF16, TensorTypeBF16:
-		return false
-	default:
-		return true
-	}
-}
-
-func (t TensorType) RowSize(ne uint64) uint64 {
-	return t.TypeSize() * ne / t.BlockSize()
-}
-
-func (t TensorType) String() string {
-	switch t {
-	case TensorTypeF32:
-		return "F32"
-	case TensorTypeF16:
-		return "F16"
-	case TensorTypeQ4_0:
-		return "Q4_0"
-	case TensorTypeQ4_1:
-		return "Q4_1"
-	case TensorTypeQ5_0:
-		return "Q5_0"
-	case TensorTypeQ5_1:
-		return "Q5_1"
-	case TensorTypeQ8_0:
-		return "Q8_0"
-	case TensorTypeQ8_1:
-		return "Q8_1"
-	case TensorTypeQ2_K:
-		return "Q2_K"
-	case TensorTypeQ3_K:
-		return "Q3_K"
-	case TensorTypeQ4_K:
-		return "Q4_K"
-	case TensorTypeQ5_K:
-		return "Q5_K"
-	case TensorTypeQ6_K:
-		return "Q6_K"
-	case TensorTypeQ8_K:
-		return "Q8_K"
-	case TensorTypeF64:
-		return "F64"
-	case TensorTypeBF16:
-		return "BF16"
-	default:
-		return "unknown"
-	}
-}

fs/gguf/gguf.go

@@ -1,350 +0,0 @@
-package gguf
-
-import (
-	"bytes"
-	"cmp"
-	"encoding/binary"
-	"errors"
-	"fmt"
-	"io"
-	"iter"
-	"os"
-	"slices"
-	"strings"
-)
-
-const (
-	typeUint8 uint32 = iota
-	typeInt8
-	typeUint16
-	typeInt16
-	typeUint32
-	typeInt32
-	typeFloat32
-	typeBool
-	typeString
-	typeArray
-	typeUint64
-	typeInt64
-	typeFloat64
-)
-
-var ErrUnsupported = errors.New("unsupported")
-
-type File struct {
-	Magic   [4]byte
-	Version uint32
-
-	keyValues *lazy[KeyValue]
-	tensors   *lazy[TensorInfo]
-	offset    int64
-
-	file   *os.File
-	reader *readSeeker
-	bts    []byte
-}
-
-func Open(path string) (f *File, err error) {
-	f = &File{bts: make([]byte, 4096)}
-	f.file, err = os.Open(path)
-	if err != nil {
-		return nil, err
-	}
-
-	f.reader = newReadSeeker(f.file, 32<<10)
-
-	if err := binary.Read(f.reader, binary.LittleEndian, &f.Magic); err != nil {
-		return nil, err
-	}
-
-	if bytes.Equal(f.Magic[:], []byte("gguf")) {
-		return nil, fmt.Errorf("%w file type %v", ErrUnsupported, f.Magic)
-	}
-
-	if err := binary.Read(f.reader, binary.LittleEndian, &f.Version); err != nil {
-		return nil, err
-	}
-
-	if f.Version != 3 {
-		return nil, fmt.Errorf("%w version %v", ErrUnsupported, f.Version)
-	}
-
-	f.tensors, err = newLazy(f, f.readTensor)
-	if err != nil {
-		return nil, err
-	}
-
-	f.tensors.doneFunc = func() error {
-		offset, err := f.reader.Seek(0, io.SeekCurrent)
-		if err != nil {
-			return err
-		}
-
-		alignment := cmp.Or(f.KeyValue("general.alignment").Int(), 32)
-		f.offset = offset + (alignment-offset%alignment)%alignment
-		return nil
-	}
-
-	f.keyValues, err = newLazy(f, f.readKeyValue)
-	if err != nil {
-		return nil, err
-	}
-
-	return f, nil
-}
-
-func (f *File) readTensor() (TensorInfo, error) {
-	name, err := readString(f)
-	if err != nil {
-		return TensorInfo{}, err
-	}
-
-	dims, err := read[uint32](f)
-	if err != nil {
-		return TensorInfo{}, err
-	}
-
-	shape := make([]uint64, dims)
-	for i := range dims {
-		shape[i], err = read[uint64](f)
-		if err != nil {
-			return TensorInfo{}, err
-		}
-	}
-
-	type_, err := read[uint32](f)
-	if err != nil {
-		return TensorInfo{}, err
-	}
-
-	offset, err := read[uint64](f)
-	if err != nil {
-		return TensorInfo{}, err
-	}
-
-	return TensorInfo{
-		Name:   name,
-		Offset: offset,
-		Shape:  shape,
-		Type:   TensorType(type_),
-	}, nil
-}
-
-func (f *File) readKeyValue() (KeyValue, error) {
-	key, err := readString(f)
-	if err != nil {
-		return KeyValue{}, err
-	}
-
-	t, err := read[uint32](f)
-	if err != nil {
-		return KeyValue{}, err
-	}
-
-	value, err := func() (any, error) {
-		switch t {
-		case typeUint8:
-			return read[uint8](f)
-		case typeInt8:
-			return read[int8](f)
-		case typeUint16:
-			return read[uint16](f)
-		case typeInt16:
-			return read[int16](f)
-		case typeUint32:
-			return read[uint32](f)
-		case typeInt32:
-			return read[int32](f)
-		case typeUint64:
-			return read[uint64](f)
-		case typeInt64:
-			return read[int64](f)
-		case typeFloat32:
-			return read[float32](f)
-		case typeFloat64:
-			return read[float64](f)
-		case typeBool:
-			return read[bool](f)
-		case typeString:
-			return readString(f)
-		case typeArray:
-			return readArray(f)
-		default:
-			return nil, fmt.Errorf("%w type %d", ErrUnsupported, t)
-		}
-	}()
-	if err != nil {
-		return KeyValue{}, err
-	}
-
-	return KeyValue{
-		Key:   key,
-		Value: Value{value},
-	}, nil
-}
-
-func read[T any](f *File) (t T, err error) {
-	err = binary.Read(f.reader, binary.LittleEndian, &t)
-	return t, err
-}
-
-func readString(f *File) (string, error) {
-	n, err := read[uint64](f)
-	if err != nil {
-		return "", err
-	}
-
-	if int(n) > len(f.bts) {
-		f.bts = make([]byte, n)
-	}
-
-	bts := f.bts[:n]
-	if _, err := io.ReadFull(f.reader, bts); err != nil {
-		return "", err
-	}
-	defer clear(bts)
-
-	return string(bts), nil
-}
-
-func readArray(f *File) (any, error) {
-	t, err := read[uint32](f)
-	if err != nil {
-		return nil, err
-	}
-
-	n, err := read[uint64](f)
-	if err != nil {
-		return nil, err
-	}
-
-	switch t {
-	case typeUint8:
-		return readArrayData[uint8](f, n)
-	case typeInt8:
-		return readArrayData[int8](f, n)
-	case typeUint16:
-		return readArrayData[uint16](f, n)
-	case typeInt16:
-		return readArrayData[int16](f, n)
-	case typeUint32:
-		return readArrayData[uint32](f, n)
-	case typeInt32:
-		return readArrayData[int32](f, n)
-	case typeUint64:
-		return readArrayData[uint64](f, n)
-	case typeInt64:
-		return readArrayData[int64](f, n)
-	case typeFloat32:
-		return readArrayData[float32](f, n)
-	case typeFloat64:
-		return readArrayData[float64](f, n)
-	case typeBool:
-		return readArrayData[bool](f, n)
-	case typeString:
-		return readArrayString(f, n)
-	default:
-		return nil, fmt.Errorf("%w type %d", ErrUnsupported, t)
-	}
-}
-
-func readArrayData[T any](f *File, n uint64) (s []T, err error) {
-	s = make([]T, n)
-	for i := range n {
-		e, err := read[T](f)
-		if err != nil {
-			return nil, err
-		}
-
-		s[i] = e
-	}
-
-	return s, nil
-}
-
-func readArrayString(f *File, n uint64) (s []string, err error) {
-	s = make([]string, n)
-	for i := range n {
-		e, err := readString(f)
-		if err != nil {
-			return nil, err
-		}
-
-		s[i] = e
-	}
-
-	return s, nil
-}
-
-func (f *File) Close() error {
-	f.keyValues.stop()
-	f.tensors.stop()
-	return f.file.Close()
-}
-
-func (f *File) KeyValue(key string) KeyValue {
-	if !strings.HasPrefix(key, "general.") && !strings.HasPrefix(key, "tokenizer.") {
-		key = f.KeyValue("general.architecture").String() + "." + key
-	}
-
-	if index := slices.IndexFunc(f.keyValues.values, func(kv KeyValue) bool {
-		return kv.Key == key
-	}); index >= 0 {
-		return f.keyValues.values[index]
-	}
-
-	for keyValue, ok := f.keyValues.next(); ok; keyValue, ok = f.keyValues.next() {
-		if keyValue.Key == key {
-			return keyValue
-		}
-	}
-
-	return KeyValue{}
-}
-
-func (f *File) NumKeyValues() int {
-	return int(f.keyValues.count)
-}
-
-func (f *File) KeyValues() iter.Seq2[int, KeyValue] {
-	return f.keyValues.All()
-}
-
-func (f *File) TensorInfo(name string) TensorInfo {
-	if index := slices.IndexFunc(f.tensors.values, func(t TensorInfo) bool {
-		return t.Name == name
-	}); index >= 0 {
-		return f.tensors.values[index]
-	}
-
-	// fast-forward through key values if we haven't already
-	_ = f.keyValues.rest()
-	for tensor, ok := f.tensors.next(); ok; tensor, ok = f.tensors.next() {
-		if tensor.Name == name {
-			return tensor
-		}
-	}
-
-	return TensorInfo{}
-}
-
-func (f *File) NumTensors() int {
-	return int(f.tensors.count)
-}
-
-func (f *File) TensorInfos() iter.Seq2[int, TensorInfo] {
-	// fast forward through key values if we haven't already
-	f.keyValues.rest()
-	return f.tensors.All()
-}
-
-func (f *File) TensorReader(name string) (TensorInfo, io.Reader, error) {
-	t := f.TensorInfo(name)
-	if t.NumBytes() == 0 {
-		return TensorInfo{}, nil, fmt.Errorf("tensor %s not found", name)
-	}
-
-	// fast forward through tensor info if we haven't already
-	_ = f.tensors.rest()
-	return t, io.NewSectionReader(f.file, f.offset+int64(t.Offset), t.NumBytes()), nil
-}

fs/gguf/gguf_test.go

@@ -1,320 +0,0 @@
-package gguf
-
-import (
-	"encoding/binary"
-	"fmt"
-	"os"
-	"path/filepath"
-	"slices"
-	"testing"
-)
-
-func TestRead(t *testing.T) {
-	// Setup
-	tempDir := t.TempDir()
-	tempFile := filepath.Join(tempDir, "test.gguf")
-
-	if err := createTestGGUFFile(tempFile, map[string]any{
-		"general.architecture": "llama",
-		"general.alignment":    int64(32),
-	}, []testTensorInfo{
-		{Name: "token_embd.weight", Shape: []uint64{1000, 512}, Type: 1}, // F16
-		{Name: "output.weight", Shape: []uint64{512, 1000}, Type: 1},     // F16
-	}); err != nil {
-		t.Fatal(err)
-	}
-
-	f, err := Open(tempFile)
-	if err != nil {
-		t.Fatal(err)
-	}
-	defer f.Close()
-
-	// Test
-	if got := f.NumKeyValues(); got != 2 {
-		t.Errorf("NumKeyValues() = %d, want %d", got, 2)
-	}
-	if got := f.NumTensors(); got != 2 {
-		t.Errorf("NumTensors() = %d, want %d", got, 2)
-	}
-	archKV := f.KeyValue("general.architecture")
-	if archKV.Key == "" {
-		t.Error("KeyValue(\"general.architecture\") not found")
-	}
-	if got := archKV.String(); got != "llama" {
-		t.Errorf("KeyValue(\"general.architecture\").String() = %q, want %q", got, "llama")
-	}
-	alignKV := f.KeyValue("general.alignment")
-	if alignKV.Key == "" {
-		t.Error("KeyValue(\"general.alignment\") not found")
-	}
-	if got := alignKV.Int(); got != 32 {
-		t.Errorf("KeyValue(\"general.alignment\").Int() = %d, want %d", got, 32)
-	}
-	expectedTensorNames := []string{"token_embd.weight", "output.weight"}
-	var gotTensorNames []string
-	for _, tensor := range f.TensorInfos() {
-		gotTensorNames = append(gotTensorNames, tensor.Name)
-	}
-	if !slices.Equal(gotTensorNames, expectedTensorNames) {
-		t.Errorf("tensor names = %v, want %v", gotTensorNames, expectedTensorNames)
-	}
-	tokenTensor := f.TensorInfo("token_embd.weight")
-	if tokenTensor.Name != "token_embd.weight" {
-		t.Error("TensorInfo(\"token_embd.weight\") not found")
-	}
-	if len(tokenTensor.Shape) == 0 {
-		t.Error("TensorInfo(\"token_embd.weight\") has empty shape")
-	}
-	outputTensor := f.TensorInfo("output.weight")
-	if outputTensor.Name != "output.weight" {
-		t.Error("TensorInfo(\"output.weight\") not found")
-	}
-	if len(outputTensor.Shape) == 0 {
-		t.Error("TensorInfo(\"output.weight\") has empty shape")
-	}
-	var gotKeyCount int
-	for _, kv := range f.KeyValues() {
-		gotKeyCount++
-		if kv.Key == "" {
-			t.Error("found key value with empty key")
-		}
-	}
-	if gotKeyCount != 2 {
-		t.Errorf("iterated key count = %d, want %d", gotKeyCount, 2)
-	}
-	tensorInfo, reader, err := f.TensorReader("token_embd.weight")
-	if err != nil {
-		t.Errorf("TensorReader(\"token_embd.weight\") error: %v", err)
-	}
-	if tensorInfo.Name != "token_embd.weight" {
-		t.Errorf("TensorReader returned wrong tensor: %q", tensorInfo.Name)
-	}
-	if reader == nil {
-		t.Error("TensorReader returned nil reader")
-	}
-}
-
-func BenchmarkRead(b *testing.B) {
-	// Create benchmark test file
-	tempDir := b.TempDir()
-	tempFile := filepath.Join(tempDir, "benchmark.gguf")
-
-	if err := createTestGGUFFile(tempFile, map[string]any{
-		"general.architecture": "llama",
-		"general.alignment":    int64(32),
-	}, []testTensorInfo{
-		{Name: "token_embd.weight", Shape: []uint64{1000, 512}, Type: 1}, // F16
-		{Name: "output.weight", Shape: []uint64{512, 1000}, Type: 1},     // F16
-	}); err != nil {
-		b.Fatal(err)
-	}
-
-	// Get file info for reporting
-	info, err := os.Stat(tempFile)
-	if err != nil {
-		b.Fatal(err)
-	}
-	b.Logf("Benchmark file size: %d bytes", info.Size())
-
-	b.ReportAllocs()
-
-	for b.Loop() {
-		f, err := Open(tempFile)
-		if err != nil {
-			b.Fatal(err)
-		}
-
-		// Access some data to ensure it's actually being read
-		_ = f.KeyValue("general.architecture").String()
-		_ = f.KeyValue("general.alignment").Int()
-		_ = f.NumTensors()
-		_ = f.NumKeyValues()
-
-		// Iterate through some tensors
-		count := 0
-		for _, tensor := range f.TensorInfos() {
-			_ = tensor.Name
-			count++
-			if count >= 2 {
-				break
-			}
-		}
-
-		f.Close()
-	}
-}
-
-// Helper function to create test GGUF files
-func createTestGGUFFile(path string, keyValues map[string]any, tensors []testTensorInfo) error {
-	file, err := os.Create(path)
-	if err != nil {
-		return err
-	}
-	defer file.Close()
-
-	// Write GGUF magic
-	if _, err := file.Write([]byte("GGUF")); err != nil {
-		return err
-	}
-
-	// Write version
-	if err := binary.Write(file, binary.LittleEndian, uint32(3)); err != nil {
-		return err
-	}
-
-	// Write tensor count
-	if err := binary.Write(file, binary.LittleEndian, uint64(len(tensors))); err != nil {
-		return err
-	}
-
-	// Write metadata count
-	if err := binary.Write(file, binary.LittleEndian, uint64(len(keyValues))); err != nil {
-		return err
-	}
-
-	// Write metadata
-	for key, value := range keyValues {
-		if err := writeKeyValue(file, key, value); err != nil {
-			return err
-		}
-	}
-
-	// Write tensor info
-	for _, tensor := range tensors {
-		if err := writeTensorInfo(file, tensor); err != nil {
-			return err
-		}
-	}
-
-	// Write some dummy tensor data
-	dummyData := make([]byte, 1024)
-	file.Write(dummyData)
-
-	return nil
-}
-
-type testTensorInfo struct {
-	Name  string
-	Shape []uint64
-	Type  uint32
-}
-
-func writeKeyValue(file *os.File, key string, value any) error {
-	// Write key length and key
-	if err := binary.Write(file, binary.LittleEndian, uint64(len(key))); err != nil {
-		return err
-	}
-	if _, err := file.Write([]byte(key)); err != nil {
-		return err
-	}
-
-	// Write value based on type
-	switch v := value.(type) {
-	case string:
-		if err := binary.Write(file, binary.LittleEndian, typeString); err != nil {
-			return err
-		}
-		if err := binary.Write(file, binary.LittleEndian, uint64(len(v))); err != nil {
-			return err
-		}
-		_, err := file.Write([]byte(v))
-		return err
-	case int64:
-		if err := binary.Write(file, binary.LittleEndian, typeInt64); err != nil {
-			return err
-		}
-		return binary.Write(file, binary.LittleEndian, v)
-	case bool:
-		if err := binary.Write(file, binary.LittleEndian, typeBool); err != nil {
-			return err
-		}
-		return binary.Write(file, binary.LittleEndian, v)
-	case float64:
-		if err := binary.Write(file, binary.LittleEndian, typeFloat64); err != nil {
-			return err
-		}
-		return binary.Write(file, binary.LittleEndian, v)
-	case []string:
-		if err := binary.Write(file, binary.LittleEndian, typeArray); err != nil {
-			return err
-		}
-		if err := binary.Write(file, binary.LittleEndian, typeString); err != nil {
-			return err
-		}
-		if err := binary.Write(file, binary.LittleEndian, uint64(len(v))); err != nil {
-			return err
-		}
-		for _, s := range v {
-			if err := binary.Write(file, binary.LittleEndian, uint64(len(s))); err != nil {
-				return err
-			}
-			if _, err := file.Write([]byte(s)); err != nil {
-				return err
-			}
-		}
-		return nil
-	case []int64:
-		if err := binary.Write(file, binary.LittleEndian, typeArray); err != nil {
-			return err
-		}
-		if err := binary.Write(file, binary.LittleEndian, typeInt64); err != nil {
-			return err
-		}
-		if err := binary.Write(file, binary.LittleEndian, uint64(len(v))); err != nil {
-			return err
-		}
-		for _, i := range v {
-			if err := binary.Write(file, binary.LittleEndian, i); err != nil {
-				return err
-			}
-		}
-		return nil
-	case []float64:
-		if err := binary.Write(file, binary.LittleEndian, typeArray); err != nil {
-			return err
-		}
-		if err := binary.Write(file, binary.LittleEndian, typeFloat64); err != nil {
-			return err
-		}
-		if err := binary.Write(file, binary.LittleEndian, uint64(len(v))); err != nil {
-			return err
-		}
-		for _, f := range v {
-			if err := binary.Write(file, binary.LittleEndian, f); err != nil {
-				return err
-			}
-		}
-		return nil
-	default:
-		return fmt.Errorf("unsupported value type: %T", value)
-	}
-}
-
-func writeTensorInfo(file *os.File, tensor testTensorInfo) error {
-	// Write tensor name
-	if err := binary.Write(file, binary.LittleEndian, uint64(len(tensor.Name))); err != nil {
-		return err
-	}
-	if _, err := file.Write([]byte(tensor.Name)); err != nil {
-		return err
-	}
-
-	// Write dimensions
-	if err := binary.Write(file, binary.LittleEndian, uint32(len(tensor.Shape))); err != nil {
-		return err
-	}
-	for _, dim := range tensor.Shape {
-		if err := binary.Write(file, binary.LittleEndian, dim); err != nil {
-			return err
-		}
-	}
-
-	// Write type
-	if err := binary.Write(file, binary.LittleEndian, tensor.Type); err != nil {
-		return err
-	}
-
-	// Write offset (dummy value)
-	return binary.Write(file, binary.LittleEndian, uint64(0))
-}

fs/gguf/keyvalue.go

@@ -1,102 +0,0 @@
-package gguf
-
-import (
-	"reflect"
-	"slices"
-)
-
-type KeyValue struct {
-	Key string
-	Value
-}
-
-type Value struct {
-	value any
-}
-
-func value[T any](v Value, kinds ...reflect.Kind) (t T) {
-	vv := reflect.ValueOf(v.value)
-	if slices.Contains(kinds, vv.Kind()) {
-		t = vv.Convert(reflect.TypeOf(t)).Interface().(T)
-	}
-	return
-}
-
-func values[T any](v Value, kinds ...reflect.Kind) (ts []T) {
-	switch vv := reflect.ValueOf(v.value); vv.Kind() {
-	case reflect.Slice:
-		if slices.Contains(kinds, vv.Type().Elem().Kind()) {
-			ts = make([]T, vv.Len())
-			for i := range vv.Len() {
-				ts[i] = vv.Index(i).Convert(reflect.TypeOf(ts[i])).Interface().(T)
-			}
-		}
-	}
-	return
-}
-
-// Int returns Value as a signed integer. If it is not a signed integer, it returns 0.
-func (v Value) Int() int64 {
-	return value[int64](v, reflect.Int, reflect.Int8, reflect.Int16, reflect.Int32, reflect.Int64)
-}
-
-// Ints returns Value as a signed integer slice. If it is not a signed integer slice, it returns nil.
-func (v Value) Ints() (i64s []int64) {
-	return values[int64](v, reflect.Int, reflect.Int8, reflect.Int16, reflect.Int32, reflect.Int64)
-}
-
-// Uint converts an unsigned integer value to uint64. If the value is not a unsigned integer, it returns 0.
-func (v Value) Uint() uint64 {
-	return value[uint64](v, reflect.Uint, reflect.Uint8, reflect.Uint16, reflect.Uint32, reflect.Uint64)
-}
-
-// Uints returns Value as a unsigned integer slice. If it is not a unsigned integer slice, it returns nil.
-func (v Value) Uints() (u64s []uint64) {
-	return values[uint64](v, reflect.Uint, reflect.Uint8, reflect.Uint16, reflect.Uint32, reflect.Uint64)
-}
-
-// Float returns Value as a float. If it is not a float, it returns 0.
-func (v Value) Float() float64 {
-	return value[float64](v, reflect.Float32, reflect.Float64)
-}
-
-// Floats returns Value as a float slice. If it is not a float slice, it returns nil.
-func (v Value) Floats() (f64s []float64) {
-	return values[float64](v, reflect.Float32, reflect.Float64)
-}
-
-// Bool returns Value as a boolean. If it is not a boolean, it returns false.
-func (v Value) Bool() bool {
-	return value[bool](v, reflect.Bool)
-}
-
-// Bools returns Value as a boolean slice. If it is not a boolean slice, it returns nil.
-func (v Value) Bools() (bools []bool) {
-	return values[bool](v, reflect.Bool)
-}
-
-// String returns Value as a string. If it is not a string, it returns an empty string.
-func (v Value) String() string {
-	return value[string](v, reflect.String)
-}
-
-// Strings returns Value as a string slice. If it is not a string slice, it returns nil.
-func (v Value) Strings() (strings []string) {
-	return values[string](v, reflect.String)
-}
-
-// IsNil checks if the Value is nil. It returns true if the value is nil or if it is a nil pointer, interface, slice, map, channel, or function.
-func (v Value) IsNil() bool {
-	if v.value == nil {
-		return true
-	}
-
-	// Check for nil pointers, interfaces, slices, maps, channels, and functions
-	rv := reflect.ValueOf(v.value)
-	switch rv.Kind() {
-	case reflect.Ptr, reflect.Interface, reflect.Slice, reflect.Map, reflect.Chan, reflect.Func:
-		return rv.IsNil()
-	}
-
-	return false
-}

