chore: ⬆️ Update leejet/stable-diffusion.cpp to bd17f53b7386fb5f60e8587b75e73c4b2fed3426 (#9854)

⬆️ Update leejet/stable-diffusion.cpp

Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>

.agents/llama-cpp-backend.md

@@ -61,6 +61,12 @@ Always check `llama.cpp` for new model configuration options that should be supp
    - `reasoning_format` - Reasoning format options
    - Any new flags or parameters
 
+### Speculative Decoding Types
+
+The `spec_type` option in `grpc-server.cpp` delegates to upstream's `common_speculative_types_from_names()`, so new speculative types added to the `common_speculative_type_from_name` map in `common/speculative.cpp` are picked up automatically with no code changes - only docs need an entry in `docs/content/advanced/model-configuration.md`. Current values: `none`, `draft-simple`, `draft-eagle3`, `draft-mtp`, `ngram-simple`, `ngram-map-k`, `ngram-map-k4v`, `ngram-mod`, `ngram-cache`.
+
+`draft-mtp` (Multi-Token Prediction, [ggml-org/llama.cpp#22673](https://github.com/ggml-org/llama.cpp/pull/22673)) does not need a separate draft GGUF: when `spec_type` includes `draft-mtp` and `draftmodel` is empty, the upstream server creates an MTP context off the target model itself. LocalAI's gRPC layer needs no changes for this — it works through the existing `params.speculative.types` plumbing and the derived `cparams.n_rs_seq = params.speculative.need_n_rs_seq()` in `common_context_params_to_llama`.
+
 ### Implementation Guidelines
 
 1. **Feature Parity**: Always aim for feature parity with llama.cpp's implementation

backend/cpp/ds4/Makefile

@@ -1,10 +1,10 @@
 # ds4 backend Makefile.
 #
-# Upstream pin lives below as DS4_VERSION?=0cba357ca1bc0e7510421cc26888e420ea942123
+# Upstream pin lives below as DS4_VERSION?=ef0a4905d05263df8e63689f2dd1efac618a752c
 # (.github/bump_deps.sh) can find and update it - matches the
 # llama-cpp / ik-llama-cpp / turboquant convention.
 
-DS4_VERSION?=0cba357ca1bc0e7510421cc26888e420ea942123
+DS4_VERSION?=ef0a4905d05263df8e63689f2dd1efac618a752c
 DS4_REPO?=https://github.com/antirez/ds4
 
 CURRENT_MAKEFILE_DIR := $(dir $(abspath $(lastword $(MAKEFILE_LIST))))

backend/cpp/ik-llama-cpp/Makefile

@@ -1,5 +1,5 @@
 
-IK_LLAMA_VERSION?=949bb8f1d660fc1264c137a6f3dbd619375f6134
+IK_LLAMA_VERSION?=3e573cfea6e0a332eff822ffbdb1dd3b112e9051
 LLAMA_REPO?=https://github.com/ikawrakow/ik_llama.cpp
 
 CMAKE_ARGS?=

backend/cpp/llama-cpp/Makefile

@@ -1,5 +1,5 @@
 
-LLAMA_VERSION?=a9883db8ee021cf16783016a60996d41820b5195
+LLAMA_VERSION?=0253fb21f595246f54c192fe8332f34