fs/gguf/keyvalue_test.go

@@ -1,208 +0,0 @@
-package gguf
-
-import (
-	"testing"
-
-	"github.com/google/go-cmp/cmp"
-)
-
-func split(name string, values map[string][]any) (matched []any, unmatched []any) {
-	for key, value := range values {
-		if key == name {
-			matched = value
-		} else {
-			unmatched = append(unmatched, value...)
-		}
-	}
-	return
-}
-
-func TestValue(t *testing.T) {
-	values := map[string][]any{
-		"int64":   {int(42), int8(42), int16(42), int32(42), int64(42)},
-		"uint64":  {uint(42), uint8(42), uint16(42), uint32(42), uint64(42)},
-		"float64": {float32(42), float64(42)},
-		"string":  {"42", "hello"},
-		"bool":    {true, false},
-	}
-
-	t.Run("int64", func(t *testing.T) {
-		matched, unmatched := split("int64", values)
-		for _, v := range matched {
-			kv := KeyValue{"key", Value{v}}
-			if i64 := kv.Int(); i64 != 42 {
-				t.Errorf("expected 42, got %d", i64)
-			}
-		}
-
-		for _, v := range unmatched {
-			kv := KeyValue{"key", Value{v}}
-			if i64 := kv.Int(); i64 != 0 {
-				t.Errorf("expected 42, got %d", i64)
-			}
-		}
-	})
-
-	t.Run("uint64", func(t *testing.T) {
-		matched, unmatched := split("uint64", values)
-		for _, v := range matched {
-			kv := KeyValue{"key", Value{v}}
-			if u64 := kv.Uint(); u64 != 42 {
-				t.Errorf("expected 42, got %d", u64)
-			}
-		}
-
-		for _, v := range unmatched {
-			kv := KeyValue{"key", Value{v}}
-			if u64 := kv.Uint(); u64 != 0 {
-				t.Errorf("expected 42, got %d", u64)
-			}
-		}
-	})
-
-	t.Run("float64", func(t *testing.T) {
-		matched, unmatched := split("float64", values)
-		for _, v := range matched {
-			kv := KeyValue{"key", Value{v}}
-			if f64 := kv.Float(); f64 != 42 {
-				t.Errorf("expected 42, got %f", f64)
-			}
-		}
-
-		for _, v := range unmatched {
-			kv := KeyValue{"key", Value{v}}
-			if f64 := kv.Float(); f64 != 0 {
-				t.Errorf("expected 42, got %f", f64)
-			}
-		}
-	})
-
-	t.Run("string", func(t *testing.T) {
-		matched, unmatched := split("string", values)
-		for _, v := range matched {
-			kv := KeyValue{"key", Value{v}}
-			if s := kv.String(); s != v {
-				t.Errorf("expected 42, got %s", s)
-			}
-		}
-
-		for _, v := range unmatched {
-			kv := KeyValue{"key", Value{v}}
-			if s := kv.String(); s != "" {
-				t.Errorf("expected 42, got %s", s)
-			}
-		}
-	})
-
-	t.Run("bool", func(t *testing.T) {
-		matched, unmatched := split("bool", values)
-		for _, v := range matched {
-			kv := KeyValue{"key", Value{v}}
-			if b := kv.Bool(); b != v {
-				t.Errorf("expected true, got %v", b)
-			}
-		}
-
-		for _, v := range unmatched {
-			kv := KeyValue{"key", Value{v}}
-			if b := kv.Bool(); b != false {
-				t.Errorf("expected false, got %v", b)
-			}
-		}
-	})
-}
-
-func TestValues(t *testing.T) {
-	values := map[string][]any{
-		"int64s":   {[]int{42}, []int8{42}, []int16{42}, []int32{42}, []int64{42}},
-		"uint64s":  {[]uint{42}, []uint8{42}, []uint16{42}, []uint32{42}, []uint64{42}},
-		"float64s": {[]float32{42}, []float64{42}},
-		"strings":  {[]string{"42"}, []string{"hello"}},
-		"bools":    {[]bool{true}, []bool{false}},
-	}
-
-	t.Run("int64s", func(t *testing.T) {
-		matched, unmatched := split("int64s", values)
-		for _, v := range matched {
-			kv := KeyValue{"key", Value{v}}
-			if diff := cmp.Diff(kv.Ints(), []int64{42}); diff != "" {
-				t.Errorf("diff: %s", diff)
-			}
-		}
-
-		for _, v := range unmatched {
-			kv := KeyValue{"key", Value{v}}
-			if i64s := kv.Ints(); i64s != nil {
-				t.Errorf("expected nil, got %v", i64s)
-			}
-		}
-	})
-
-	t.Run("uint64s", func(t *testing.T) {
-		matched, unmatched := split("uint64s", values)
-		for _, v := range matched {
-			kv := KeyValue{"key", Value{v}}
-			if diff := cmp.Diff(kv.Uints(), []uint64{42}); diff != "" {
-				t.Errorf("diff: %s", diff)
-			}
-		}
-
-		for _, v := range unmatched {
-			kv := KeyValue{"key", Value{v}}
-			if u64s := kv.Uints(); u64s != nil {
-				t.Errorf("expected nil, got %v", u64s)
-			}
-		}
-	})
-
-	t.Run("float64s", func(t *testing.T) {
-		matched, unmatched := split("float64s", values)
-		for _, v := range matched {
-			kv := KeyValue{"key", Value{v}}
-			if diff := cmp.Diff(kv.Floats(), []float64{42}); diff != "" {
-				t.Errorf("diff: %s", diff)
-			}
-		}
-
-		for _, v := range unmatched {
-			kv := KeyValue{"key", Value{v}}
-			if f64s := kv.Floats(); f64s != nil {
-				t.Errorf("expected nil, got %v", f64s)
-			}
-		}
-	})
-
-	t.Run("strings", func(t *testing.T) {
-		matched, unmatched := split("strings", values)
-		for _, v := range matched {
-			kv := KeyValue{"key", Value{v}}
-			if diff := cmp.Diff(kv.Strings(), v); diff != "" {
-				t.Errorf("diff: %s", diff)
-			}
-		}
-
-		for _, v := range unmatched {
-			kv := KeyValue{"key", Value{v}}
-			if s := kv.Strings(); s != nil {
-				t.Errorf("expected nil, got %v", s)
-			}
-		}
-	})
-
-	t.Run("bools", func(t *testing.T) {
-		matched, unmatched := split("bools", values)
-		for _, v := range matched {
-			kv := KeyValue{"key", Value{v}}
-			if diff := cmp.Diff(kv.Bools(), v); diff != "" {
-				t.Errorf("diff: %s", diff)
-			}
-		}
-
-		for _, v := range unmatched {
-			kv := KeyValue{"key", Value{v}}
-			if b := kv.Bools(); b != nil {
-				t.Errorf("expected nil, got %v", b)
-			}
-		}
-	})
-}

fs/gguf/lazy.go

@@ -1,88 +0,0 @@
-package gguf
-
-import (
-	"encoding/binary"
-	"iter"
-	"log/slog"
-)
-
-type lazy[T any] struct {
-	count  uint64
-	next   func() (T, bool)
-	stop   func()
-	values []T
-
-	doneFunc func() error
-}
-
-func newLazy[T any](f *File, fn func() (T, error)) (*lazy[T], error) {
-	it := lazy[T]{}
-	if err := binary.Read(f.reader, binary.LittleEndian, &it.count); err != nil {
-		return nil, err
-	}
-
-	it.values = make([]T, 0)
-	it.next, it.stop = iter.Pull(func(yield func(T) bool) {
-		for i := range it.count {
-			t, err := fn()
-			if err != nil {
-				slog.Error("error reading tensor", "index", i, "error", err)
-				return
-			}
-
-			it.values = append(it.values, t)
-			if !yield(t) {
-				break
-			}
-		}
-
-		if it.doneFunc != nil {
-			it.doneFunc()
-		}
-	})
-
-	return &it, nil
-}
-
-func (g *lazy[T]) Values() iter.Seq[T] {
-	return func(yield func(T) bool) {
-		for _, v := range g.All() {
-			if !yield(v) {
-				break
-			}
-		}
-	}
-}
-
-func (g *lazy[T]) All() iter.Seq2[int, T] {
-	return func(yield func(int, T) bool) {
-		for i := range int(g.count) {
-			if i < len(g.values) {
-				if !yield(i, g.values[i]) {
-					break
-				}
-			} else {
-				t, ok := g.next()
-				if !ok {
-					break
-				}
-
-				if !yield(i, t) {
-					break
-				}
-			}
-		}
-	}
-}
-
-func (g *lazy[T]) rest() (collected bool) {
-	for {
-		_, ok := g.next()
-		collected = collected || ok
-		if !ok {
-			break
-		}
-	}
-
-	return collected
-}

fs/gguf/reader.go

@@ -1,34 +0,0 @@
-package gguf
-
-import (
-	"bufio"
-	"io"
-)
-
-type readSeeker struct {
-	rs io.ReadSeeker
-	br *bufio.Reader
-}
-
-func newReadSeeker(rs io.ReadSeeker, size int) *readSeeker {
-	return &readSeeker{
-		rs: rs,
-		br: bufio.NewReaderSize(rs, size),
-	}
-}
-
-func (b *readSeeker) Read(p []byte) (int, error) {
-	return b.br.Read(p)
-}
-
-func (b *readSeeker) Seek(offset int64, whence int) (int64, error) {
-	if whence == io.SeekCurrent {
-		offset -= int64(b.br.Buffered())
-	}
-	n, err := b.rs.Seek(offset, whence)
-	if err != nil {
-		return 0, err
-	}
-	b.br.Reset(b.rs)
-	return n, nil
-}

fs/gguf/tensor.go

@@ -1,284 +0,0 @@
-package gguf
-
-import (
-	"log/slog"
-	"strings"
-)
-
-type TensorInfo struct {
-	Name   string
-	Offset uint64
-	Shape  []uint64
-	Type   TensorType
-}
-
-func (t TensorInfo) NumValues() int64 {
-	var numItems int64 = 1
-	for _, dim := range t.Shape {
-		numItems *= int64(dim)
-	}
-	return numItems
-}
-
-// NumBytes returns the number of bytes in the tensor.
-func (t TensorInfo) NumBytes() int64 {
-	return int64(float64(t.NumValues()) * t.Type.NumBytes())
-}
-
-func (t TensorInfo) LogValue() slog.Value {
-	return slog.GroupValue(
-		slog.String("name", t.Name),
-		slog.Int64("offset", int64(t.Offset)),
-		slog.Any("shape", t.Shape),
-		slog.Int64("num_values", t.NumValues()),
-		slog.Int64("num_bytes", t.NumBytes()),
-		slog.Any("type", t.Type),
-	)
-}
-
-type TensorType uint32
-
-const (
-	TensorTypeF32 TensorType = iota
-	TensorTypeF16
-	TensorTypeQ4_0
-	TensorTypeQ4_1
-
-	// unexported // unused in gguf
-	tensorTypeQ4_2
-	tensorTypeQ4_3
-
-	TensorTypeQ5_0
-	TensorTypeQ5_1
-	TensorTypeQ8_0
-	TensorTypeQ8_1
-	TensorTypeQ2_K
-	TensorTypeQ3_K
-	TensorTypeQ4_K
-	TensorTypeQ5_K
-	TensorTypeQ6_K
-	TensorTypeQ8_K
-
-	// unexported // unquantizable by ollama
-	tensorTypeIQ2_XXS
-	tensorTypeIQ2_XS
-	tensorTypeIQ3_XXS
-	tensorTypeIQ1_S
-	tensorTypeIQ4_NL
-	tensorTypeIQ3_S
-	tensorTypeIQ2_S
-	tensorTypeIQ4_XS
-
-	TensorTypeI8
-	TensorTypeI16
-	TensorTypeI32
-	TensorTypeI64
-	TensorTypeF64
-
-	// unexported // unquantizable by ollama
-	tensorTypeIQ1_M
-
-	TensorTypeBF16
-
-	// unexported // unused in gguf
-	tensorTypeQ4_0_4_4
-	tensorTypeQ4_0_4_8
-	tensorTypeQ4_0_8_8
-
-	// unexported // unquantizable by ollama
-	tensorTypeTQ1_0
-	tensorTypeTQ2_0
-
-	// unexported // unused in gguf
-	tensorTypeIQ4_NL_4_4
-	tensorTypeIQ4_NL_4_8
-	tensorTypeIQ4_NL_8_8
-)
-
-func (t TensorType) NumBytes() float64 {
-	return float64(t.typeSize()) / float64(t.blockSize())
-}
-
-func (t TensorType) typeSize() int64 {
-	switch t {
-	case TensorTypeF32:
-		return 4
-	case TensorTypeF16:
-		return 2
-	case TensorTypeQ4_0:
-		return 2 + t.blockSize()/2
-	case TensorTypeQ4_1:
-		return 2 + 2 + t.blockSize()/2
-	case TensorTypeQ5_0:
-		return 2 + 4 + t.blockSize()/2
-	case TensorTypeQ5_1:
-		return 2 + 2 + 4 + t.blockSize()/2
-	case TensorTypeQ8_0:
-		return 2 + t.blockSize()
-	case TensorTypeQ8_1:
-		return 2 + 2 + t.blockSize()
-	case TensorTypeQ2_K:
-		return t.blockSize()/16 + t.blockSize()/4 + 2 + 2
-	case TensorTypeQ3_K:
-		return t.blockSize()/8 + t.blockSize()/4 + 12 + 2
-	case TensorTypeQ4_K:
-		return 2 + 2 + 12 + t.blockSize()/2
-	case TensorTypeQ5_K:
-		return 2 + 2 + 12 + t.blockSize()/8 + t.blockSize()/2
-	case TensorTypeQ6_K:
-		return t.blockSize()/2 + t.blockSize()/4 + t.blockSize()/16 + 2
-	case TensorTypeQ8_K:
-		return 4 + t.blockSize() + 2*t.blockSize()/16
-	case tensorTypeIQ2_XXS:
-		return 2 + 2*t.blockSize()/8
-	case tensorTypeIQ2_XS:
-		return 2 + 2*t.blockSize()/8 + t.blockSize()/32
-	case tensorTypeIQ3_XXS:
-		return 2 + t.blockSize()/4 + t.blockSize()/8
-	case tensorTypeIQ1_S:
-		return 2 + t.blockSize()/8 + t.blockSize()/16
-	case tensorTypeIQ4_NL:
-		return 2 + t.blockSize()/2
-	case tensorTypeIQ3_S:
-		return 2 + t.blockSize()/4 + t.blockSize()/8 + t.blockSize()/32 + 4
-	case tensorTypeIQ2_S:
-		return 2 + t.blockSize()/4 + t.blockSize()/16
-	case tensorTypeIQ4_XS:
-		return 2 + 2 + t.blockSize()/2 + t.blockSize()/64
-	case TensorTypeI8:
-		return 1
-	case TensorTypeI16:
-		return 2
-	case TensorTypeI32:
-		return 4
-	case TensorTypeI64:
-		return 8
-	case TensorTypeF64:
-		return 8
-	case tensorTypeIQ1_M:
-		return t.blockSize()/8 + t.blockSize()/16 + t.blockSize()/32
-	case TensorTypeBF16:
-		return 2
-	default:
-		return 0
-	}
-}
-
-func (t TensorType) blockSize() int64 {
-	switch t {
-	case TensorTypeF32,
-		TensorTypeF16,
-		TensorTypeI8,
-		TensorTypeI16,
-		TensorTypeI32,
-		TensorTypeI64,
-		TensorTypeF64,
-		TensorTypeBF16:
-		return 1
-	case TensorTypeQ4_0,
-		TensorTypeQ4_1,
-		TensorTypeQ5_0,
-		TensorTypeQ5_1,
-		TensorTypeQ8_0,
-		TensorTypeQ8_1,
-		tensorTypeIQ4_NL:
-		return 32
-	default:
-		return 256
-	}
-}
-
-func (t TensorType) String() string {
-	switch t {
-	case TensorTypeF32:
-		return "f32"
-	case TensorTypeF16:
-		return "f16"
-	case TensorTypeQ4_0:
-		return "q4_0"
-	case TensorTypeQ4_1:
-		return "q4_1"
-	case tensorTypeQ4_2:
-		return "q4_2"
-	case tensorTypeQ4_3:
-		return "q4_3"
-	case TensorTypeQ5_0:
-		return "q5_0"
-	case TensorTypeQ5_1:
-		return "q5_1"
-	case TensorTypeQ8_0:
-		return "q8_0"
-	case TensorTypeQ8_1:
-		return "q8_1"
-	case TensorTypeQ2_K:
-		return "q2_k"
-	case TensorTypeQ3_K:
-		return "q3_k"
-	case TensorTypeQ4_K:
-		return "q4_k"
-	case TensorTypeQ5_K:
-		return "q5_k"
-	case TensorTypeQ6_K:
-		return "q6_k"
-	case TensorTypeQ8_K:
-		return "q8_k"
-	case tensorTypeIQ2_XXS:
-		return "iq2_xxs"
-	case tensorTypeIQ2_XS:
-		return "iq2_xs"
-	case tensorTypeIQ3_XXS:
-		return "iq3_xxs"
-	case tensorTypeIQ1_S:
-		return "iq1_s"
-	case tensorTypeIQ4_NL:
-		return "iq4_nl"
-	case tensorTypeIQ3_S:
-		return "iq3_s"
-	case tensorTypeIQ2_S:
-		return "iq2_s"
-	case tensorTypeIQ4_XS:
-		return "iq4_xs"
-	case TensorTypeI8:
-		return "i8"
-	case TensorTypeI16:
-		return "i16"
-	case TensorTypeI32:
-		return "i32"
-	case TensorTypeI64:
-		return "i64"
-	case TensorTypeF64:
-		return "f64"
-	case tensorTypeIQ1_M:
-		return "iq1_m"
-	case TensorTypeBF16:
-		return "bf16"
-	case tensorTypeQ4_0_4_4:
-		return "q4_0_4_4"
-	case tensorTypeQ4_0_4_8:
-		return "q4_0_4_8"
-	case tensorTypeQ4_0_8_8:
-		return "q4_0_8_8"
-	case tensorTypeTQ1_0:
-		return "tq1_0"
-	case tensorTypeTQ2_0:
-		return "tq2_0"
-	case tensorTypeIQ4_NL_4_4:
-		return "iq4_nl_4_4"
-	case tensorTypeIQ4_NL_4_8:
-		return "iq4_nl_4_8"
-	case tensorTypeIQ4_NL_8_8:
-		return "iq4_nl_8_8"
-	default:
-		return "unknown"
-	}
-}
-
-func (t TensorType) LogValue() slog.Value {
-	return slog.GroupValue(
-		slog.Uint64("value", uint64(t)),
-		slog.String("name", strings.ToUpper(t.String())),
-		slog.Int64("size", t.typeSize()),
-		slog.Int64("block_size", t.blockSize()),
-		slog.Float64("num_bytes", t.NumBytes()),
-	)
-}

go.mod

@@ -11,7 +11,7 @@ require (
 	github.com/spf13/cobra v1.7.0
 	github.com/stretchr/testify v1.9.0
 	github.com/x448/float16 v0.8.4
-	golang.org/x/sync v0.12.0
+	golang.org/x/sync v0.11.0
 )
 
 require (
@@ -70,12 +70,12 @@ require (
 	github.com/twitchyliquid64/golang-asm v0.15.1 // indirect
 	github.com/ugorji/go/codec v1.2.12 // indirect
 	golang.org/x/arch v0.8.0 // indirect
-	golang.org/x/crypto v0.36.0
+	golang.org/x/crypto v0.33.0
 	golang.org/x/exp v0.0.0-20250218142911-aa4b98e5adaa
-	golang.org/x/net v0.38.0 // indirect
-	golang.org/x/sys v0.31.0
-	golang.org/x/term v0.30.0
-	golang.org/x/text v0.23.0
+	golang.org/x/net v0.35.0 // indirect
+	golang.org/x/sys v0.30.0
+	golang.org/x/term v0.29.0
+	golang.org/x/text v0.22.0
 	google.golang.org/protobuf v1.34.1
 	gopkg.in/yaml.v3 v3.0.1 // indirect
 )

go.sum

@@ -214,8 +214,8 @@ golang.org/x/crypto v0.0.0-20190308221718-c2843e01d9a2/go.mod h1:djNgcEr1/C05ACk
 golang.org/x/crypto v0.0.0-20190510104115-cbcb75029529/go.mod h1:yigFU9vqHzYiE8UmvKecakEJjdnWj3jj499lnFckfCI=
 golang.org/x/crypto v0.0.0-20191011191535-87dc89f01550/go.mod h1:yigFU9vqHzYiE8UmvKecakEJjdnWj3jj499lnFckfCI=
 golang.org/x/crypto v0.0.0-20200622213623-75b288015ac9/go.mod h1:LzIPMQfyMNhhGPhUkYOs5KpL4U8rLKemX1yGLhDgUto=
-golang.org/x/crypto v0.36.0 h1:AnAEvhDddvBdpY+uR+MyHmuZzzNqXSe/GvuDeob5L34=
-golang.org/x/crypto v0.36.0/go.mod h1:Y4J0ReaxCR1IMaabaSMugxJES1EpwhBHhv2bDHklZvc=
+golang.org/x/crypto v0.33.0 h1:IOBPskki6Lysi0lo9qQvbxiQ+FvsCC/YWOecCHAixus=
+golang.org/x/crypto v0.33.0/go.mod h1:bVdXmD7IV/4GdElGPozy6U7lWdRXA4qyRVGJV57uQ5M=
 golang.org/x/exp v0.0.0-20180321215751-8460e604b9de/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA=
 golang.org/x/exp v0.0.0-20180807140117-3d87b88a115f/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA=
 golang.org/x/exp v0.0.0-20190121172915-509febef88a4/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA=
@@ -257,8 +257,8 @@ golang.org/x/net v0.0.0-20200822124328-c89045814202/go.mod h1:/O7V0waA8r7cgGh81R
 golang.org/x/net v0.0.0-20201021035429-f5854403a974/go.mod h1:sp8m0HH+o8qH0wwXwYZr8TS3Oi6o0r6Gce1SSxlDquU=
 golang.org/x/net v0.0.0-20210405180319-a5a99cb37ef4/go.mod h1:p54w0d4576C0XHj96bSt6lcn1PtDYWL6XObtHCRCNQM=
 golang.org/x/net v0.0.0-20210614182718-04defd469f4e/go.mod h1:9nx3DQGgdP8bBQD5qxJ1jj9UTztislL4KSBs9R2vV5Y=
-golang.org/x/net v0.38.0 h1:vRMAPTMaeGqVhG5QyLJHqNDwecKTomGeqbnfZyKlBI8=
-golang.org/x/net v0.38.0/go.mod h1:ivrbrMbzFq5J41QOQh0siUuly180yBYtLp+CKbEaFx8=
+golang.org/x/net v0.35.0 h1:T5GQRQb2y08kTAByq9L4/bz8cipCdA8FbRTXewonqY8=
+golang.org/x/net v0.35.0/go.mod h1:EglIi67kWsHKlRzzVMUD93VMSWGFOMSZgxFjparz1Qk=
 golang.org/x/oauth2 v0.0.0-20180821212333-d2e6202438be/go.mod h1:N/0e6XlmueqKjAGxoOufVs8QHGRruUQn6yWY3a++T0U=
 golang.org/x/oauth2 v0.0.0-20200107190931-bf48bf16ab8d/go.mod h1:gOpvHmFTYa4IltrdGE7lF6nIHvwfUNPOp7c8zoXwtLw=
 golang.org/x/sync v0.0.0-20180314180146-1d60e4601c6f/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
@@ -268,8 +268,8 @@ golang.org/x/sync v0.0.0-20190423024810-112230192c58/go.mod h1:RxMgew5VJxzue5/jJ
 golang.org/x/sync v0.0.0-20190911185100-cd5d95a43a6e/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
 golang.org/x/sync v0.0.0-20201020160332-67f06af15bc9/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
 golang.org/x/sync v0.0.0-20210220032951-036812b2e83c/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
-golang.org/x/sync v0.12.0 h1:MHc5BpPuC30uJk597Ri8TV3CNZcTLu6B6z4lJy+g6Jw=
-golang.org/x/sync v0.12.0/go.mod h1:1dzgHSNfp02xaA81J2MS99Qcpr2w7fw1gpm99rleRqA=
+golang.org/x/sync v0.11.0 h1:GGz8+XQP4FvTTrjZPzNKTMFtSXH80RAzG+5ghFPgK9w=
+golang.org/x/sync v0.11.0/go.mod h1:Czt+wKu1gCyEFDUtn0jG5QVvpJ6rzVqr5aXyt9drQfk=
 golang.org/x/sys v0.0.0-20180830151530-49385e6e1522/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
 golang.org/x/sys v0.0.0-20190215142949-d0b11bdaac8a/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
 golang.org/x/sys v0.0.0-20190312061237-fead79001313/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
@@ -285,17 +285,17 @@ golang.org/x/sys v0.0.0-20210510120138-977fb7262007/go.mod h1:oPkhp1MJrh7nUepCBc
 golang.org/x/sys v0.0.0-20210630005230-0f9fa26af87c/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.5.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.6.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
-golang.org/x/sys v0.31.0 h1:ioabZlmFYtWhL+TRYpcnNlLwhyxaM9kWTDEmfnprqik=
-golang.org/x/sys v0.31.0/go.mod h1:BJP2sWEmIv4KK5OTEluFJCKSidICx8ciO85XgH3Ak8k=
+golang.org/x/sys v0.30.0 h1:QjkSwP/36a20jFYWkSue1YwXzLmsV5Gfq7Eiy72C1uc=
+golang.org/x/sys v0.30.0/go.mod h1:/VUhepiaJMQUp4+oa/7Zr1D23ma6VTLIYjOOTFZPUcA=
 golang.org/x/term v0.0.0-20201126162022-7de9c90e9dd1/go.mod h1:bj7SfCRtBDWHUb9snDiAeCFNEtKQo2Wmx5Cou7ajbmo=
-golang.org/x/term v0.30.0 h1:PQ39fJZ+mfadBm0y5WlL4vlM7Sx1Hgf13sMIY2+QS9Y=
-golang.org/x/term v0.30.0/go.mod h1:NYYFdzHoI5wRh/h5tDMdMqCqPJZEuNqVR5xJLd/n67g=
+golang.org/x/term v0.29.0 h1:L6pJp37ocefwRRtYPKSWOWzOtWSxVajvz2ldH/xi3iU=
+golang.org/x/term v0.29.0/go.mod h1:6bl4lRlvVuDgSf3179VpIxBF0o10JUpXWOnI7nErv7s=
 golang.org/x/text v0.3.0/go.mod h1:NqM8EUOU14njkJ3fqMW+pc6Ldnwhi/IjpwHt7yyuwOQ=
 golang.org/x/text v0.3.3/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
 golang.org/x/text v0.3.5/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
 golang.org/x/text v0.3.6/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
-golang.org/x/text v0.23.0 h1:D71I7dUrlY+VX0gQShAThNGHFxZ13dGLBHQLVl1mJlY=
-golang.org/x/text v0.23.0/go.mod h1:/BLNzu4aZCJ1+kcD0DNRotWKage4q2rGVAg4o22unh4=
+golang.org/x/text v0.22.0 h1:bofq7m3/HAFvbF51jz3Q9wLg3jkvSPuiZu/pD1XwgtM=
+golang.org/x/text v0.22.0/go.mod h1:YRoo4H8PVmsu+E3Ou7cqLVH8oXWIHVoX0jqUWALQhfY=
 golang.org/x/tools v0.0.0-20180525024113-a5b4c53f6e8b/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
 golang.org/x/tools v0.0.0-20180917221912-90fa682c2a6e/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
 golang.org/x/tools v0.0.0-20190114222345-bf090417da8b/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=

integration/embed_test.go

@@ -34,15 +34,13 @@ func cosineSimilarity[V float32 | float64](v1, v2 []V) V {
 func TestAllMiniLMEmbeddings(t *testing.T) {
 	ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
 	defer cancel()
-	client, _, cleanup := InitServerConnection(ctx, t)
-	defer cleanup()
 
 	req := api.EmbeddingRequest{
 		Model:  "all-minilm",
 		Prompt: "why is the sky blue?",
 	}
 
-	res, err := embeddingTestHelper(ctx, client, t, req)
+	res, err := embeddingTestHelper(ctx, t, req)
 
 	if err != nil {
 		t.Fatalf("error: %v", err)
@@ -64,15 +62,13 @@ func TestAllMiniLMEmbeddings(t *testing.T) {
 func TestAllMiniLMEmbed(t *testing.T) {
 	ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
 	defer cancel()
-	client, _, cleanup := InitServerConnection(ctx, t)
-	defer cleanup()
 
 	req := api.EmbedRequest{
 		Model: "all-minilm",
 		Input: "why is the sky blue?",
 	}
 
-	res, err := embedTestHelper(ctx, client, t, req)
+	res, err := embedTestHelper(ctx, t, req)
 
 	if err != nil {
 		t.Fatalf("error: %v", err)
@@ -102,15 +98,13 @@ func TestAllMiniLMEmbed(t *testing.T) {
 func TestAllMiniLMBatchEmbed(t *testing.T) {
 	ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
 	defer cancel()
-	client, _, cleanup := InitServerConnection(ctx, t)
-	defer cleanup()
 
 	req := api.EmbedRequest{
 		Model: "all-minilm",
 		Input: []string{"why is the sky blue?", "why is the grass green?"},
 	}
 
-	res, err := embedTestHelper(ctx, client, t, req)
+	res, err := embedTestHelper(ctx, t, req)
 
 	if err != nil {
 		t.Fatalf("error: %v", err)
@@ -150,8 +144,6 @@ func TestAllMiniLMBatchEmbed(t *testing.T) {
 func TestAllMiniLMEmbedTruncate(t *testing.T) {
 	ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
 	defer cancel()
-	client, _, cleanup := InitServerConnection(ctx, t)
-	defer cleanup()
 
 	truncTrue, truncFalse := true, false
 
@@ -190,7 +182,7 @@ func TestAllMiniLMEmbedTruncate(t *testing.T) {
 	res := make(map[string]*api.EmbedResponse)
 
 	for _, req := range reqs {
-		response, err := embedTestHelper(ctx, client, t, req.Request)
+		response, err := embedTestHelper(ctx, t, req.Request)
 		if err != nil {
 			t.Fatalf("error: %v", err)
 		}
@@ -206,7 +198,7 @@ func TestAllMiniLMEmbedTruncate(t *testing.T) {
 	}
 
 	// check that truncate set to false returns an error if context length is exceeded
-	_, err := embedTestHelper(ctx, client, t, api.EmbedRequest{
+	_, err := embedTestHelper(ctx, t, api.EmbedRequest{
 		Model:    "all-minilm",
 		Input:    "why is the sky blue?",
 		Truncate: &truncFalse,
@@ -218,7 +210,9 @@ func TestAllMiniLMEmbedTruncate(t *testing.T) {
 	}
 }
 
-func embeddingTestHelper(ctx context.Context, client *api.Client, t *testing.T, req api.EmbeddingRequest) (*api.EmbeddingResponse, error) {
+func embeddingTestHelper(ctx context.Context, t *testing.T, req api.EmbeddingRequest) (*api.EmbeddingResponse, error) {
+	client, _, cleanup := InitServerConnection(ctx, t)
+	defer cleanup()
 	if err := PullIfMissing(ctx, client, req.Model); err != nil {
 		t.Fatalf("failed to pull model %s: %v", req.Model, err)
 	}
@@ -232,7 +226,9 @@ func embeddingTestHelper(ctx context.Context, client *api.Client, t *testing.T,
 	return response, nil
 }
 
-func embedTestHelper(ctx context.Context, client *api.Client, t *testing.T, req api.EmbedRequest) (*api.EmbedResponse, error) {
+func embedTestHelper(ctx context.Context, t *testing.T, req api.EmbedRequest) (*api.EmbedResponse, error) {
+	client, _, cleanup := InitServerConnection(ctx, t)
+	defer cleanup()
 	if err := PullIfMissing(ctx, client, req.Model); err != nil {
 		t.Fatalf("failed to pull model %s: %v", req.Model, err)
 	}

integration/llm_image_test.go

@@ -19,7 +19,7 @@ func TestVisionModels(t *testing.T) {
 	}
 	testCases := []testCase{
 		{
-			model: "qwen2.5vl",
+			model: "llava:7b",
 		},
 		{
 			model: "llama3.2-vision",
@@ -60,7 +60,6 @@ func TestVisionModels(t *testing.T) {
 }
 
 func TestIntegrationSplitBatch(t *testing.T) {
-	skipUnderMinVRAM(t, 6)
 	image, err := base64.StdEncoding.DecodeString(imageEncoding)
 	require.NoError(t, err)
 	req := api.GenerateRequest{

integration/model_arch_test.go

@@ -48,6 +48,17 @@ var (
 	}
 )
 
+func getTimeouts(t *testing.T) (soft time.Duration, hard time.Duration) {
+	deadline, hasDeadline := t.Deadline()
+	if !hasDeadline {
+		return 8 * time.Minute, 10 * time.Minute
+	} else if deadline.Compare(time.Now().Add(2*time.Minute)) <= 0 {
+		t.Skip("too little time")
+		return time.Duration(0), time.Duration(0)
+	}
+	return -time.Since(deadline.Add(-2 * time.Minute)), -time.Since(deadline.Add(-20 * time.Second))
+}
+
 func TestModelsGenerate(t *testing.T) {
 	softTimeout, hardTimeout := getTimeouts(t)
 	slog.Info("Setting timeouts", "soft", softTimeout, "hard", hardTimeout)

integration/quantization_test.go

@@ -1,130 +0,0 @@
-//go:build integration && models
-
-package integration
-
-import (
-	"bytes"
-	"context"
-	"fmt"
-	"log/slog"
-	"strings"
-	"testing"
-	"time"
-
-	"github.com/ollama/ollama/api"
-)
-
-func TestQuantization(t *testing.T) {
-	sourceModels := []string{
-		"qwen2.5:0.5b-instruct-fp16",
-	}
-	quantizations := []string{
-		"Q8_0",
-		"Q4_K_S",
-		"Q4_K_M",
-		"Q4_K",
-	}
-	softTimeout, hardTimeout := getTimeouts(t)
-	started := time.Now()
-	slog.Info("Setting timeouts", "soft", softTimeout, "hard", hardTimeout)
-	ctx, cancel := context.WithTimeout(context.Background(), hardTimeout)
-	defer cancel()
-	client, _, cleanup := InitServerConnection(ctx, t)
-	defer cleanup()
-
-	for _, base := range sourceModels {
-		if err := PullIfMissing(ctx, client, base); err != nil {
-			t.Fatalf("pull failed %s", err)
-		}
-		for _, quant := range quantizations {
-			newName := fmt.Sprintf("%s__%s", base, quant)
-			t.Run(newName, func(t *testing.T) {
-				if time.Now().Sub(started) > softTimeout {
-					t.Skip("skipping remaining tests to avoid excessive runtime")
-				}
-				req := &api.CreateRequest{
-					Model:        newName,
-					Quantization: quant,
-					From:         base,
-				}
-				fn := func(resp api.ProgressResponse) error {
-					// fmt.Print(".")
-					return nil
-				}
-				t.Logf("quantizing: %s -> %s", base, quant)
-				if err := client.Create(ctx, req, fn); err != nil {
-					t.Fatalf("create failed %s", err)
-				}
-				defer func() {
-					req := &api.DeleteRequest{
-						Model: newName,
-					}
-					t.Logf("deleting: %s -> %s", base, quant)
-					if err := client.Delete(ctx, req); err != nil {
-						t.Logf("failed to clean up %s: %s", req.Model, err)
-					}
-				}()
-				// Check metadata on the model
-				resp, err := client.Show(ctx, &api.ShowRequest{Name: newName})
-				if err != nil {
-					t.Fatalf("unable to show model: %s", err)
-				}
-				if !strings.Contains(resp.Details.QuantizationLevel, quant) {
-					t.Fatalf("unexpected quantization for %s:\ngot: %s", newName, resp.Details.QuantizationLevel)
-				}
-
-				stream := true
-				genReq := api.GenerateRequest{
-					Model:     newName,
-					Prompt:    "why is the sky blue?",
-					KeepAlive: &api.Duration{Duration: 3 * time.Second},
-					Options: map[string]any{
-						"seed":        42,
-						"temperature": 0.0,
-					},
-					Stream: &stream,
-				}
-				t.Logf("verifying: %s -> %s", base, quant)
-
-				// Some smaller quantizations can cause models to have poor quality
-				// or get stuck in repetition loops, so we stop as soon as we have any matches
-				anyResp := []string{"rayleigh", "scattering", "day", "sun", "moon", "color", "nitrogen", "oxygen"}
-				reqCtx, reqCancel := context.WithCancel(ctx)
-				atLeastOne := false
-				var buf bytes.Buffer
-				genfn := func(response api.GenerateResponse) error {
-					buf.Write([]byte(response.Response))
-					fullResp := strings.ToLower(buf.String())
-					for _, resp := range anyResp {
-						if strings.Contains(fullResp, resp) {
-							atLeastOne = true
-							t.Log(fullResp)
-							reqCancel()
-							break
-						}
-					}
-					return nil
-				}
-
-				done := make(chan int)
-				var genErr error
-				go func() {
-					genErr = client.Generate(reqCtx, &genReq, genfn)
-					done <- 0
-				}()
-
-				select {
-				case <-done:
-					if genErr != nil && !atLeastOne {
-						t.Fatalf("failed with %s request prompt %s ", genReq.Model, genReq.Prompt)
-					}
-				case <-ctx.Done():
-					t.Error("outer test context done while waiting for generate")
-				}
-
-				t.Logf("passed")
-
-			})
-		}
-	}
-}

integration/utils_test.go

@@ -217,7 +217,6 @@ func InitServerConnection(ctx context.Context, t *testing.T) (*api.Client, strin
 					slog.Error("failed to open server log", "logfile", lifecycle.ServerLogFile, "error", err)
 					return
 				}
-				defer fp.Close()
 				data, err := io.ReadAll(fp)
 				if err != nil {
 					slog.Error("failed to read server log", "logfile", lifecycle.ServerLogFile, "error", err)
@@ -359,14 +358,3 @@ func skipUnderMinVRAM(t *testing.T, gb uint64) {
 		}
 	}
 }
-
-func getTimeouts(t *testing.T) (soft time.Duration, hard time.Duration) {
-	deadline, hasDeadline := t.Deadline()
-	if !hasDeadline {
-		return 8 * time.Minute, 10 * time.Minute
-	} else if deadline.Compare(time.Now().Add(2*time.Minute)) <= 0 {
-		t.Skip("too little time")
-		return time.Duration(0), time.Duration(0)
-	}
-	return -time.Since(deadline.Add(-2 * time.Minute)), -time.Since(deadline.Add(-20 * time.Second))
-}

kvcache/causal.go

@@ -30,11 +30,6 @@ type Causal struct {
 
 	// ** current forward pass **
 
-	// curReserve indicates that this forward pass is only for
-	// memory reservation and we should not update our metadata
-	// based on it.
-	curReserve bool
-
 	// the active layer for Get and Put
 	curLayer int
 
@@ -164,13 +159,12 @@ func (c *Causal) Close() {
 }
 
 func (c *Causal) StartForward(ctx ml.Context, batch input.Batch, reserve bool) error {
-	c.curReserve = reserve
 	c.curBatchSize = len(batch.Positions)
 	c.curSequences = batch.Sequences
 	c.curPositions = batch.Positions
 	c.opts.Except = nil
 
-	if !c.curReserve {
+	if !reserve {
 		c.updateSlidingWindow()
 
 		var err error
@@ -217,9 +211,10 @@ func (c *Causal) StartForward(ctx ml.Context, batch input.Batch, reserve bool) e
 		c.curCellRange.max = len(c.cells) - 1
 	}
 
-	c.curMask = c.buildMask(ctx)
+	var err error
+	c.curMask, err = c.buildMask(ctx)
 
-	return nil
+	return err
 }
 
 func newRange() cellRange {
@@ -244,7 +239,7 @@ func (c *Causal) findStartLoc() (int, error) {
 		}
 	}
 
-	return 0, fmt.Errorf("%w (cache: %v batch: %v)", ErrKvCacheFull, len(c.cells), c.curBatchSize)
+	return 0, fmt.Errorf("%w (length: %v)", ErrKvCacheFull, len(c.cells))
 }
 
 func (c *Causal) updateSlidingWindow() {
@@ -302,7 +297,7 @@ func roundUp(length, pad int) int {
 // Builds a mask of history x batch indicating whether for each token in the batch the
 // token in the history should apply. This is based on both the sequence and causality (the
 // position of the history is not ahead of the token in the batch).
-func (c *Causal) buildMask(ctx ml.Context) ml.Tensor {
+func (c *Causal) buildMask(ctx ml.Context) (ml.Tensor, error) {
 	// Align and pad the two dimensions as required by the backend
 	batchSize := roundUp(c.curBatchSize, c.config.MaskBatchPadding)
 
@@ -310,11 +305,6 @@ func (c *Causal) buildMask(ctx ml.Context) ml.Tensor {
 	c.curCellRange.max = roundUp(c.curCellRange.max+1, c.config.CachePadding) - 1
 
 	length := c.curCellRange.max - c.curCellRange.min + 1
-
-	if c.curReserve {
-		return ctx.Input().Empty(c.config.MaskDType, length, batchSize)
-	}
-
 	mask := make([]float32, batchSize*length)
 
 	for i := range c.curBatchSize {
@@ -335,7 +325,10 @@ func (c *Causal) buildMask(ctx ml.Context) ml.Tensor {
 		mask[i] = float32(math.Inf(-1))
 	}
 
-	maskTensor := ctx.Input().FromFloatSlice(mask, length, batchSize)
+	maskTensor, err := ctx.Input().FromFloatSlice(mask, length, batchSize)
+	if err != nil {
+		return nil, err
+	}
 
 	if c.config.MaskDType != ml.DTypeF32 {
 		out := ctx.Input().Empty(c.config.MaskDType, maskTensor.Shape()...)
@@ -343,7 +336,7 @@ func (c *Causal) buildMask(ctx ml.Context) ml.Tensor {
 		maskTensor = out
 	}
 
-	return maskTensor
+	return maskTensor, nil
 }
 
 func (c *Causal) moveCells(ctx ml.Context, src, dst, length int) {
@@ -498,7 +491,12 @@ func (c *Causal) SetCausal(ctx ml.Context, opts CausalOptions) {
 	if !slices.Equal(c.opts.Except, opts.Except) {
 		c.opts = opts
 		if ctx != nil {
-			c.curMask = c.buildMask(ctx)
+			var err error
+			c.curMask, err = c.buildMask(ctx)
+			if err != nil {
+				// This error should never occur because we have previously built a mask with the same shape
+				panic(fmt.Errorf("SetCausal: %w", err))
+			}
 		}
 	}
 }
@@ -654,7 +652,10 @@ func (c *Causal) shift(seq int, beginIndex, offset int32) error {
 		}
 	}
 
-	kShift := ctx.Input().FromIntSlice(offsets, len(offsets))
+	kShift, err := ctx.Input().FromIntSlice(offsets, len(offsets))
+	if err != nil {
+		return err
+	}
 
 	for i, key := range c.keys {
 		if key == nil {

kvcache/causal_test.go

@@ -344,7 +344,7 @@ func testCache(t *testing.T, backend ml.Backend, cache Cache, tests []testCase)
 			}
 
 			cache.SetLayer(0)
-			tensor := context.FromFloatSlice(test.in, test.inShape...)
+			tensor, _ := context.FromFloatSlice(test.in, test.inShape...)
 			cache.Put(context, tensor, tensor)
 
 			out, _, mask := cache.Get(context)
@@ -386,7 +386,7 @@ func TestCanResume(t *testing.T) {
 	}
 
 	cache.SetLayer(0)
-	tensor := context.FromFloatSlice([]float32{1, 2, 3, 4}, 1, 1, 4)
+	tensor, _ := context.FromFloatSlice([]float32{1, 2, 3, 4}, 1, 1, 4)
 	cache.Put(context, tensor, tensor)
 
 	// with window size 4, nothing has slid out of the window yet
@@ -413,7 +413,7 @@ func TestCanResume(t *testing.T) {
 	}
 
 	cache.SetLayer(0)
-	tensor = context.FromFloatSlice([]float32{5, 6}, 1, 1, 2)
+	tensor, _ = context.FromFloatSlice([]float32{5, 6}, 1, 1, 2)
 	cache.Put(context, tensor, tensor)
 
 	// only the latest position has overlapping windows
@@ -470,24 +470,24 @@ func (c *testContext) Zeros(dtype ml.DType, shape ...int) ml.Tensor {
 	return c.Empty(dtype, shape...)
 }
 
-func (c *testContext) FromFloatSlice(s []float32, shape ...int) ml.Tensor {
+func (c *testContext) FromFloatSlice(s []float32, shape ...int) (ml.Tensor, error) {
 	t := c.Empty(ml.DTypeF32, shape...).(*testTensor)
 
 	copy(t.data, s)
 
-	return t
+	return t, nil
 }
 
-func (c *testContext) FromIntSlice(s []int32, shape ...int) ml.Tensor {
+func (c *testContext) FromIntSlice(s []int32, shape ...int) (ml.Tensor, error) {
 	f := make([]float32, len(s))
 	for i := range f {
 		f[i] = float32(s[i])
 	}
 
-	out := c.FromFloatSlice(f, shape...)
+	out, _ := c.FromFloatSlice(f, shape...)
 	out.(*testTensor).dtype = ml.DTypeI32
 
-	return out
+	return out, nil
 }
 
 func (c *testContext) Arange(start, stop, step float32, dtype ml.DType) ml.Tensor {
@@ -496,7 +496,7 @@ func (c *testContext) Arange(start, stop, step float32, dtype ml.DType) ml.Tenso
 		s = append(s, i)
 	}
 
-	out := c.FromFloatSlice(s, len(s))
+	out, _ := c.FromFloatSlice(s, len(s))
 	out.(*testTensor).dtype = dtype
 	return out
 }
@@ -508,7 +508,7 @@ func (c *testContext) Forward(...ml.Tensor) ml.Context { return c }
 
 func (c *testContext) Compute(...ml.Tensor) {}
 
-func (c *testContext) Reserve() {}
+func (c *testContext) Reserve() error { return nil }
 
 func (c *testContext) MaxGraphNodes() int {
 	return 10

llama/build-info.cpp

@@ -1,4 +1,4 @@
 int LLAMA_BUILD_NUMBER = 0;
-char const *LLAMA_COMMIT = "de4c07f93783a1a96456a44dc16b9db538ee1618";
+char const *LLAMA_COMMIT = "2016f07bd106c73699ecbaace80f55db5ed95dac";
 char const *LLAMA_COMPILER = "";
 char const *LLAMA_BUILD_TARGET = "";

llama/llama.cpp/.rsync-filter

@@ -10,11 +10,11 @@ include common/stb_image.*
 include include/
 include include/llama.*
 include include/llama-*.*
-include tools/
-include tools/mtmd/
-include tools/mtmd/clip.*
-include tools/mtmd/clip-impl.*
-include tools/mtmd/llava.*
+include examples/
+include examples/llava/
+include examples/llava/clip.*
+include examples/llava/clip-impl.*
+include examples/llava/llava.*
 include src/
 include src/llama.*
 include src/llama-*.*

llama/llama.cpp/common/common.cpp

@@ -1096,6 +1096,7 @@ struct llama_context_params common_context_params_to_llama(const common_params &
     cparams.n_threads         = params.cpuparams.n_threads;
     cparams.n_threads_batch   = params.cpuparams_batch.n_threads == -1 ?
                                 params.cpuparams.n_threads : params.cpuparams_batch.n_threads;
+    cparams.logits_all        = params.logits_all;
     cparams.embeddings        = params.embedding;
     cparams.rope_scaling_type = params.rope_scaling_type;
     cparams.rope_freq_base    = params.rope_freq_base;
@@ -1113,7 +1114,6 @@ struct llama_context_params common_context_params_to_llama(const common_params &
     cparams.offload_kqv       = !params.no_kv_offload;
     cparams.flash_attn        = params.flash_attn;
     cparams.no_perf           = params.no_perf;
-    cparams.op_offload        = !params.no_op_offload;
 
     if (params.reranking) {
         cparams.embeddings    = true;
@@ -1565,20 +1565,3 @@ common_control_vector_data common_control_vector_load(const std::vector<common_c
 
     return result;
 }
-
-ggml_opt_dataset_t common_opt_dataset_init(struct llama_context * ctx, const std::vector<llama_token> & tokens, int64_t stride) {
-    const int64_t ne_datapoint = llama_n_ctx(ctx);
-    const int64_t ndata        = (tokens.size() - ne_datapoint - 1) / stride;
-    ggml_opt_dataset_t result = ggml_opt_dataset_init(
-        GGML_TYPE_I32, GGML_TYPE_I32, ne_datapoint, ne_datapoint, ndata, /*ndata_shard =*/ 1);
-
-    llama_token * data   = (llama_token *) ggml_opt_dataset_data(result)->data;
-    llama_token * labels = (llama_token *) ggml_opt_dataset_labels(result)->data;
-
-    for (int64_t idata = 0; idata < ndata; ++idata) {
-        memcpy(data   + idata*ne_datapoint, tokens.data() + idata*stride + 0, ne_datapoint*sizeof(llama_token));
-        memcpy(labels + idata*ne_datapoint, tokens.data() + idata*stride + 1, ne_datapoint*sizeof(llama_token));
-    }
-
-    return result;
-}

llama/llama.cpp/common/common.h

@@ -66,6 +66,7 @@ enum llama_example {
     LLAMA_EXAMPLE_COMMON,
     LLAMA_EXAMPLE_SPECULATIVE,
     LLAMA_EXAMPLE_MAIN,
+    LLAMA_EXAMPLE_INFILL,
     LLAMA_EXAMPLE_EMBEDDING,
     LLAMA_EXAMPLE_PERPLEXITY,
     LLAMA_EXAMPLE_RETRIEVAL,
@@ -95,7 +96,6 @@ enum common_sampler_type {
     COMMON_SAMPLER_TYPE_XTC         = 8,
     COMMON_SAMPLER_TYPE_INFILL      = 9,
     COMMON_SAMPLER_TYPE_PENALTIES   = 10,
-    COMMON_SAMPLER_TYPE_TOP_N_SIGMA = 11,
 };
 
 // dimensionality reduction methods, used by cvector-generator
@@ -161,7 +161,6 @@ struct common_params_sampling {
     std::vector<enum common_sampler_type> samplers = {
         COMMON_SAMPLER_TYPE_PENALTIES,
         COMMON_SAMPLER_TYPE_DRY,
-        COMMON_SAMPLER_TYPE_TOP_N_SIGMA,
         COMMON_SAMPLER_TYPE_TOP_K,
         COMMON_SAMPLER_TYPE_TYPICAL_P,
         COMMON_SAMPLER_TYPE_TOP_P,
@@ -324,6 +323,7 @@ struct common_params {
     bool ctx_shift         = true;  // context shift on inifinite text generation
 
     bool input_prefix_bos  = false; // prefix BOS to user inputs, preceding input_prefix
+    bool logits_all        = false; // return logits for all tokens in the batch
     bool use_mmap          = true;  // use mmap for faster loads
     bool use_mlock         = false; // use mlock to keep model in memory
     bool verbose_prompt    = false; // print prompt tokens before generation
@@ -332,7 +332,6 @@ struct common_params {
     bool no_kv_offload     = false; // disable KV offloading
     bool warmup            = true;  // warmup run
     bool check_tensors     = false; // validate tensor data
-    bool no_op_offload     = false; // globally disable offload host tensor operations to device
 
     bool single_turn       = false; // single turn chat conversation
 
@@ -341,10 +340,8 @@ struct common_params {
 
     common_conversation_mode conversation_mode = COMMON_CONVERSATION_MODE_AUTO;
 
-    // multimodal models (see tools/mtmd)
+    // multimodal models (see examples/llava)
     struct common_params_model mmproj;
-    bool mmproj_use_gpu = true;     // use GPU for multimodal model
-    bool no_mmproj = false;         // explicitly disable multimodal model
     std::vector<std::string> image; // path to image file(s)
 
     // embedding
@@ -410,14 +407,13 @@ struct common_params {
 
     bool process_output = false; // collect data for the output tensor
     bool compute_ppl    = true;  // whether to compute perplexity
-    bool parse_special  = false; // whether to parse special tokens during imatrix tokenization
 
     // cvector-generator params
     int n_pca_batch = 100;
     int n_pca_iterations = 1000;
     dimre_method cvector_dimre_method = DIMRE_METHOD_PCA;
-    std::string cvector_positive_file = "tools/cvector-generator/positive.txt";
-    std::string cvector_negative_file = "tools/cvector-generator/negative.txt";
+    std::string cvector_positive_file = "examples/cvector-generator/positive.txt";
+    std::string cvector_negative_file = "examples/cvector-generator/negative.txt";
 
     bool spm_infill = false; // suffix/prefix/middle pattern for infill
 
@@ -666,9 +662,3 @@ const char * const LLM_KV_SPLIT_COUNT         = "split.count";
 const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count";
 
 }
-
-//
-// training utils
-//
-
-ggml_opt_dataset_t common_opt_dataset_init(struct llama_context * ctx, const std::vector<llama_token> & tokens, int64_t stride);

llama/llama.cpp/common/json-schema-to-grammar.cpp

@@ -16,9 +16,6 @@ using json = nlohmann::ordered_json;
 static std::string build_repetition(const std::string & item_rule, int min_items, int max_items, const std::string & separator_rule = "") {
     auto has_max = max_items != std::numeric_limits<int>::max();
 
-    if (max_items == 0) {
-        return "";
-    }
     if (min_items == 0 && max_items == 1) {
         return item_rule + "?";
     }

llama/llama.cpp/common/sampling.cpp

@@ -1,7 +1,6 @@
 #include "sampling.h"
 
 #include "common.h"
-#include "log.h"
 
 #include <cmath>
 #include <unordered_map>
@@ -230,48 +229,51 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
                 params.logit_bias.data()));
 
     if (params.mirostat == 0) {
-        for (const auto & cnstr : params.samplers) {
-            switch (cnstr) {
-                case COMMON_SAMPLER_TYPE_DRY:
-                    {
-                        std::vector<const char *> c_breakers;
-                        c_breakers.reserve(params.dry_sequence_breakers.size());
-                        for (const auto & str : params.dry_sequence_breakers) {
-                            c_breakers.push_back(str.c_str());
-                        }
+        if (params.top_n_sigma >= 0) {
+            llama_sampler_chain_add(result->chain, llama_sampler_init_top_k        (params.top_k));
+            llama_sampler_chain_add(result->chain, llama_sampler_init_temp         (params.temp));
+            llama_sampler_chain_add(result->chain, llama_sampler_init_top_n_sigma  (params.top_n_sigma));
+        } else {
+            for (const auto & cnstr : params.samplers) {
+                switch (cnstr) {
+                    case COMMON_SAMPLER_TYPE_DRY:
+                        {
+                            std::vector<const char *> c_breakers;
+                            c_breakers.reserve(params.dry_sequence_breakers.size());
+                            for (const auto & str : params.dry_sequence_breakers) {
+                                c_breakers.push_back(str.c_str());
+                            }
 
-                        llama_sampler_chain_add(result->chain, llama_sampler_init_dry      (vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
-                    }
-                    break;
-                case COMMON_SAMPLER_TYPE_TOP_K:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_top_k       (params.top_k));
-                    break;
-                case COMMON_SAMPLER_TYPE_TOP_P:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_top_p       (params.top_p, params.min_keep));
-                    break;
-                case COMMON_SAMPLER_TYPE_TOP_N_SIGMA:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_top_n_sigma (params.top_n_sigma));
-                    break;
-                case COMMON_SAMPLER_TYPE_MIN_P:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_min_p       (params.min_p, params.min_keep));
-                    break;
-                case COMMON_SAMPLER_TYPE_XTC:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_xtc         (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed));
-                    break;
-                case COMMON_SAMPLER_TYPE_TYPICAL_P:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_typical     (params.typ_p, params.min_keep));
-                    break;
-                case COMMON_SAMPLER_TYPE_TEMPERATURE:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext    (params.temp, params.dynatemp_range, params.dynatemp_exponent));
-                    break;
-                case COMMON_SAMPLER_TYPE_INFILL:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_infill      (vocab));
-                    break;
-                case COMMON_SAMPLER_TYPE_PENALTIES:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_penalties   (params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
-                    break;
-                default:
-                    GGML_ASSERT(false && "unknown sampler type");
+                            llama_sampler_chain_add(result->chain, llama_sampler_init_dry      (vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
+                        }
+                        break;
+                    case COMMON_SAMPLER_TYPE_TOP_K:
+                        llama_sampler_chain_add(result->chain, llama_sampler_init_top_k    (params.top_k));
+                        break;
+                    case COMMON_SAMPLER_TYPE_TOP_P:
+                        llama_sampler_chain_add(result->chain, llama_sampler_init_top_p    (params.top_p, params.min_keep));
+                        break;
+                    case COMMON_SAMPLER_TYPE_MIN_P:
+                        llama_sampler_chain_add(result->chain, llama_sampler_init_min_p    (params.min_p, params.min_keep));
+                        break;
+                    case COMMON_SAMPLER_TYPE_XTC:
+                        llama_sampler_chain_add(result->chain, llama_sampler_init_xtc      (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed));
+                        break;
+                    case COMMON_SAMPLER_TYPE_TYPICAL_P:
+                        llama_sampler_chain_add(result->chain, llama_sampler_init_typical  (params.typ_p, params.min_keep));
+                        break;
+                    case COMMON_SAMPLER_TYPE_TEMPERATURE:
+                        llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent));
+                        break;
+                    case COMMON_SAMPLER_TYPE_INFILL:
+                        llama_sampler_chain_add(result->chain, llama_sampler_init_infill   (vocab));
+                        break;
+                    case COMMON_SAMPLER_TYPE_PENALTIES:
+                        llama_sampler_chain_add(result->chain, llama_sampler_init_penalties(params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
+                        break;
+                    default:
+                        GGML_ASSERT(false && "unknown sampler type");
+                }
             }
         }
         llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed));
@@ -473,7 +475,6 @@ char common_sampler_type_to_chr(enum common_sampler_type cnstr) {
         case COMMON_SAMPLER_TYPE_TOP_K:       return 'k';
         case COMMON_SAMPLER_TYPE_TYPICAL_P:   return 'y';
         case COMMON_SAMPLER_TYPE_TOP_P:       return 'p';
-        case COMMON_SAMPLER_TYPE_TOP_N_SIGMA: return 's';
         case COMMON_SAMPLER_TYPE_MIN_P:       return 'm';
         case COMMON_SAMPLER_TYPE_TEMPERATURE: return 't';
         case COMMON_SAMPLER_TYPE_XTC:         return 'x';
@@ -489,7 +490,6 @@ std::string common_sampler_type_to_str(enum common_sampler_type cnstr) {
         case COMMON_SAMPLER_TYPE_TOP_K:       return "top_k";
         case COMMON_SAMPLER_TYPE_TYPICAL_P:   return "typ_p";
         case COMMON_SAMPLER_TYPE_TOP_P:       return "top_p";
-        case COMMON_SAMPLER_TYPE_TOP_N_SIGMA: return "top_n_sigma";
         case COMMON_SAMPLER_TYPE_MIN_P:       return "min_p";
         case COMMON_SAMPLER_TYPE_TEMPERATURE: return "temperature";
         case COMMON_SAMPLER_TYPE_XTC:         return "xtc";
@@ -504,7 +504,6 @@ std::vector<common_sampler_type> common_sampler_types_from_names(const std::vect
         { "dry",         COMMON_SAMPLER_TYPE_DRY },
         { "top_k",       COMMON_SAMPLER_TYPE_TOP_K },
         { "top_p",       COMMON_SAMPLER_TYPE_TOP_P },
-        { "top_n_sigma", COMMON_SAMPLER_TYPE_TOP_N_SIGMA },
         { "typ_p",       COMMON_SAMPLER_TYPE_TYPICAL_P },
         { "min_p",       COMMON_SAMPLER_TYPE_MIN_P },
         { "temperature", COMMON_SAMPLER_TYPE_TEMPERATURE },
@@ -518,7 +517,6 @@ std::vector<common_sampler_type> common_sampler_types_from_names(const std::vect
     std::unordered_map<std::string, common_sampler_type> sampler_alt_name_map {
         { "top-k",       COMMON_SAMPLER_TYPE_TOP_K },
         { "top-p",       COMMON_SAMPLER_TYPE_TOP_P },
-        { "top-n-sigma", COMMON_SAMPLER_TYPE_TOP_N_SIGMA },
         { "nucleus",     COMMON_SAMPLER_TYPE_TOP_P },
         { "typical-p",   COMMON_SAMPLER_TYPE_TYPICAL_P },
         { "typical",     COMMON_SAMPLER_TYPE_TYPICAL_P },
@@ -535,16 +533,14 @@ std::vector<common_sampler_type> common_sampler_types_from_names(const std::vect
         auto sampler = sampler_canonical_name_map.find(name);
         if (sampler != sampler_canonical_name_map.end()) {
             samplers.push_back(sampler->second);
-            continue;
-        }
-        if (allow_alt_names) {
-            sampler = sampler_alt_name_map.find(name);
-            if (sampler != sampler_alt_name_map.end()) {
-                samplers.push_back(sampler->second);
-                continue;
+        } else {
+            if (allow_alt_names) {
+                sampler = sampler_alt_name_map.find(name);
+                if (sampler != sampler_alt_name_map.end()) {
+                    samplers.push_back(sampler->second);
+                }
             }
         }
-        LOG_WRN("%s: unable to match sampler by name '%s'\n", __func__, name.c_str());
     }
 
     return samplers;
@@ -556,7 +552,6 @@ std::vector<common_sampler_type> common_sampler_types_from_chars(const std::stri
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_K),       COMMON_SAMPLER_TYPE_TOP_K },
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TYPICAL_P),   COMMON_SAMPLER_TYPE_TYPICAL_P },
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_P),       COMMON_SAMPLER_TYPE_TOP_P },
-        { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_N_SIGMA), COMMON_SAMPLER_TYPE_TOP_N_SIGMA },
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_MIN_P),       COMMON_SAMPLER_TYPE_MIN_P },
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TEMPERATURE), COMMON_SAMPLER_TYPE_TEMPERATURE },
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_XTC),         COMMON_SAMPLER_TYPE_XTC },
@@ -571,8 +566,6 @@ std::vector<common_sampler_type> common_sampler_types_from_chars(const std::stri
         const auto sampler = sampler_name_map.find(c);
         if (sampler != sampler_name_map.end()) {
             samplers.push_back(sampler->second);
-        } else {
-            LOG_WRN("%s: unable to match sampler by char '%c'\n", __func__, c);
         }
     }
 

llama/llama.cpp/examples/llava/clip-impl.h

@@ -2,6 +2,8 @@
 #include "gguf.h"
 #include "clip.h"
 
+#include "clip.h"
+
 #include <climits>
 #include <cstdarg>
 #include <string>
@@ -15,29 +17,33 @@
 #define KEY_FTYPE               "general.file_type"
 #define KEY_NAME                "general.name"
 #define KEY_DESCRIPTION         "general.description"
+#define KEY_HAS_TEXT_ENC        "clip.has_text_encoder"
+#define KEY_HAS_VIS_ENC         "clip.has_vision_encoder"
+#define KEY_HAS_LLAVA_PROJ      "clip.has_llava_projector"
+#define KEY_HAS_MINICPMV_PROJ   "clip.has_minicpmv_projector"
+#define KEY_HAS_GLM_PROJ        "clip.has_glm_projector"
 #define KEY_MINICPMV_VERSION    "clip.minicpmv_version"
+#define KEY_HAS_QWEN2VL_MERGER  "clip.has_qwen2vl_merger"
 #define KEY_USE_GELU            "clip.use_gelu"
 #define KEY_USE_SILU            "clip.use_silu"
-#define KEY_N_EMBD              "clip.vision.embedding_length"
-#define KEY_N_FF                "clip.vision.feed_forward_length"
-#define KEY_N_BLOCK             "clip.vision.block_count"
-#define KEY_N_HEAD              "clip.vision.attention.head_count"
-#define KEY_LAYER_NORM_EPS      "clip.vision.attention.layer_norm_epsilon"
-#define KEY_PROJ_DIM            "clip.vision.projection_dim"
+#define KEY_N_EMBD              "clip.%s.embedding_length"
+#define KEY_N_FF                "clip.%s.feed_forward_length"
+#define KEY_N_BLOCK             "clip.%s.block_count"
+#define KEY_N_HEAD              "clip.%s.attention.head_count"
+#define KEY_LAYER_NORM_EPS      "clip.%s.attention.layer_norm_epsilon"
+#define KEY_PROJ_DIM            "clip.%s.projection_dim"
+#define KEY_TOKENS              "tokenizer.ggml.tokens"
+#define KEY_N_POSITIONS         "clip.text.context_length"
 #define KEY_IMAGE_SIZE          "clip.vision.image_size"
 #define KEY_PATCH_SIZE          "clip.vision.patch_size"
 #define KEY_IMAGE_MEAN          "clip.vision.image_mean"
 #define KEY_IMAGE_STD           "clip.vision.image_std"
-#define KEY_FEATURE_LAYER       "clip.vision.feature_layer"
-#define KEY_PROJ_SCALE_FACTOR   "clip.vision.projector.scale_factor"
 #define KEY_PROJ_TYPE           "clip.projector_type"
-#define KEY_SPATIAL_MERGE_SIZE  "clip.vision.spatial_merge_size"
+#define KEY_FEATURE_LAYER       "clip.vision.feature_layer"
 
 #define KEY_MM_PATCH_MERGE_TYPE   "clip.vision.mm_patch_merge_type"
 #define KEY_IMAGE_GRID_PINPOINTS  "clip.vision.image_grid_pinpoints"
 #define KEY_IMAGE_CROP_RESOLUTION "clip.vision.image_crop_resolution"
-#define KEY_WIN_ATTN_PATTERN      "clip.vision.n_wa_pattern"
-#define KEY_ATTN_WINDOW_SIZE      "clip.vision.window_size"
 
 
 //
@@ -53,16 +59,10 @@
 #define TN_ATTN_Q          "%s.blk.%d.attn_q.%s"
 #define TN_ATTN_V          "%s.blk.%d.attn_v.%s"
 #define TN_ATTN_OUTPUT     "%s.blk.%d.attn_out.%s"
-#define TN_ATTN_K_NORM     "%s.blk.%d.attn_k_norm.%s"
-#define TN_ATTN_Q_NORM     "%s.blk.%d.attn_q_norm.%s"
 #define TN_FFN_DOWN        "%s.blk.%d.ffn_down.%s"
-#define TN_FFN_GATE        "%s.blk.%d.ffn_gate.%s"
 #define TN_FFN_UP          "%s.blk.%d.ffn_up.%s"
-#define TN_FFN_GATE        "%s.blk.%d.ffn_gate.%s"
-#define TN_LN_1            "%s.blk.%d.ln1.%s" // layer norm
-#define TN_LN_2            "%s.blk.%d.ln2.%s" // layer norm
-#define TN_LS_1            "%s.blk.%d.ls1.%s" // layer scale
-#define TN_LS_2            "%s.blk.%d.ls2.%s" // layer scale
+#define TN_LN_1            "%s.blk.%d.ln1.%s"
+#define TN_LN_2            "%s.blk.%d.ln2.%s"
 #define TN_LN_PRE          "%s.pre_ln.%s"
 #define TN_LN_POST         "%s.post_ln.%s"
 #define TN_LLAVA_PROJ      "mm.%d.%s"
@@ -70,14 +70,8 @@
 #define TN_MVLM_PROJ_BLOCK "mm.model.mb_block.%d.block.%d.%s"
 #define TN_MVLM_PROJ_PEG   "mm.model.peg.%d.%s"
 #define TN_IMAGE_NEWLINE   "model.image_newline"
-#define TN_MM_INP_NORM     "mm.input_norm.weight"
 #define TN_MM_INP_PROJ     "mm.input_projection.weight" // gemma3
 #define TN_MM_SOFT_EMB_N   "mm.soft_emb_norm.weight"    // gemma3
-#define TN_MM_PROJECTOR    "mm.model.fc.weight"         // idefics3
-#define TN_MM_PATCH_MERGER "mm.patch_merger.weight"     // mistral small 3.1
-#define TN_TOK_IMG_BREAK   "v.token_embd.img_break"     // pixtral
-#define TN_TOK_GLM_BOI     "adapter.boi"                // glm-edge (these embeddings are not in text model)
-#define TN_TOK_GLM_EOI     "adapter.eoi"                // glm-edge (these embeddings are not in text model)
 
 // mimicpmv
 #define TN_MINICPMV_POS_EMBD_K "resampler.pos_embed_k"
@@ -93,23 +87,18 @@
 #define TN_GLM_ADAPTER_D_H_2_4H "adapter.linear.dense_h_to_4h.%s"
 #define TN_GLM_ADAPTER_GATE     "adapter.linear.gate.%s"
 #define TN_GLM_ADAPTER_D_4H_2_H "adapter.linear.dense_4h_to_h.%s"
-
-// align x to upper multiple of n
-#define CLIP_ALIGN(x, n) ((((x) + (n) - 1) / (n)) * (n))
+#define TN_GLM_BOI_W            "adapter.boi"
+#define TN_GLM_EOI_W            "adapter.eoi"
 
 enum projector_type {
     PROJECTOR_TYPE_MLP,
     PROJECTOR_TYPE_MLP_NORM,
     PROJECTOR_TYPE_LDP,
     PROJECTOR_TYPE_LDPV2,
-    PROJECTOR_TYPE_MINICPMV,
+    PROJECTOR_TYPE_RESAMPLER,
     PROJECTOR_TYPE_GLM_EDGE,
-    PROJECTOR_TYPE_QWEN2VL,
+    PROJECTOR_TYPE_MERGER,
     PROJECTOR_TYPE_GEMMA3,
-    PROJECTOR_TYPE_IDEFICS3,
-    PROJECTOR_TYPE_PIXTRAL,
-    PROJECTOR_TYPE_QWEN25VL,
-    PROJECTOR_TYPE_INTERNVL,
     PROJECTOR_TYPE_UNKNOWN,
 };
 
@@ -117,14 +106,10 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
     { PROJECTOR_TYPE_MLP,       "mlp" },
     { PROJECTOR_TYPE_LDP,       "ldp" },
     { PROJECTOR_TYPE_LDPV2,     "ldpv2"},
-    { PROJECTOR_TYPE_MINICPMV,  "resampler"},
+    { PROJECTOR_TYPE_RESAMPLER, "resampler"},
     { PROJECTOR_TYPE_GLM_EDGE,  "adapter"},
-    { PROJECTOR_TYPE_QWEN2VL,   "qwen2vl_merger"},
-    { PROJECTOR_TYPE_QWEN25VL,  "qwen2.5vl_merger"},
+    { PROJECTOR_TYPE_MERGER,    "qwen2vl_merger"},
     { PROJECTOR_TYPE_GEMMA3,    "gemma3"},
-    { PROJECTOR_TYPE_IDEFICS3,  "idefics3"},
-    { PROJECTOR_TYPE_PIXTRAL,   "pixtral"},
-    { PROJECTOR_TYPE_INTERNVL,  "internvl"},
 };
 
 static projector_type clip_projector_type_from_string(const std::string & str) {
@@ -239,15 +224,6 @@ struct clip_image_u8_batch {
 
 struct clip_image_f32_batch {
     std::vector<clip_image_f32_ptr> entries;
-
-    clip_image_f32_batch clone() const {
-        clip_image_f32_batch new_batch;
-        new_batch.entries.reserve(entries.size());
-        for (const auto & entry : entries) {
-            new_batch.entries.emplace_back(new clip_image_f32(*entry));
-        }
-        return new_batch;
-    }
 };
 
 //

llama/llama.cpp/examples/llava/clip.h

@@ -47,7 +47,7 @@ CLIP_API struct clip_ctx * clip_init(const char * fname, struct clip_context_par
 CLIP_API void clip_free(struct clip_ctx * ctx);
 
 CLIP_API size_t clip_embd_nbytes(const struct clip_ctx * ctx);
-CLIP_API size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_w, int img_h);
+CLIP_API size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_h, int img_w);
 
 CLIP_API int32_t clip_get_image_size (const struct clip_ctx * ctx);
 CLIP_API int32_t clip_get_patch_size (const struct clip_ctx * ctx);
@@ -59,29 +59,18 @@ CLIP_API const char * clip_patch_merge_type(const struct clip_ctx * ctx);
 CLIP_API const int32_t * clip_image_grid(const struct clip_ctx * ctx);
 CLIP_API size_t get_clip_image_grid_size(const struct clip_ctx * ctx);
 
-GGML_DEPRECATED(CLIP_API int clip_n_patches(const struct clip_ctx * ctx),
-    "use clip_n_output_tokens instead");
-GGML_DEPRECATED(CLIP_API int clip_n_patches_by_img(const struct clip_ctx * ctx, struct clip_image_f32 * img),
-    "use clip_n_output_tokens instead");
-
-CLIP_API int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * img);
-
-// for M-RoPE, this will be the number of token positions in X and Y directions
-// for other models, X will be the total number of tokens and Y will be 1
-CLIP_API int clip_n_output_tokens_x(const struct clip_ctx * ctx, struct clip_image_f32 * img);
-CLIP_API int clip_n_output_tokens_y(const struct clip_ctx * ctx, struct clip_image_f32 * img);
-
-// this should be equal to the embedding dimension of the text model
-CLIP_API int clip_n_mmproj_embd(const struct clip_ctx * ctx);
+CLIP_API int clip_n_patches        (const struct clip_ctx * ctx);
+CLIP_API int clip_n_patches_by_img (const struct clip_ctx * ctx, struct clip_image_f32 * img);
+CLIP_API int clip_n_mmproj_embd    (const struct clip_ctx * ctx);
 
 CLIP_API int clip_uhd_num_image_embeds_col(struct clip_ctx * ctx_clip);
 CLIP_API void clip_add_load_image_size(struct clip_ctx * ctx_clip, struct clip_image_size * load_image_size);
 CLIP_API struct clip_image_size * clip_get_load_image_size(struct clip_ctx * ctx_clip);
 
-CLIP_API struct clip_image_size      * clip_image_size_init(void);
-CLIP_API struct clip_image_u8        * clip_image_u8_init (void);
-CLIP_API struct clip_image_f32       * clip_image_f32_init(void);
-CLIP_API struct clip_image_f32_batch * clip_image_f32_batch_init(void); // only used by libllava
+CLIP_API struct clip_image_size      * clip_image_size_init();
+CLIP_API struct clip_image_u8        * clip_image_u8_init ();
+CLIP_API struct clip_image_f32       * clip_image_f32_init();
+CLIP_API struct clip_image_f32_batch * clip_image_f32_batch_init(); // only used by libllava
 
 // nx, ny are the output image dimensions
 CLIP_API unsigned char * clip_image_u8_get_data(struct clip_image_u8 * img, uint32_t * nx, uint32_t * ny);
@@ -125,6 +114,8 @@ CLIP_API bool clip_is_qwen2vl(const struct clip_ctx * ctx);
 CLIP_API bool clip_is_llava(const struct clip_ctx * ctx);
 CLIP_API bool clip_is_gemma3(const struct clip_ctx * ctx);
 
+CLIP_API int get_deepest_feature_layer(const struct clip_ctx * ctx);
+
 CLIP_API bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img, int h, int w, float * vec);
 
 

llama/llama.cpp/examples/llava/llava.cpp

@@ -2,7 +2,6 @@
 #include "llava.h"
 
 #include "llama.h"
-#include "ggml-cpp.h"
 
 #include <algorithm>
 #include <cerrno>
@@ -113,7 +112,7 @@ static struct clip_image_grid_shape get_anyres_image_grid_shape(const std::pair<
 }
 
 // Take the image segments in a grid configuration and return the embeddings and the number of embeddings into preallocated memory (image_embd_out)
-static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector<float *> & image_embd_v, struct clip_image_grid_shape grid_shape, float * image_embd_out, int * n_img_pos_out, clip_image_f32 * img_input) {
+static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector<float *> & image_embd_v, struct clip_image_grid_shape grid_shape, float * image_embd_out, int * n_img_pos_out) {
     struct {
         struct ggml_context * ctx;
     } model;
@@ -176,7 +175,7 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector<float *>
 
     model.ctx = ggml_init(params);
 
-    struct ggml_tensor * image_features = ggml_new_tensor_3d(model.ctx, GGML_TYPE_F32, clip_n_mmproj_embd(ctx_clip), clip_n_output_tokens(ctx_clip, img_input), num_images - 1); // example: 4096 x 576 x 4
+    struct ggml_tensor * image_features = ggml_new_tensor_3d(model.ctx, GGML_TYPE_F32, clip_n_mmproj_embd(ctx_clip), clip_n_patches(ctx_clip), num_images - 1); // example: 4096 x 576 x 4
     // ggml_tensor_printf(image_features,"image_features",__LINE__,false,false);
     // fill it with the image embeddings, ignoring the base
     for (size_t i = 1; i < num_images; i++) {
@@ -210,17 +209,13 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector<float *>
     struct ggml_tensor *flatten = ggml_view_2d(model.ctx, permuted_cont, clip_n_mmproj_embd(ctx_clip), num_patches_height * num_patches_width * num_patches_per_side * num_patches_per_side,  size_ele * clip_n_mmproj_embd(ctx_clip), 0);
     // ggml_tensor_printf(flatten,"flatten",__LINE__,false,false);
     ggml_build_forward_expand(gf, flatten);
-
-    ggml_backend_ptr backend { ggml_backend_init_by_type(GGML_BACKEND_DEVICE_TYPE_CPU, nullptr) };
-    GGML_ASSERT(backend != nullptr && "failed to initialize CPU backend");
-    ggml_backend_graph_compute(backend.get(), gf);
-
+    ggml_graph_compute_with_ctx(model.ctx, gf, 1);
     struct ggml_tensor* result = ggml_graph_node(gf, -1);
 
     memcpy(image_embd_out, image_embd_v[0], clip_embd_nbytes(ctx_clip)); // main image as global context
     // append without newline tokens (default behavior in llava_arch when not using unpad ):
-    memcpy(image_embd_out + clip_n_output_tokens(ctx_clip, img_input) * clip_n_mmproj_embd(ctx_clip), (float*)result->data, clip_embd_nbytes(ctx_clip) * (num_images-1)); // grid patches
-    *n_img_pos_out = static_cast<int>(result->ne[1]+clip_n_output_tokens(ctx_clip, img_input));
+    memcpy(image_embd_out + clip_n_patches(ctx_clip) * clip_n_mmproj_embd(ctx_clip), (float*)result->data, clip_embd_nbytes(ctx_clip) * (num_images-1)); // grid patches
+    *n_img_pos_out = static_cast<int>(result->ne[1]+clip_n_patches(ctx_clip));
 
     // Debug: Test single segments
     // Current findings: sending base image, sending a segment embedding all works similar to python
@@ -318,7 +313,7 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
                 image_embd + n_img_pos_out * clip_n_mmproj_embd(ctx_clip),
                 image_embd_v[i],
                 clip_embd_nbytes_by_img(ctx_clip, nx, ny));
-            n_img_pos_out += clip_n_output_tokens(ctx_clip, img_res);
+            n_img_pos_out += clip_n_patches_by_img(ctx_clip, img_res);
         }
         *n_img_pos = n_img_pos_out;
         for (size_t i = 0; i < image_embd_v.size(); i++) {
@@ -347,8 +342,8 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
     }
     else if (strcmp(mm_patch_merge_type, "spatial_unpad") != 0) {
         // flat / default llava-1.5 type embedding
+        *n_img_pos = clip_n_patches(ctx_clip);
         clip_image_f32 * img_res = clip_image_f32_get_img(img_res_v.get(), 0);
-        *n_img_pos = clip_n_output_tokens(ctx_clip, img_res);
         bool encoded = clip_image_encode(ctx_clip, n_threads, img_res, image_embd); // image_embd shape is 576 x 4096
         if (!encoded) {
             LOG_ERR("Unable to encode image\n");
@@ -386,8 +381,7 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
         struct clip_image_grid_shape grid_shape = get_anyres_image_grid_shape({img->nx,img->ny}, grid_pinpoints, image_size);
 
         int n_img_pos_out;
-        clip_image_f32 * img_input = clip_image_f32_get_img(img_res_v.get(), 0);
-        clip_llava_handle_patches(ctx_clip, image_embd_v, grid_shape, image_embd, &n_img_pos_out, img_input);
+        clip_llava_handle_patches(ctx_clip, image_embd_v, grid_shape, image_embd, &n_img_pos_out);
         *n_img_pos = n_img_pos_out;
 
         for (size_t i = 0; i < image_embd_v.size(); i++) {
@@ -462,7 +456,7 @@ struct llava_embd_batch {
     std::vector<llama_seq_id *> seq_ids;
     std::vector<int8_t>         logits;
     llama_batch batch;
-    llava_embd_batch(float * embd, int32_t n_tokens, llama_pos pos_0, llama_seq_id seq_id) {
+    llava_embd_batch(float * embd, int32_t n_embd, int32_t n_tokens, llama_pos pos_0, llama_seq_id seq_id) {
         pos     .resize(n_tokens);
         n_seq_id.resize(n_tokens);
         seq_ids .resize(n_tokens + 1);
@@ -474,6 +468,7 @@ struct llava_embd_batch {
             /*n_tokens       =*/ n_tokens,
             /*tokens         =*/ nullptr,
             /*embd           =*/ embd,
+            /*n_embd         =*/ n_embd,
             /*pos            =*/ pos.data(),
             /*n_seq_id       =*/ n_seq_id.data(),
             /*seq_id         =*/ seq_ids.data(),
@@ -497,7 +492,7 @@ bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_
             n_eval = n_batch;
         }
         float * embd = image_embed->embed+i*n_embd;
-        llava_embd_batch llava_batch = llava_embd_batch(embd, n_eval, *n_past, 0);
+        llava_embd_batch llava_batch = llava_embd_batch(embd, n_embd, n_eval, *n_past, 0);
         if (llama_decode(ctx_llama, llava_batch.batch)) {
             LOG_ERR("%s : failed to eval\n", __func__);
             return false;

llama/llama.cpp/examples/llava/llava.go

@@ -1,4 +1,4 @@
-package mtmd
+package llava
 
 // #cgo CXXFLAGS: -std=c++11
 // #cgo CPPFLAGS: -I${SRCDIR}/../../include -I${SRCDIR}/../../common

llama/llama.cpp/include/llama.h

@@ -4,7 +4,6 @@
 #include "ggml.h"
 #include "ggml-cpu.h"
 #include "ggml-backend.h"
-#include "ggml-opt.h"
 
 #include <stddef.h>
 #include <stdint.h>
@@ -112,8 +111,6 @@ extern "C" {
         LLAMA_VOCAB_PRE_TYPE_TRILLION       = 31,
         LLAMA_VOCAB_PRE_TYPE_BAILINGMOE     = 32,
         LLAMA_VOCAB_PRE_TYPE_LLAMA4         = 33,
-        LLAMA_VOCAB_PRE_TYPE_PIXTRAL        = 34,
-        LLAMA_VOCAB_PRE_TYPE_SEED_CODER     = 35,
     };
 
     enum llama_rope_type {
@@ -258,6 +255,7 @@ extern "C" {
 
         llama_token  *  token;
         float        *  embd;
+        int32_t         n_embd;
         llama_pos    *  pos;
         int32_t      *  n_seq_id;
         llama_seq_id ** seq_id;
@@ -353,18 +351,20 @@ extern "C" {
         enum ggml_type type_k; // data type for K cache [EXPERIMENTAL]
         enum ggml_type type_v; // data type for V cache [EXPERIMENTAL]
 
+        // Keep the booleans together and at the end of the struct to avoid misalignment during copy-by-value.
+        // TODO: move at the end of the struct
+        bool logits_all;  // the llama_decode() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead)
+        bool embeddings;  // if true, extract embeddings (together with logits)
+        bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU
+        bool flash_attn;  // whether to use flash attention [EXPERIMENTAL]
+        bool no_perf;     // whether to measure performance timings
+        bool cross_attn;  // whether to use cross attention
+
         // Abort callback
         // if it returns true, execution of llama_decode() will be aborted
         // currently works only with CPU execution
         ggml_abort_callback abort_callback;
         void *              abort_callback_data;
-
-        // Keep the booleans together and at the end of the struct to avoid misalignment during copy-by-value.
-        bool embeddings;  // if true, extract embeddings (together with logits)
-        bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU
-        bool flash_attn;  // whether to use flash attention [EXPERIMENTAL]
-        bool no_perf;     // whether to measure performance timings
-        bool op_offload;  // whether to offload host tensor operations to device
     };
 
     // model quantization parameters
@@ -446,10 +446,6 @@ extern "C" {
                                  size_t    n_paths,
               struct llama_model_params    params);
 
-    LLAMA_API void llama_model_save_to_file(
-            const struct llama_model * model,
-                        const char * path_model);
-
     DEPRECATED(LLAMA_API void llama_free_model(struct llama_model * model),
             "use llama_model_free instead");
 
@@ -464,6 +460,10 @@ extern "C" {
             struct llama_context_params   params),
             "use llama_init_from_model instead");
 
+    // TODO (jmorganca): this should most likely be passed in as part of a batch
+    // and not set on the context for all batches.
+    LLAMA_API void llama_set_cross_attention(struct llama_context * ctx, bool cross_attn_state);
+
     // Frees all allocated memory
     LLAMA_API void llama_free(struct llama_context * ctx);
 
@@ -929,19 +929,14 @@ extern "C" {
     // Frees a batch of tokens allocated with llama_batch_init()
     LLAMA_API void llama_batch_free(struct llama_batch batch);
 
-    // Process a batch of tokens.
-    // In contrast to llama_decode() - this call does not use KV cache.
-    // For encode-decoder contexts, processes the batch using the encoder.
-    // Can store the encoder output internally for later use by the decoder's cross-attention layers.
+    // Processes a batch of tokens with the ecoder part of the encoder-decoder model.
+    // Stores the encoder output internally for later use by the decoder cross-attention layers.
     //   0 - success
     // < 0 - error. the KV cache state is restored to the state before this call
     LLAMA_API int32_t llama_encode(
             struct llama_context * ctx,
               struct llama_batch   batch);
 
-    // Process a batch of tokens.
-    // Requires KV cache.
-    // For encode-decoder contexts, processes the batch using the decoder.
     // Positive return values does not mean a fatal error, but rather a warning.
     //   0 - success
     //   1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context)
@@ -1242,7 +1237,6 @@ extern "C" {
         "will be removed in the future (see https://github.com/ggml-org/llama.cpp/pull/9896#discussion_r1800920915)");
 
     /// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
-    /// Setting k <= 0 makes this a noop
     LLAMA_API struct llama_sampler * llama_sampler_init_top_k      (int32_t k);
 
     /// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
@@ -1438,37 +1432,6 @@ extern "C" {
     LLAMA_API void                           llama_perf_sampler_print(const struct llama_sampler * chain);
     LLAMA_API void                           llama_perf_sampler_reset(      struct llama_sampler * chain);
 
-    //
-    // training
-    //
-
-    // function that returns whether or not a given tensor contains trainable parameters
-    typedef bool (*llama_opt_param_filter)(const struct ggml_tensor * tensor, void * userdata);
-
-    // always returns true
-    LLAMA_API bool llama_opt_param_filter_all(const struct ggml_tensor * tensor, void * userdata);
-
-    struct llama_opt_params {
-        uint32_t n_ctx_train; // assumed context size post training, use context size specified in llama_context if 0
-
-        llama_opt_param_filter param_filter; // callback for determining which tensors contain trainable parameters
-        void * param_filter_ud;              // userdata for determining which tensors contain trainable parameters
-
-        ggml_opt_get_optimizer_params get_opt_pars; // callback for calculating optimizer parameters
-        void * get_opt_pars_ud;                     // userdata for calculating optimizer parameters
-    };
-
-    LLAMA_API void llama_opt_init(struct llama_context * lctx, struct llama_model * model, struct llama_opt_params lopt_params);
-
-    LLAMA_API void llama_opt_epoch(
-            struct llama_context    * lctx,
-            ggml_opt_dataset_t        dataset,
-            ggml_opt_result_t         result_train,
-            ggml_opt_result_t         result_eval,
-            int64_t                   idata_split,
-            ggml_opt_epoch_callback   callback_train,
-            ggml_opt_epoch_callback   callback_eval);
-
 #ifdef __cplusplus
 }
 #endif

llama/llama.cpp/src/llama-adapter.cpp

@@ -253,9 +253,6 @@ static void llama_adapter_lora_init_impl(llama_model & model, const char * path_
     std::vector<ggml_backend_buffer_type_t> buft_extra;
     {
         auto * cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
-        if (!cpu_dev) {
-            throw std::runtime_error(format("%s: no CPU backend found", __func__));
-        }
         auto * cpu_reg = ggml_backend_dev_backend_reg(cpu_dev);
 
         auto ggml_backend_dev_get_extra_bufts_fn = (ggml_backend_dev_get_extra_bufts_t)
@@ -294,9 +291,6 @@ static void llama_adapter_lora_init_impl(llama_model & model, const char * path_
                 LLAMA_LOG_WARN("%s: lora for '%s' cannot use buft '%s', fallback to CPU\n", __func__, model_tensor->name, ggml_backend_buft_name(buft));
 
                 auto * cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
-                if (!cpu_dev) {
-                    throw std::runtime_error(format("%s: no CPU backend found", __func__));
-                }
                 buft = ggml_backend_dev_buffer_type(cpu_dev);
 
                 break;

llama/llama.cpp/src/llama-arch.cpp

@@ -6,6 +6,7 @@
 
 static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_LLAMA,            "llama"            },
+    { LLM_ARCH_MLLAMA,           "mllama"           },
     { LLM_ARCH_LLAMA4,           "llama4"           },
     { LLM_ARCH_DECI,             "deci"             },
     { LLM_ARCH_FALCON,           "falcon"           },
@@ -19,7 +20,6 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_REFACT,           "refact"           },
     { LLM_ARCH_BERT,             "bert"             },
     { LLM_ARCH_NOMIC_BERT,       "nomic-bert"       },
-    { LLM_ARCH_NOMIC_BERT_MOE,   "nomic-bert-moe"   },
     { LLM_ARCH_JINA_BERT_V2,     "jina-bert-v2"     },
     { LLM_ARCH_BLOOM,            "bloom"            },
     { LLM_ARCH_STABLELM,         "stablelm"         },
@@ -73,6 +73,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_WAVTOKENIZER_DEC, "wavtokenizer-dec" },
     { LLM_ARCH_PLM,              "plm"              },
     { LLM_ARCH_BAILINGMOE,       "bailingmoe"       },
+    { LLM_ARCH_MISTRAL3,         "mistral3"         },
     { LLM_ARCH_UNKNOWN,          "(unknown)"        },
 };
 
@@ -108,7 +109,6 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_EXPERT_WEIGHTS_SCALE,              "%s.expert_weights_scale"              },
     { LLM_KV_EXPERT_WEIGHTS_NORM,               "%s.expert_weights_norm"               },
     { LLM_KV_EXPERT_GATING_FUNC,                "%s.expert_gating_func"                },
-    { LLM_KV_MOE_EVERY_N_LAYERS,                "%s.moe_every_n_layers"                },
     { LLM_KV_POOLING_TYPE,                      "%s.pooling_type"                      },
     { LLM_KV_LOGIT_SCALE,                       "%s.logit_scale"                       },
     { LLM_KV_DECODER_START_TOKEN_ID,            "%s.decoder_start_token_id"            },
@@ -144,6 +144,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_ATTENTION_SLIDING_WINDOW,               "%s.attention.sliding_window"               },
     { LLM_KV_ATTENTION_SCALE,                        "%s.attention.scale"                        },
     { LLM_KV_ATTENTION_BLOCK_SKIP_CONNECTION,        "%s.attention.block_skip_connection"        },
+    { LLM_KV_ATTENTION_CROSS_ATTENTION_LAYERS,       "%s.attention.cross_attention_layers"       },
     { LLM_KV_ATTENTION_KEY_LENGTH_MLA,               "%s.attention.key_length_mla"               },
     { LLM_KV_ATTENTION_VALUE_LENGTH_MLA,             "%s.attention.value_length_mla"             },
 
@@ -273,6 +274,40 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_FFN_UP_SHEXP,    "blk.%d.ffn_up_shexp" },
         },
     },
+    {
+        LLM_ARCH_MLLAMA,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
+            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_GATE_EXP,    "blk.%d.ffn_gate.%d" },
+            { LLM_TENSOR_FFN_DOWN_EXP,    "blk.%d.ffn_down.%d" },
+            { LLM_TENSOR_FFN_UP_EXP,      "blk.%d.ffn_up.%d" },
+            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
+            { LLM_TENSOR_CROSS_ATTN_K_NORM,    "blk.%d.cross_attn_k_norm" },
+            { LLM_TENSOR_CROSS_ATTN_K_PROJ,    "blk.%d.cross_attn_k_proj" },
+            { LLM_TENSOR_CROSS_ATTN_O_PROJ,    "blk.%d.cross_attn_o_proj" },
+            { LLM_TENSOR_CROSS_ATTN_Q_NORM,    "blk.%d.cross_attn_q_norm" },
+            { LLM_TENSOR_CROSS_ATTN_Q_PROJ,    "blk.%d.cross_attn_q_proj" },
+            { LLM_TENSOR_CROSS_ATTN_V_PROJ,    "blk.%d.cross_attn_v_proj" },
+            { LLM_TENSOR_CROSS_ATTN_ATTN_GATE, "blk.%d.cross_attn_attn_gate" },
+            { LLM_TENSOR_CROSS_ATTN_MLP_GATE,  "blk.%d.cross_attn_mlp_gate" },
+        },
+    },
     {
         LLM_ARCH_DECI,
         {
@@ -476,24 +511,6 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
         },
     },
-    {
-        LLM_ARCH_NOMIC_BERT_MOE,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
-            { LLM_TENSOR_TOKEN_TYPES,     "token_types" },
-            { LLM_TENSOR_ATTN_OUT_NORM,   "blk.%d.attn_output_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_LAYER_OUT_NORM,  "blk.%d.layer_output_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
-        },
-    },
     {
         LLM_ARCH_JINA_BERT_V2,
         {
@@ -1570,6 +1587,22 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_FFN_UP_SHEXP,       "blk.%d.ffn_up_shexp" },
         },
     },
+    {
+        LLM_ARCH_MISTRAL3,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,  "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+            { LLM_TENSOR_ATTN_NORM,   "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,      "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,      "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,      "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,    "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,    "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,    "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_UP,      "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_DOWN,    "blk.%d.ffn_down" },
+        }
+    },
     {
         LLM_ARCH_UNKNOWN,
         {
@@ -1701,6 +1734,14 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
     // this tensor is loaded for T5, but never used
     {LLM_TENSOR_DEC_CROSS_ATTN_REL_B,       {LLM_TENSOR_LAYER_REPEATING, GGML_OP_NONE}},
     {LLM_TENSOR_BSKCN_TV,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_CROSS_ATTN_K_NORM,          {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_CROSS_ATTN_K_PROJ,          {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_CROSS_ATTN_O_PROJ,          {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_CROSS_ATTN_Q_NORM,          {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_CROSS_ATTN_Q_PROJ,          {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_CROSS_ATTN_V_PROJ,          {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_CROSS_ATTN_ATTN_GATE,       {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_CROSS_ATTN_MLP_GATE,        {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
     {LLM_TENSOR_CONV1D,                     {LLM_TENSOR_LAYER_INPUT,     GGML_OP_IM2COL}},
     {LLM_TENSOR_POS_NET_NORM,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
     {LLM_TENSOR_POS_NET_NORM1,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},

llama/llama.cpp/src/llama-arch.h

@@ -11,6 +11,7 @@
 enum llm_arch {
     LLM_ARCH_LLAMA,
     LLM_ARCH_LLAMA4,
+    LLM_ARCH_MLLAMA,
     LLM_ARCH_DECI,
     LLM_ARCH_FALCON,
     LLM_ARCH_BAICHUAN,
@@ -23,7 +24,6 @@ enum llm_arch {
     LLM_ARCH_REFACT,
     LLM_ARCH_BERT,
     LLM_ARCH_NOMIC_BERT,
-    LLM_ARCH_NOMIC_BERT_MOE,
     LLM_ARCH_JINA_BERT_V2,
     LLM_ARCH_BLOOM,
     LLM_ARCH_STABLELM,
@@ -75,6 +75,7 @@ enum llm_arch {
     LLM_ARCH_CHAMELEON,
     LLM_ARCH_SOLAR,
     LLM_ARCH_WAVTOKENIZER_DEC,
+    LLM_ARCH_MISTRAL3,
     LLM_ARCH_PLM,
     LLM_ARCH_BAILINGMOE,
     LLM_ARCH_UNKNOWN,
@@ -112,7 +113,6 @@ enum llm_kv {
     LLM_KV_EXPERT_WEIGHTS_SCALE,
     LLM_KV_EXPERT_WEIGHTS_NORM,
     LLM_KV_EXPERT_GATING_FUNC,
-    LLM_KV_MOE_EVERY_N_LAYERS,
     LLM_KV_POOLING_TYPE,
     LLM_KV_LOGIT_SCALE,
     LLM_KV_DECODER_START_TOKEN_ID,
@@ -148,6 +148,7 @@ enum llm_kv {
     LLM_KV_ATTENTION_SLIDING_WINDOW,
     LLM_KV_ATTENTION_SCALE,
     LLM_KV_ATTENTION_BLOCK_SKIP_CONNECTION,
+    LLM_KV_ATTENTION_CROSS_ATTENTION_LAYERS,
     LLM_KV_ATTENTION_KEY_LENGTH_MLA,
     LLM_KV_ATTENTION_VALUE_LENGTH_MLA,
 
@@ -349,6 +350,14 @@ enum llm_tensor {
     LLM_TENSOR_CLS,
     LLM_TENSOR_CLS_OUT,
     LLM_TENSOR_BSKCN_TV,
+    LLM_TENSOR_CROSS_ATTN_K_NORM,
+    LLM_TENSOR_CROSS_ATTN_K_PROJ,
+    LLM_TENSOR_CROSS_ATTN_O_PROJ,
+    LLM_TENSOR_CROSS_ATTN_Q_NORM,
+    LLM_TENSOR_CROSS_ATTN_Q_PROJ,
+    LLM_TENSOR_CROSS_ATTN_V_PROJ,
+    LLM_TENSOR_CROSS_ATTN_ATTN_GATE,
+    LLM_TENSOR_CROSS_ATTN_MLP_GATE,
     LLM_TENSOR_CONV1D,
     LLM_TENSOR_CONVNEXT_DW,
     LLM_TENSOR_CONVNEXT_NORM,

llama/llama.cpp/src/llama-batch.cpp

@@ -189,7 +189,7 @@ llama_ubatch llama_sbatch::split_seq(size_t n_ubatch) {
     return ubatch;
 }
 
-llama_sbatch::llama_sbatch(const llama_batch & batch, size_t n_embd, bool simple_split, bool logits_all) {
+void llama_sbatch::from_batch(const llama_batch & batch, size_t n_embd, bool simple_split, bool logits_all) {
     GGML_ASSERT(batch.n_tokens >= 0);
     this->batch = &batch;
     this->n_embd = n_embd;
@@ -203,7 +203,6 @@ llama_sbatch::llama_sbatch(const llama_batch & batch, size_t n_embd, bool simple
     for (size_t i = 0; i < n_tokens; ++i) {
         ids[i] = i;
     }
-
     if (simple_split) {
         seq.resize(1);
         llama_sbatch_seq & s = seq[0];
@@ -213,7 +212,6 @@ llama_sbatch::llama_sbatch(const llama_batch & batch, size_t n_embd, bool simple
         s.length = n_tokens;
         return;
     }
-
     std::sort(ids.begin(), ids.end(),
             [&batch](size_t a, size_t b) {
                 int32_t n_seq_a = batch.n_seq_id ? batch.n_seq_id[a] : 1;
@@ -241,7 +239,6 @@ llama_sbatch::llama_sbatch(const llama_batch & batch, size_t n_embd, bool simple
                 return n_seq_a > n_seq_b;
             }
     );
-
     // init seq
     llama_sbatch_seq * last_seq = nullptr;
 
@@ -265,7 +262,6 @@ llama_sbatch::llama_sbatch(const llama_batch & batch, size_t n_embd, bool simple
         seq.push_back(new_seq);
         last_seq = &seq.back();
     }
-
     // keep shared prompts first at the end, then sort by length descending.
     std::sort(seq.begin(), seq.end(),
             [](llama_sbatch_seq & a, llama_sbatch_seq & b) {
@@ -320,6 +316,7 @@ struct llama_batch llama_batch_get_one(
         /*n_tokens       =*/ n_tokens,
         /*tokens         =*/ tokens,
         /*embd           =*/ nullptr,
+        /*n_embd         =*/ 0,
         /*pos            =*/ nullptr,
         /*n_seq_id       =*/ nullptr,
         /*seq_id         =*/ nullptr,
@@ -332,6 +329,7 @@ struct llama_batch llama_batch_init(int32_t n_tokens_alloc, int32_t embd, int32_
         /*n_tokens       =*/ 0,
         /*tokens         =*/ nullptr,
         /*embd           =*/ nullptr,
+        /*n_embd         =*/ 0,
         /*pos            =*/ nullptr,
         /*n_seq_id       =*/ nullptr,
         /*seq_id         =*/ nullptr,
@@ -340,6 +338,7 @@ struct llama_batch llama_batch_init(int32_t n_tokens_alloc, int32_t embd, int32_
 
     if (embd) {
         batch.embd = (float *) malloc(sizeof(float) * n_tokens_alloc * embd);
+        batch.n_embd = embd;
     } else {
         batch.token = (llama_token *) malloc(sizeof(llama_token) * n_tokens_alloc);
     }

llama/llama.cpp/src/llama-batch.h

@@ -70,8 +70,7 @@ struct llama_sbatch {
     // sequence-wise split
     llama_ubatch split_seq(size_t n_ubatch);
 
-    llama_sbatch() = default;
-    llama_sbatch(const llama_batch & batch, size_t n_embd, bool simple_split = false, bool logits_all = false);
+    void from_batch(const llama_batch & batch, size_t n_embd, bool simple_split = false, bool logits_all = false);
 };
 
 // temporary allocate memory for the input batch if needed

llama/llama.cpp/src/llama-chat.cpp

@@ -35,7 +35,6 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
     { "mistral-v3",        LLM_CHAT_TEMPLATE_MISTRAL_V3        },
     { "mistral-v3-tekken", LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN },
     { "mistral-v7",        LLM_CHAT_TEMPLATE_MISTRAL_V7        },
-    { "mistral-v7-tekken", LLM_CHAT_TEMPLATE_MISTRAL_V7_TEKKEN },
     { "phi3",              LLM_CHAT_TEMPLATE_PHI_3             },
     { "phi4",              LLM_CHAT_TEMPLATE_PHI_4             },
     { "falcon3",           LLM_CHAT_TEMPLATE_FALCON_3          },
@@ -51,8 +50,8 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
     { "deepseek3",         LLM_CHAT_TEMPLATE_DEEPSEEK_3        },
     { "command-r",         LLM_CHAT_TEMPLATE_COMMAND_R         },
     { "llama3",            LLM_CHAT_TEMPLATE_LLAMA_3           },
-    { "chatglm3",          LLM_CHAT_TEMPLATE_CHATGLM_3         },
-    { "chatglm4",          LLM_CHAT_TEMPLATE_CHATGLM_4         },
+    { "chatglm3",          LLM_CHAT_TEMPLATE_CHATGML_3         },
+    { "chatglm4",          LLM_CHAT_TEMPLATE_CHATGML_4         },
     { "glmedge",           LLM_CHAT_TEMPLATE_GLMEDGE           },
     { "minicpm",           LLM_CHAT_TEMPLATE_MINICPM           },
     { "exaone3",           LLM_CHAT_TEMPLATE_EXAONE_3          },
@@ -63,7 +62,6 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
     { "yandex",            LLM_CHAT_TEMPLATE_YANDEX            },
     { "bailing",           LLM_CHAT_TEMPLATE_BAILING           },
     { "llama4",            LLM_CHAT_TEMPLATE_LLAMA4            },
-    { "smolvlm",           LLM_CHAT_TEMPLATE_SMOLVLM           },
 };
 
 llm_chat_template llm_chat_template_from_str(const std::string & name) {
@@ -83,9 +81,7 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) {
     if (tmpl_contains("<|im_start|>")) {
         return tmpl_contains("<|im_sep|>")
             ? LLM_CHAT_TEMPLATE_PHI_4
-            : tmpl_contains("<end_of_utterance>")
-                ? LLM_CHAT_TEMPLATE_SMOLVLM // SmolVLM uses <|im_start|> as BOS, but it is NOT chatml
-                : LLM_CHAT_TEMPLATE_CHATML;
+            : LLM_CHAT_TEMPLATE_CHATML;
     } else if (tmpl.find("mistral") == 0 || tmpl_contains("[INST]")) {
         if (tmpl_contains("[SYSTEM_PROMPT]")) {
             return LLM_CHAT_TEMPLATE_MISTRAL_V7;
@@ -123,12 +119,8 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) {
         }
     } else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|end|>")) {
         return LLM_CHAT_TEMPLATE_PHI_3;
-    } else if (tmpl_contains("[gMASK]<sop>")) {
-        return LLM_CHAT_TEMPLATE_CHATGLM_4;
     } else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|user|>")) {
         return tmpl_contains("</s>") ? LLM_CHAT_TEMPLATE_FALCON_3 : LLM_CHAT_TEMPLATE_GLMEDGE;
-    } else if (tmpl_contains("<|{{ item['role'] }}|>") && tmpl_contains("<|begin_of_image|>")) {
-        return LLM_CHAT_TEMPLATE_GLMEDGE;
     } else if (tmpl_contains("<|user|>") && tmpl_contains("<|endoftext|>")) {
         return LLM_CHAT_TEMPLATE_ZEPHYR;
     } else if (tmpl_contains("bos_token + message['role']")) {
@@ -157,7 +149,9 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) {
         return LLM_CHAT_TEMPLATE_LLAMA_3;
     } else if (tmpl_contains("[gMASK]sop")) {
         // chatglm3-6b
-        return LLM_CHAT_TEMPLATE_CHATGLM_3;
+        return LLM_CHAT_TEMPLATE_CHATGML_3;
+    } else if (tmpl_contains("[gMASK]<sop>")) {
+        return LLM_CHAT_TEMPLATE_CHATGML_4;
     } else if (tmpl_contains(LU8("<用户>"))) {
         // MiniCPM-3B-OpenHermes-2.5-v2-GGUF
         return LLM_CHAT_TEMPLATE_MINICPM;
@@ -203,20 +197,19 @@ int32_t llm_chat_apply_template(
         if (add_ass) {
             ss << "<|im_start|>assistant\n";
         }
-    } else if (tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V7 || tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V7_TEKKEN) {
+    } else if (tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V7) {
         // Official mistral 'v7' template
         // See: https://huggingface.co/mistralai/Mistral-Large-Instruct-2411#basic-instruct-template-v7
-        //      https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503#basic-instruct-template-v7-tekken
-        const char * trailing_space = tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V7 ? " " : "";
         for (auto message : chat) {
             std::string role(message->role);
             std::string content(message->content);
             if (role == "system") {
-                ss << "[SYSTEM_PROMPT]" << trailing_space << content << "[/SYSTEM_PROMPT]";
+                ss << "[SYSTEM_PROMPT] " << content << "[/SYSTEM_PROMPT]";
             } else if (role == "user") {
-                ss << "[INST]" << trailing_space << content << "[/INST]";
-            } else {
-                ss << trailing_space << content << "</s>";
+                ss << "[INST] " << content << "[/INST]";
+            }
+            else {
+                ss << " " << content << "</s>";
             }
         }
     } else if (tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V1
@@ -439,7 +432,7 @@ int32_t llm_chat_apply_template(
         if (add_ass) {
             ss << "<|start_header_id|>assistant<|end_header_id|>\n\n";
         }
-    } else if (tmpl == LLM_CHAT_TEMPLATE_CHATGLM_3) {
+    } else if (tmpl == LLM_CHAT_TEMPLATE_CHATGML_3) {
         // chatglm3-6b
         ss << "[gMASK]" << "sop";
         for (auto message : chat) {
@@ -449,14 +442,14 @@ int32_t llm_chat_apply_template(
         if (add_ass) {
             ss << "<|assistant|>";
         }
-    } else if (tmpl == LLM_CHAT_TEMPLATE_CHATGLM_4) {
+    } else if (tmpl == LLM_CHAT_TEMPLATE_CHATGML_4) {
         ss << "[gMASK]" << "<sop>";
         for (auto message : chat) {
             std::string role(message->role);
             ss << "<|" << role << "|>" << "\n" << message->content;
         }
         if (add_ass) {
-            ss << "<|assistant|>\n";
+            ss << "<|assistant|>";
         }
     } else if (tmpl == LLM_CHAT_TEMPLATE_GLMEDGE) {
         for (auto message : chat) {
@@ -627,23 +620,7 @@ int32_t llm_chat_apply_template(
         if (add_ass) {
             ss << "<|header_start|>assistant<|header_end|>\n\n";
         }
-    } else if (tmpl == LLM_CHAT_TEMPLATE_SMOLVLM) {
-        // SmolVLM
-        ss << "<|im_start|>"; // uses <|im_start|> as BOS, but the actual content is NOT chatml
-        for (auto message : chat) {
-            std::string role(message->role);
-            if (role == "system") {
-                ss << message->content << "\n\n";
-            } else if (role == "user") {
-                ss << "User: " << message->content << "<end_of_utterance>\n";
-            } else {
-                ss << "Assistant: " << message->content << "<end_of_utterance>\n";
-            }
-        }
-        if (add_ass) {
-            ss << "Assistant:";
-        }
-    } else {
+    }  else {
         // template not supported
         return -1;
     }

llama/llama.cpp/src/llama-chat.h

@@ -14,7 +14,6 @@ enum llm_chat_template {
     LLM_CHAT_TEMPLATE_MISTRAL_V3,
     LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN,
     LLM_CHAT_TEMPLATE_MISTRAL_V7,
-    LLM_CHAT_TEMPLATE_MISTRAL_V7_TEKKEN,
     LLM_CHAT_TEMPLATE_PHI_3,
     LLM_CHAT_TEMPLATE_PHI_4,
     LLM_CHAT_TEMPLATE_FALCON_3,
@@ -30,8 +29,8 @@ enum llm_chat_template {
     LLM_CHAT_TEMPLATE_DEEPSEEK_3,
     LLM_CHAT_TEMPLATE_COMMAND_R,
     LLM_CHAT_TEMPLATE_LLAMA_3,
-    LLM_CHAT_TEMPLATE_CHATGLM_3,
-    LLM_CHAT_TEMPLATE_CHATGLM_4,
+    LLM_CHAT_TEMPLATE_CHATGML_3,
+    LLM_CHAT_TEMPLATE_CHATGML_4,
     LLM_CHAT_TEMPLATE_GLMEDGE,
     LLM_CHAT_TEMPLATE_MINICPM,
     LLM_CHAT_TEMPLATE_EXAONE_3,
@@ -42,7 +41,6 @@ enum llm_chat_template {
     LLM_CHAT_TEMPLATE_YANDEX,
     LLM_CHAT_TEMPLATE_BAILING,
     LLM_CHAT_TEMPLATE_LLAMA4,
-    LLM_CHAT_TEMPLATE_SMOLVLM,
     LLM_CHAT_TEMPLATE_UNKNOWN,
 };
 

llama/llama.cpp/src/llama-context.h

@@ -8,7 +8,6 @@
 #include "llama-kv-cache.h"
 
 #include "ggml-cpp.h"
-#include "ggml-opt.h"
 
 #include <map>
 #include <vector>
@@ -29,12 +28,7 @@ struct llama_context {
 
     void synchronize();
 
-    const llama_model   & get_model()   const;
-    const llama_cparams & get_cparams() const;
-
-    ggml_backend_sched_t get_sched() const;
-
-    ggml_context * get_ctx_compute() const;
+    const llama_model & get_model() const;
 
     uint32_t n_ctx()         const;
     uint32_t n_ctx_per_seq() const;
@@ -72,6 +66,7 @@ struct llama_context {
     void set_embeddings (bool value);
     void set_causal_attn(bool value);
     void set_warmup(bool value);
+    void set_cross_attn(bool value);
 
     void set_adapter_lora(
             llama_adapter_lora * adapter,
@@ -135,32 +130,6 @@ struct llama_context {
     llama_perf_context_data perf_get_data() const;
     void perf_reset();
 
-    //
-    // training
-    //
-
-    void opt_init(struct llama_model * model, struct llama_opt_params lopt_params);
-
-    void opt_epoch(
-            ggml_opt_dataset_t      dataset,
-            ggml_opt_result_t       result_train,
-            ggml_opt_result_t       result_eval,
-            int64_t                 idata_split,
-            ggml_opt_epoch_callback callback_train,
-            ggml_opt_epoch_callback callback_eval);
-
-    void opt_epoch_iter(
-            ggml_opt_dataset_t               dataset,
-            ggml_opt_result_t                result,
-            const std::vector<llama_token> & tokens,
-            const std::vector<llama_token> & labels_sparse,
-            llama_batch                    & batch,
-            ggml_opt_epoch_callback          callback,
-            bool                             train,
-            int64_t                          idata_in_loop,
-            int64_t                          ndata_in_loop,
-            int64_t                          t_loop_start);
-
 private:
     //
     // output
@@ -170,30 +139,51 @@ private:
     // Returns max number of outputs for which space was reserved.
     int32_t output_reserve(int32_t n_outputs);
 
+    // make the outputs have the same order they had in the user-provided batch
+    // TODO: maybe remove this
+    void output_reorder();
+
     //
     // graph
     //
 
-public:
     int32_t graph_max_nodes() const;
 
     // zero-out inputs and create the ctx_compute for the compute graph
     ggml_cgraph * graph_init();
 
-    // returns the result of ggml_backend_sched_graph_compute_async execution
-    ggml_status graph_compute(
-            ggml_cgraph * gf,
-                   bool   batched);
-
-private:
     llm_graph_result_ptr graph_build(
             ggml_context * ctx,
              ggml_cgraph * gf,
       const llama_ubatch & ubatch,
           llm_graph_type   gtype);
 
+    // returns the result of ggml_backend_sched_graph_compute_async execution
+    ggml_status graph_compute(
+            ggml_cgraph * gf,
+                   bool   batched);
+
     llm_graph_cb graph_get_cb() const;
 
+    // used by kv_self_update()
+    ggml_tensor * build_rope_shift(
+        ggml_context * ctx0,
+        ggml_tensor * cur,
+        ggml_tensor * shift,
+        ggml_tensor * factors,
+              float   freq_base,
+              float   freq_scale,
+        ggml_backend_buffer * bbuf) const;
+
+    llm_graph_result_ptr build_kv_self_shift(
+            ggml_context * ctx0,
+            ggml_cgraph * gf) const;
+
+    llm_graph_result_ptr build_kv_self_defrag(
+            ggml_context * ctx0,
+            ggml_cgraph * gf,
+            const std::vector<struct llama_kv_defrag_move> & moves) const;
+
     // TODO: read/write lora adapters and cvec
     size_t state_write_data(llama_io_write_i & io);
     size_t state_read_data (llama_io_read_i  & io);
@@ -210,10 +200,14 @@ private:
     llama_cparams       cparams;
     llama_adapter_cvec  cvec;
     llama_adapter_loras loras;
+    llama_sbatch        sbatch;
 
     llama_cross cross; // TODO: tmp for handling cross-attention - need something better probably
 
-    std::unique_ptr<llama_memory_i> memory;
+    std::unique_ptr<llama_kv_cache_unified> kv_self;
+
+    // TODO: remove
+    bool logits_all = false;
 
     // decode output (2-dimensional array: [n_outputs][n_vocab])
     size_t  logits_size = 0; // capacity (of floats) for logits
@@ -240,9 +234,6 @@ private:
 
     ggml_context_ptr ctx_compute;
 
-    // training
-    ggml_opt_context_t opt_ctx = nullptr;
-
     ggml_threadpool_t threadpool       = nullptr;
     ggml_threadpool_t threadpool_batch = nullptr;
 

llama/llama.cpp/src/llama-cparams.h

@@ -29,8 +29,8 @@ struct llama_cparams {
     bool offload_kqv;
     bool flash_attn;
     bool no_perf;
+    bool cross_attn;
     bool warmup;
-    bool op_offload;
 
     enum llama_pooling_type pooling_type;
 

llama/llama.cpp/src/llama-graph.cpp

@@ -55,21 +55,7 @@ void llm_graph_input_pos::set_input(const llama_ubatch * ubatch) {
     if (ubatch->pos && pos) {
         const int64_t n_tokens = ubatch->n_tokens;
 
-        if (ubatch->token && n_pos_per_embd == 4) {
-            // in case we're using M-RoPE with text tokens, convert the 1D positions to 4D
-            // the 3 first dims are the same, and 4th dim is all 0
-            std::vector<llama_pos> pos_data(n_tokens*n_pos_per_embd);
-            // copy the first dimension
-            for (int i = 0; i < n_tokens; ++i) {
-                pos_data[               i] = ubatch->pos[i];
-                pos_data[    n_tokens + i] = ubatch->pos[i];
-                pos_data[2 * n_tokens + i] = ubatch->pos[i];
-                pos_data[3 * n_tokens + i] = 0; // 4th dim is 0
-            }
-            ggml_backend_tensor_set(pos, pos_data.data(), 0, pos_data.size()*ggml_element_size(pos));
-        } else {
-            ggml_backend_tensor_set(pos, ubatch->pos, 0, n_tokens*n_pos_per_embd*ggml_element_size(pos));
-        }
+        ggml_backend_tensor_set(pos, ubatch->pos, 0, n_tokens*n_pos_per_token*ggml_element_size(pos));
     }
 }
 
@@ -85,7 +71,7 @@ void llm_graph_input_attn_temp::set_input(const llama_ubatch * ubatch) {
             ) * f_attn_temp_scale + 1.0;
         }
 
-        ggml_backend_tensor_set(attn_scale, attn_scale_data.data(), 0, n_tokens*ggml_element_size(attn_scale));
+        ggml_backend_tensor_set(attn_scale, attn_scale_data.data(), 0, n_tokens*n_pos_per_token*ggml_element_size(attn_scale));
     }
 }
 
@@ -284,7 +270,24 @@ void llm_graph_input_s_copy::set_input(const llama_ubatch * ubatch) {
 
         // assuming copy destinations ALWAYS happen ONLY on the cells between head and head+n
         for (uint32_t i = 0; i < n_kv; ++i) {
-            data[i] = kv_self->s_copy(i);
+            const uint32_t  cell_id = i + kv_self->head;
+
+            //////////////////////////////////////////////
+            // TODO: this should not mutate the KV cache !
+            llama_kv_cell & kv_cell = const_cast<class llama_kv_cache_unified *>(kv_self)->cells[i];
+
+            // prevent out-of-bound sources
+            if (kv_cell.src < 0 || (uint32_t) kv_cell.src >= kv_self->size) {
+                kv_cell.src = cell_id;
+            }
+
+            data[i] = kv_cell.src;
+
+            // TODO: do not mutate the KV cache
+            // ensure copy only happens once
+            if (kv_cell.src != (int32_t) cell_id) {
+                kv_cell.src = cell_id;
+            }
         }
     }
 }
@@ -300,7 +303,18 @@ void llm_graph_input_s_mask::set_input(const llama_ubatch * ubatch) {
 
         // clear unused states
         for (int i = 0; i < n_kv; ++i) {
-            data[i] = kv_self->s_mask(i);
+            const uint32_t  cell_id = i + kv_self->head;
+
+            //////////////////////////////////////////////
+            // TODO: this should not mutate the KV cache !
+            llama_kv_cell & kv_cell = const_cast<class llama_kv_cache_unified *>(kv_self)->cells[i];
+
+            data[i] = (float) (kv_cell.src >= 0);
+
+            // only clear once
+            if (kv_cell.src < 0) {
+                kv_cell.src = cell_id;
+            }
         }
     }
 }
@@ -532,6 +546,12 @@ void llm_graph_input_attn_cross::set_input(const llama_ubatch * ubatch) {
     }
 }
 
+void llm_graph_input_cross_attn_state::set_input(const llama_ubatch * ubatch) {
+    if (ubatch->embd) {
+        ggml_backend_tensor_set(cross_attn_state, ubatch->embd, 0, ggml_nbytes(cross_attn_state));
+    }
+}
+
 //
 // llm_graph_context
 //
@@ -578,7 +598,7 @@ llm_graph_context::llm_graph_context(const llm_graph_params & params) :
     res              (std::make_unique<llm_graph_result>()) {
     }
 
-int64_t llm_graph_context::n_pos_per_embd() const {
+int64_t llm_graph_context::n_pos_per_token() const {
     return arch == LLM_ARCH_QWEN2VL ? 4 : 1;
 }
 
@@ -782,17 +802,13 @@ ggml_tensor * llm_graph_context::build_ffn(
             } break;
     }
 
-    if (gate && type_gate == LLM_FFN_PAR) {
+    if (type_gate == LLM_FFN_PAR) {
         cur = ggml_mul(ctx0, cur, tmp);
         cb(cur, "ffn_gate_par", il);
     }
 
     if (down) {
         cur = build_lora_mm(down, cur);
-        if (arch == LLM_ARCH_GLM4) {
-            // GLM4 seems to have numerical issues with half-precision accumulators
-            ggml_mul_mat_set_prec(cur, GGML_PREC_F32);
-        }
     }
 
     if (down_b) {
@@ -900,35 +916,28 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
     ggml_tensor * up = build_lora_mm_id(up_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
     cb(up, "ffn_moe_up", il);
 
-    ggml_tensor * experts = nullptr;
-    if (gate_exps) {
-        cur = build_lora_mm_id(gate_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
-        cb(cur, "ffn_moe_gate", il);
-    } else {
-        cur = up;
-    }
+    ggml_tensor * gate = build_lora_mm_id(gate_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
+    cb(gate, "ffn_moe_gate", il);
 
     switch (type_op) {
         case LLM_FFN_SILU:
             {
-                cur = ggml_silu(ctx0, cur);
-                cb(cur, "ffn_moe_silu", il);
+                gate = ggml_silu(ctx0, gate);
+                cb(gate, "ffn_moe_silu", il);
             } break;
         case LLM_FFN_GELU:
             {
-                cur = ggml_gelu(ctx0, cur);
-                cb(cur, "ffn_moe_gelu", il);
+                gate = ggml_gelu(ctx0, gate);
+                cb(gate, "ffn_moe_gelu", il);
             } break;
         default:
             GGML_ABORT("fatal error");
     }
 
-    if (gate_exps) {
-        cur = ggml_mul(ctx0, cur, up); // [n_ff, n_expert_used, n_tokens]
-        cb(cur, "ffn_moe_gate_par", il);
-    }
+    ggml_tensor * par = ggml_mul(ctx0, up, gate); // [n_ff, n_expert_used, n_tokens]
+    cb(par, "ffn_moe_gate_par", il);
 
-    experts = build_lora_mm_id(down_exps, cur, selected_experts); // [n_embd, n_expert_used, n_tokens]
+    ggml_tensor * experts = build_lora_mm_id(down_exps, par, selected_experts); // [n_embd, n_expert_used, n_tokens]
     cb(experts, "ffn_moe_down", il);
 
     if (!weight_before_ffn) {
@@ -971,7 +980,6 @@ ggml_tensor * llm_graph_context::build_inp_embd(ggml_tensor * tok_embd) const {
         inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ubatch.n_tokens);
         //cb(inp->tokens, "inp_tokens", -1);
         ggml_set_input(inp->tokens);
-        res->t_tokens = inp->tokens;
 
         cur = ggml_get_rows(ctx0, tok_embd, inp->tokens);
 
@@ -1012,11 +1020,11 @@ ggml_tensor * llm_graph_context::build_inp_embd(ggml_tensor * tok_embd) const {
 }
 
 ggml_tensor * llm_graph_context::build_inp_pos() const {
-    auto inp = std::make_unique<llm_graph_input_pos>(n_pos_per_embd());
+    auto inp = std::make_unique<llm_graph_input_pos>(n_pos_per_token());
 
     auto & cur = inp->pos;
 
-    cur = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens*n_pos_per_embd());
+    cur = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens*n_pos_per_token());
     ggml_set_input(cur);
 
     res->add_input(std::move(inp));
@@ -1025,12 +1033,11 @@ ggml_tensor * llm_graph_context::build_inp_pos() const {
 }
 
 ggml_tensor * llm_graph_context::build_inp_attn_scale() const {
-    auto inp = std::make_unique<llm_graph_input_attn_temp>(hparams.n_attn_temp_floor_scale, hparams.f_attn_temp_scale);
+    auto inp = std::make_unique<llm_graph_input_attn_temp>(n_pos_per_token(), hparams.n_attn_temp_floor_scale, hparams.f_attn_temp_scale);
 
     auto & cur = inp->attn_scale;
 
-    // this need to be 1x1xN for broadcasting
-    cur = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 1, 1, n_tokens);
+    cur = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 1, 1, n_tokens*n_pos_per_token());
     ggml_set_input(cur);
 
     res->add_input(std::move(inp));
@@ -1078,7 +1085,7 @@ ggml_tensor * llm_graph_context::build_inp_cls() const {
 }
 
 ggml_tensor * llm_graph_context::build_inp_s_copy() const {
-    const llama_kv_cache_recurrent * kv_self = static_cast<const llama_kv_cache_recurrent *>(memory);
+    const llama_kv_cache_unified * kv_self = static_cast<const llama_kv_cache_unified *>(memory);
 
     auto inp = std::make_unique<llm_graph_input_s_copy>(kv_self);
 
@@ -1095,7 +1102,7 @@ ggml_tensor * llm_graph_context::build_inp_s_copy() const {
 }
 
 ggml_tensor * llm_graph_context::build_inp_s_mask() const {
-    const llama_kv_cache_recurrent * kv_self = static_cast<const llama_kv_cache_recurrent *>(memory);
+    const llama_kv_cache_unified * kv_self = static_cast<const llama_kv_cache_unified *>(memory);
 
     auto inp = std::make_unique<llm_graph_input_s_mask>(kv_self);
 
@@ -1228,19 +1235,8 @@ ggml_tensor * llm_graph_context::build_attn_mha(
         ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32);
 
         if (v_mla) {
-#if 0
-            // v_mla can be applied as a matrix-vector multiplication with broadcasting across dimension 3 == n_tokens.
-            // However, the code is optimized for dimensions 0 and 1 being large, so this is ineffient.
             cur = ggml_reshape_4d(ctx0, cur, v_mla->ne[0], 1, n_head, n_tokens);
             cur = ggml_mul_mat(ctx0, v_mla, cur);
-#else
-            // It's preferable to do the calculation as a matrix-matrix multiplication with n_tokens in dimension 1.
-            // The permutations are noops and only change how the tensor data is interpreted.
-            cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
-            cur = ggml_mul_mat(ctx0, v_mla, cur);
-            cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
-            cur = ggml_cont(ctx0, cur); // Needed because ggml_reshape_2d expects contiguous inputs.
-#endif
         }
 
         cur = ggml_reshape_2d(ctx0, cur, cur->ne[0]*n_head, n_tokens);
@@ -1420,6 +1416,8 @@ ggml_tensor * llm_graph_context::build_attn(
 
     // store to KV cache
     {
+        GGML_ASSERT(!kv_self->recurrent);
+
         const auto kv_head = kv_self->head;
 
         GGML_ASSERT(kv_self->size == n_ctx);
@@ -1514,6 +1512,25 @@ llm_graph_input_attn_cross * llm_graph_context::build_attn_inp_cross() const {
     return (llm_graph_input_attn_cross *) res->add_input(std::move(inp));
 }
 
+ggml_tensor * llm_graph_context::build_inp_cross_attn_state() const {
+    const int64_t n_embd = hparams.n_embd;
+
+    auto inp = std::make_unique<llm_graph_input_cross_attn_state>();
+
+    ggml_tensor * cur = nullptr;
+
+    inp->cross_attn_state = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd, 1601, 4);
+    ggml_set_input(inp->cross_attn_state);
+
+    cur = inp->cross_attn_state;
+
+    cb(cur, "inp_cross_attn_state", -1);
+
+    res->add_input(std::move(inp));
+
+    return cur;
+}
+
 ggml_tensor * llm_graph_context::build_attn(
         llm_graph_input_attn_cross * inp,
         ggml_cgraph * gf,
@@ -1569,7 +1586,7 @@ ggml_tensor * llm_graph_context::build_copy_mask_state(
          ggml_tensor * state_mask,
              int32_t   n_state,
              int32_t   n_seqs) const {
-    const llama_kv_cache_recurrent * kv_self = static_cast<const llama_kv_cache_recurrent *>(memory);
+    const llama_kv_cache_unified * kv_self = static_cast<const llama_kv_cache_unified *>(memory);
 
     const auto n_kv    = kv_self->n;
     const auto kv_head = kv_self->head;
@@ -1601,7 +1618,7 @@ ggml_tensor * llm_graph_context::build_rwkv_token_shift_load(
          ggml_tensor * state_mask,
   const llama_ubatch & ubatch,
                  int   il) const {
-    const llama_kv_cache_recurrent * kv_self = static_cast<const llama_kv_cache_recurrent *>(memory);
+    const llama_kv_cache_unified * kv_self = static_cast<const llama_kv_cache_unified *>(memory);
 
     const auto token_shift_count = hparams.token_shift_count;
 
@@ -1622,7 +1639,7 @@ ggml_tensor * llm_graph_context::build_rwkv_token_shift_store(
          ggml_tensor * token_shift,
   const llama_ubatch & ubatch,
                  int   il) const {
-    const llama_kv_cache_recurrent * kv_self = static_cast<const llama_kv_cache_recurrent *>(memory);
+    const llama_kv_cache_unified * kv_self = static_cast<const llama_kv_cache_unified *>(memory);
 
     const auto token_shift_count = hparams.token_shift_count;
     const auto n_embd = hparams.n_embd;

llama/llama.cpp/src/llama-graph.h

@@ -19,7 +19,6 @@ struct llama_cparams;
 
 class llama_memory_i;
 class llama_kv_cache_unified;
-class llama_kv_cache_recurrent;
 
 // certain models (typically multi-modal) can produce different types of graphs
 enum llm_graph_type {
@@ -87,31 +86,34 @@ public:
 
     ggml_tensor * tokens = nullptr; // I32 [n_batch]
     ggml_tensor * embd   = nullptr; // F32 [n_embd, n_batch]
+    ggml_tensor * cross_attn_state; // F32 [4, n_embd, 1061]
 };
 
 class llm_graph_input_pos : public llm_graph_input_i {
 public:
-    llm_graph_input_pos(int64_t n_pos_per_embd) : n_pos_per_embd(n_pos_per_embd) {}
+    llm_graph_input_pos(int64_t n_pos_per_token) : n_pos_per_token(n_pos_per_token) {}
     virtual ~llm_graph_input_pos() = default;
 
     void set_input(const llama_ubatch * ubatch) override;
 
     ggml_tensor * pos = nullptr; // I32 [n_batch]
 
-    const int64_t n_pos_per_embd = 1;
+    const int64_t n_pos_per_token = 1;
 };
 
 // temperature tuning, used by llama4
 class llm_graph_input_attn_temp : public llm_graph_input_i {
 public:
-    llm_graph_input_attn_temp(uint32_t n_attn_temp_floor_scale, float f_attn_temp_scale)
-        : n_attn_temp_floor_scale(n_attn_temp_floor_scale), f_attn_temp_scale(f_attn_temp_scale) {}
+    llm_graph_input_attn_temp(int64_t n_pos_per_token, uint32_t n_attn_temp_floor_scale, float f_attn_temp_scale)
+        : n_pos_per_token(n_pos_per_token), n_attn_temp_floor_scale(n_attn_temp_floor_scale), f_attn_temp_scale(f_attn_temp_scale) {}
     virtual ~llm_graph_input_attn_temp() = default;
 
     void set_input(const llama_ubatch * ubatch) override;
 
     ggml_tensor * attn_scale = nullptr; // F32 [n_batch]
 
+    const int64_t n_pos_per_token = 1;
+
     const uint32_t n_attn_temp_floor_scale;
     const float    f_attn_temp_scale;
 };
@@ -187,26 +189,26 @@ public:
 
 class llm_graph_input_s_copy : public llm_graph_input_i {
 public:
-    llm_graph_input_s_copy(const llama_kv_cache_recurrent * kv_self) : kv_self(kv_self) {}
+    llm_graph_input_s_copy(const llama_kv_cache_unified * kv_self) : kv_self(kv_self) {}
     virtual ~llm_graph_input_s_copy() = default;
 
     void set_input(const llama_ubatch * ubatch) override;
 
     ggml_tensor * s_copy; // I32 [kv_size]
 
-    const llama_kv_cache_recurrent * kv_self;
+    const llama_kv_cache_unified * kv_self;
 };
 
 class llm_graph_input_s_mask : public llm_graph_input_i {
 public:
-    llm_graph_input_s_mask(const llama_kv_cache_recurrent * kv_self) : kv_self(kv_self) {}
+    llm_graph_input_s_mask(const llama_kv_cache_unified * kv_self) : kv_self(kv_self) {}
     virtual ~llm_graph_input_s_mask() = default;
 
     void set_input(const llama_ubatch * ubatch) override;
 
     ggml_tensor * s_mask; // F32 [1, n_kv]
 
-    const llama_kv_cache_recurrent * kv_self;
+    const llama_kv_cache_unified * kv_self;
 };
 
 class llm_graph_input_cross_embd : public llm_graph_input_i {
@@ -284,6 +286,16 @@ public:
     const llama_cross * cross = nullptr;
 };
 
+class llm_graph_input_cross_attn_state : public llm_graph_input_i {
+public:
+    llm_graph_input_cross_attn_state()          = default;
+    virtual ~llm_graph_input_cross_attn_state() = default;
+
+    void set_input(const llama_ubatch * ubatch) override;
+
+    ggml_tensor * cross_attn_state; // F32 [4, n_embd, 1061]
+};
+
 //
 // llm_graph_result
 //
@@ -298,7 +310,6 @@ class llm_graph_result_i {
 public:
     virtual ~llm_graph_result_i() = default;
 
-    virtual ggml_tensor * get_tokens()      = 0;
     virtual ggml_tensor * get_logits()      = 0;
     virtual ggml_tensor * get_embd()        = 0;
     virtual ggml_tensor * get_embd_pooled() = 0;
@@ -313,7 +324,6 @@ class llm_graph_result : public llm_graph_result_i {
 public:
     virtual ~llm_graph_result() = default;
 
-    ggml_tensor * get_tokens()      override { return t_tokens; }
     ggml_tensor * get_logits()      override { return t_logits; }
     ggml_tensor * get_embd()        override { return t_embd; }
     ggml_tensor * get_embd_pooled() override { return t_embd_pooled; }
@@ -330,7 +340,6 @@ public:
     }
 
     // important graph nodes
-    ggml_tensor * t_tokens      = nullptr;
     ggml_tensor * t_logits      = nullptr;
     ggml_tensor * t_embd        = nullptr;
     ggml_tensor * t_embd_pooled = nullptr;
@@ -354,8 +363,8 @@ struct llm_graph_params {
     const llama_cparams & cparams;
     const llama_ubatch  & ubatch;
 
-    ggml_backend_sched_t sched;
-    ggml_backend_t backend_cpu;
+    ggml_backend_sched * sched;
+    ggml_backend * backend_cpu;
 
     const llama_adapter_cvec  * cvec;
     const llama_adapter_loras * loras;
@@ -406,9 +415,9 @@ struct llm_graph_context {
 
     ggml_context * ctx0 = nullptr;
 
-    ggml_backend_sched_t sched;
+    ggml_backend_sched * sched;
 
-    ggml_backend_t backend_cpu; // TODO: needed by build_attn_mha, figure out a way to remove?
+    ggml_backend * backend_cpu; // TODO: needed by build_attn_mha, figure out a way to remove?
 
     const llama_adapter_cvec  * cvec;
     const llama_adapter_loras * loras;
@@ -421,7 +430,7 @@ struct llm_graph_context {
 
     llm_graph_context(const llm_graph_params & params);
 
-    int64_t n_pos_per_embd() const;
+    int64_t n_pos_per_token() const;
 
     void cb(ggml_tensor * cur, const char * name, int il) const;
 
@@ -495,6 +504,7 @@ struct llm_graph_context {
     ggml_tensor * build_inp_cls() const;
     ggml_tensor * build_inp_s_copy() const;
     ggml_tensor * build_inp_s_mask() const;
+    ggml_tensor * build_inp_cross_attn_state() const;
 
     ggml_tensor * build_inp_cross_embd() const;
     ggml_tensor * build_inp_pos_bucket_enc() const;

llama/llama.cpp/src/llama-hparams.cpp

@@ -85,3 +85,7 @@ bool llama_hparams::is_swa(uint32_t il) const {
 
     GGML_ABORT("fatal error");
 }
+
+bool llama_hparams::cross_attention_layers(uint32_t il) const {
+    return std::find(cross_attn_layers.begin(), cross_attn_layers.end(), il) != cross_attn_layers.end();
+}

llama/llama.cpp/src/llama-hparams.h

@@ -2,6 +2,8 @@
 
 #include "llama.h"
 
+#include <algorithm>
+
 #include <array>
 
 // bump if necessary
@@ -42,6 +44,7 @@ struct llama_hparams {
     uint32_t n_expert = 0;
     uint32_t n_expert_used = 0;
     uint32_t n_rel_attn_bkts = 0;
+    uint32_t n_vocab = 0;
 
     // note: deepseek2 using MLA converts into MQA with larger heads, then decompresses to MHA
     uint32_t n_embd_head_k_mla = 0;
@@ -56,6 +59,7 @@ struct llama_hparams {
     std::array<uint32_t, LLAMA_MAX_LAYERS> n_ff_arr;
 
     std::array<std::array<uint32_t, LLAMA_MAX_LAYERS>, 4> n_bskcn_arr = {};
+    std::array<uint32_t, LLAMA_MAX_LAYERS> cross_attn_layers;
 
     uint32_t n_layer_dense_lead = 0;
     uint32_t n_lora_q           = 0;
@@ -68,7 +72,6 @@ struct llama_hparams {
     float    expert_weights_scale = 0.0;
     bool     expert_weights_norm  = false;
     uint32_t expert_gating_func   = LLAMA_EXPERT_GATING_FUNC_TYPE_NONE;
-    uint32_t moe_every_n_layers   = 0;
 
     float f_norm_eps;
     float f_norm_rms_eps;
@@ -159,6 +162,9 @@ struct llama_hparams {
     // Block skip connection
     bool n_bskcn(uint32_t n, uint32_t il) const;
 
+    // cross attention layers
+    bool cross_attention_layers(uint32_t il) const;
+
     bool is_swa(uint32_t il) const;
 };
 

llama/llama.cpp/src/llama-kv-cache.h

@@ -2,72 +2,32 @@
 
 #include "llama.h"
 #include "llama-io.h"
-#include "llama-graph.h"
 #include "llama-memory.h"
 
 #include "ggml-cpp.h"
 
+#include <functional>
 #include <set>
 #include <vector>
 
 struct llama_cparams;
 struct llama_hparams;
 struct llama_ubatch;
-struct llama_sbatch;
-struct llama_model;
-struct llama_context;
 
 struct llama_kv_cache : public llama_memory_i {
-    virtual ~llama_kv_cache() = default;
+    using llama_memory_i::llama_memory_i;
 
-    // call if batch processing fails - restores the cache state
-    virtual void restore() = 0;
+    virtual void restore() = 0; // call if batch processing fails - restores the cache state
+    virtual void commit() = 0;  // call after successful batch processing - clears any pending state
 
-    // call after successful batch processing - clears any pending state
-    virtual void commit()  = 0;
+    virtual int32_t get_n_tokens()   const = 0;
+    virtual int32_t get_used_cells() const = 0; // TODO: remove, this is too-specific to the unified cache
 
-    // process any pending defrag/shift/etc. operations
-    // optionally call once before processing a new batch
-    virtual bool update(llama_context & lctx) = 0;
-
-    // schedule a defrag if the fragmentation threshold is exceeded. otherwise, do nothing
-    virtual void defrag_sched(float thold) = 0;
-
-    // simulate full cache, used for allocating worst-case compute buffers
-    virtual void set_full() = 0;
-
-    //
-    // batch processing
-    //
-
-    virtual llama_sbatch sbatch_init(const llama_batch & batch, bool logits_all) = 0;
-
-    // different KV caches require different batch splitting strategies
-    virtual llama_ubatch ubatch_next(llama_sbatch & sbatch, uint32_t n_ubatch, bool embd_pooled) const = 0;
-
-    // find an empty slot of size "n_tokens" in the cache
-    virtual bool find_slot(const llama_ubatch & batch) = 0;
-
-    // getters
-    virtual int32_t   get_n_tokens()   const = 0;
-    virtual int32_t   get_used_cells() const = 0; // TODO: remove, this is too-specific to the unified cache
-    virtual llama_pos get_pos_max()    const = 0;
-    virtual bool      get_can_shift()  const = 0;
+    virtual bool get_can_shift() const = 0;
 
     bool get_can_edit() const override { return get_can_shift(); }
-
-    //
-    // state write/read
-    //
-
-    virtual void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const = 0;
-    virtual void state_read (llama_io_read_i  & io, llama_seq_id seq_id = -1) = 0;
 };
 
-//
-// llama_kv_cache_guard
-//
-
 struct llama_kv_cache_guard {
     llama_kv_cache_guard(llama_kv_cache * kv) : kv(kv) {}
 
@@ -82,7 +42,7 @@ struct llama_kv_cache_guard {
 private:
     llama_kv_cache * kv;
 };
- 
+
 // block of KV slots to move when defragging
 struct llama_kv_defrag_move {
     uint32_t src;
@@ -90,50 +50,65 @@ struct llama_kv_defrag_move {
     uint32_t len;
 };
 
-//
-// llama_kv_cache_unified
-//
+struct llama_kv_cell {
+    llama_pos pos   = -1;
+    llama_pos delta =  0;
+    int32_t   src   = -1; // used by recurrent state models to copy states
+    int32_t   tail  = -1;
 
+    std::set<llama_seq_id> seq_id;
+
+    bool has_seq_id(const llama_seq_id & id) const {
+        return seq_id.find(id) != seq_id.end();
+    }
+
+    bool is_empty() const {
+        return seq_id.empty();
+    }
+
+    bool is_same_seq(const llama_kv_cell & other) const {
+        return seq_id == other.seq_id;
+    }
+};
+
+// ring-buffer of cached KV data
+// TODO: pimpl
 // TODO: add notion of max sequences
 class llama_kv_cache_unified : public llama_kv_cache {
 public:
-    struct kv_cell {
-        llama_pos pos   = -1;
-        llama_pos delta =  0;
-
-        std::set<llama_seq_id> seq_id;
-
-        bool has_seq_id(const llama_seq_id & id) const {
-            return seq_id.find(id) != seq_id.end();
-        }
-
-        bool is_empty() const {
-            return seq_id.empty();
-        }
-
-        bool is_same_seq(const kv_cell & other) const {
-            return seq_id == other.seq_id;
-        }
+    // can be used to query data from the model if needed
+    struct callbacks {
+        std::function<ggml_tensor * (uint32_t n_ctx_per_seq, int il)> get_rope_factors;
     };
 
-    static uint32_t get_padding(const llama_cparams & cparams);
-
     llama_kv_cache_unified(
-            const llama_model & model,
+            const llama_hparams & hparams,
+            callbacks             cbs);
+
+    virtual ~llama_kv_cache_unified() = default;
+
+    // TODO: become constructor
+    bool init(
+            const llama_model & model,   // TODO: do not reference the model
+          const llama_cparams & cparams,
                     ggml_type   type_k,
                     ggml_type   type_v,
-                         bool   v_trans,
-                         bool   offload,
                      uint32_t   kv_size,
-                     uint32_t   padding);
+                         bool   offload);
 
-    ~llama_kv_cache_unified() = default;
+    int32_t get_n_tokens()   const override;
+    int32_t get_used_cells() const override;
 
-    //
-    // llama_memory_i
-    //
+    size_t total_size() const;
+
+    // TODO: better data structures to reduce the cost of this operation
+    llama_pos pos_max() const;
 
     void clear() override;
+    void defrag() override;
+
+    virtual void restore() override;
+    virtual void commit() override;
 
     bool seq_rm  (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1) override;
     void seq_cp  (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) override;
@@ -143,76 +118,25 @@ public:
 
     llama_pos seq_pos_max(llama_seq_id seq_id) const override;
 
-    //
-    // llama_kv_cache
-    //
-
-    void restore() override;
-    void commit()  override;
-
-    bool update(llama_context & ctx) override;
-
-    void defrag_sched(float thold) override;
-
-    void set_full() override;
-
-    llama_sbatch sbatch_init(const llama_batch & batch, bool logits_all) override;
-
-    llama_ubatch ubatch_next(llama_sbatch & sbatch, uint32_t n_ubatch, bool embd_pooled) const override;
+    bool get_can_shift() const override;
 
+    // find an empty slot of size "n_tokens" in the cache
     // updates the cache head
     // Note: On success, it's important that cache.head points
     // to the first cell of the slot.
-    bool find_slot(const llama_ubatch & batch) override;
+    bool find_slot(const llama_ubatch & batch);
 
-    int32_t get_n_tokens()   const override;
-    int32_t get_used_cells() const override;
+    // TODO: maybe not needed
+    uint32_t get_padding(const llama_cparams & cparams) const;
 
-    // TODO: better data structures to reduce the cost of this operation
-    llama_pos get_pos_max() const override;
+    // find how many cells are currently in use
+    uint32_t cell_max() const;
 
-    bool get_can_shift() const override;
-
-    // state write/load
-
-    void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const override;
-    void state_read (llama_io_read_i  & io, llama_seq_id seq_id = -1) override;
-
-    // Note: The value of head isn't only used to optimize searching
-    // for a free KV slot. llama_decode_impl also uses it, so it
-    // cannot be freely changed after a slot has been allocated.
-    uint32_t head = 0;
-    uint32_t size = 0;
-    uint32_t used = 0; // used cells (i.e. at least one seq_id)
-
-    // computed before each graph build
-    uint32_t n = 0;
-
-    std::vector<kv_cell> cells;
-
-    std::vector<ggml_tensor *> k_l; // per layer
-    std::vector<ggml_tensor *> v_l;
-
-private:
-    const llama_model & model;
-    const llama_hparams & hparams;
-
-    bool has_shift = false;
-    bool do_defrag = false;
-
-    bool v_trans   = true;  // the value tensor is transposed
-    bool can_shift = false;
-
-    // required padding
-    uint32_t padding = 1;
-
-    ggml_type type_k = GGML_TYPE_F16;
-    ggml_type type_v = GGML_TYPE_F16;
-
-    std::vector<ggml_context_ptr>        ctxs;
-    std::vector<ggml_backend_buffer_ptr> bufs;
+    size_t size_k_bytes() const;
+    size_t size_v_bytes() const;
 
     // defrag
+
     struct {
         std::vector<llama_kv_defrag_move> moves;
     } defrag_info;
@@ -221,6 +145,7 @@ private:
     bool defrag_prepare(int32_t n_max_nodes);
 
     // commit/restore cache
+
     struct slot_range {
         uint32_t c0 = 0; // note: these are cell indices, not sequence positions
         uint32_t c1 = 0;
@@ -231,125 +156,25 @@ private:
         std::vector<slot_range> ranges;
     } pending;
 
-    // find how many cells are currently in use
-    uint32_t cell_max() const;
-
-    size_t total_size() const;
-
-    size_t size_k_bytes() const;
-    size_t size_v_bytes() const;
-
-    ggml_tensor * build_rope_shift(
-            const llama_cparams & cparams,
-                   ggml_context * ctx,
-                    ggml_tensor * cur,
-                    ggml_tensor * shift,
-                    ggml_tensor * factors,
-                          float   freq_base,
-                          float   freq_scale) const;
-
-    llm_graph_result_ptr build_graph_shift(
-            const llama_cparams & cparams,
-                   ggml_context * ctx,
-                    ggml_cgraph * gf) const;
-
-    llm_graph_result_ptr build_graph_defrag(
-            const llama_cparams & cparams,
-                   ggml_context * ctx,
-                    ggml_cgraph * gf,
-                    const std::vector<llama_kv_defrag_move> & moves) const;
-
-    void state_write_meta(llama_io_write_i & io, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges, llama_seq_id seq_id = -1) const;
-    void state_write_data(llama_io_write_i & io, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges) const;
-
-    bool state_read_meta(llama_io_read_i & io, uint32_t cell_count, llama_seq_id dest_seq_id = -1);
-    bool state_read_data(llama_io_read_i & io, uint32_t cell_count);
-};
-
-//
-// llama_kv_cache_recurrent
-//
-
-class llama_kv_cache_recurrent : public llama_kv_cache {
-public:
-    struct kv_cell {
-        llama_pos pos  = -1;
-        int32_t   src  = -1; // used to copy states
-        int32_t   tail = -1;
-
-        std::set<llama_seq_id> seq_id;
-
-        bool has_seq_id(const llama_seq_id & id) const {
-            return seq_id.find(id) != seq_id.end();
-        }
-
-        bool is_empty() const {
-            return seq_id.empty();
-        }
-
-        bool is_same_seq(const kv_cell & other) const {
-            return seq_id == other.seq_id;
-        }
-    };
-
-    llama_kv_cache_recurrent(
-            const llama_model & model,
-                    ggml_type   type_k,
-                    ggml_type   type_v,
-                         bool   offload,
-                     uint32_t   kv_size);
-
-    ~llama_kv_cache_recurrent() = default;
-
-    //
-    // llama_memory_i
-    //
-
-    void clear() override;
-
-    bool seq_rm  (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1) override;
-    void seq_cp  (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) override;
-    void seq_keep(llama_seq_id seq_id) override;
-    void seq_add (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1, llama_pos delta) override;
-    void seq_div (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1, int d) override;
-
-    llama_pos seq_pos_max(llama_seq_id seq_id) const override;
-
-    //
-    // llama_kv_cache
-    //
-
-    void restore() override;
-    void commit()  override;
-
-    bool update(llama_context & lctx) override;
-
-    void defrag_sched(float thold) override;
-
-    void set_full() override;
-
-    llama_sbatch sbatch_init(const llama_batch & batch, bool logits_all) override;
-
-    llama_ubatch ubatch_next(llama_sbatch & sbatch, uint32_t n_ubatch, bool embd_pooled) const override;
-
-    bool find_slot(const llama_ubatch & batch) override;
-
-    int32_t get_n_tokens()   const override;
-    int32_t get_used_cells() const override;
-
-    // TODO: better data structures to reduce the cost of this operation
-    llama_pos get_pos_max() const override;
-
-    bool get_can_shift() const override;
-
-    // TODO: temporary methods - they are not really const as they do const_cast<>, fix this
-    int32_t s_copy(int i) const;
-    float   s_mask(int i) const;
-
     // state write/load
 
-    void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const override;
-    void state_read (llama_io_read_i  & io, llama_seq_id seq_id = -1) override;
+    void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const;
+    void state_read (llama_io_read_i  & io, llama_seq_id seq_id = -1);
+
+    // members
+
+    const llama_hparams & hparams;
+
+    callbacks cbs;
+
+    bool has_shift = false;
+    bool do_defrag = false;
+
+    // TODO: remove this and implement llama_kv_cache_recurrent instead
+    bool recurrent = false; // with recurrent state models, a cell can hold the state for more than one past token
+
+    bool v_trans   = true;  // the value tensor is transposed
+    bool can_shift = false;
 
     // Note: The value of head isn't only used to optimize searching
     // for a free KV slot. llama_decode_impl also uses it, so it
@@ -361,41 +186,18 @@ public:
     // computed before each graph build
     uint32_t n = 0;
 
-    std::vector<kv_cell> cells;
+    std::vector<llama_kv_cell> cells;
 
     std::vector<ggml_tensor *> k_l; // per layer
     std::vector<ggml_tensor *> v_l;
 
 private:
-    //const llama_model & model;
-    const llama_hparams & hparams;
-
-    // commit/restore cache
-    // TODO: rework for recurrent cache
-    struct slot_range {
-        uint32_t c0 = 0; // note: these are cell indices, not sequence positions
-        uint32_t c1 = 0;
-    };
-
-    // pending cell updates that are not yet committed
-    struct {
-        std::vector<slot_range> ranges;
-    } pending;
-
     ggml_type type_k = GGML_TYPE_F16;
     ggml_type type_v = GGML_TYPE_F16;
 
     std::vector<ggml_context_ptr>        ctxs;
     std::vector<ggml_backend_buffer_ptr> bufs;
 
-    // find how many cells are currently in use
-    uint32_t cell_max() const;
-
-    size_t total_size() const;
-
-    size_t size_k_bytes() const;
-    size_t size_v_bytes() const;
-
     void state_write_meta(llama_io_write_i & io, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges, llama_seq_id seq_id = -1) const;
     void state_write_data(llama_io_write_i & io, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges) const;
 
@@ -403,6 +205,11 @@ private:
     bool state_read_data(llama_io_read_i & io, uint32_t cell_count);
 };
 
+// TODO: temporary reusing llama_kv_cache_unified -- implement recurrent cache and simplify llama_kv_cache_unified
+//class llama_kv_cache_recurrent : public llama_kv_cache_unified {
+//public:
+//    using llama_kv_cache_unified::llama_kv_cache_unified;
+//};
 
 //
 // kv cache view

llama/llama.cpp/src/llama-memory.h

@@ -2,22 +2,12 @@
 
 #include "llama.h"
 
-struct llama_memory_params {
-    // kv cache
-    ggml_type type_k;
-    ggml_type type_v;
-
-    // parameters for other types of memory
-    // ...
-};
-
 // general concept of LLM memory
 // the KV cache is a type of LLM memory, but there can be other types
 class llama_memory_i {
 public:
-    virtual ~llama_memory_i() = default;
-
     virtual void clear() = 0;
+    virtual void defrag() = 0;
 
     virtual bool seq_rm  (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1) = 0;
     virtual void seq_cp  (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) = 0;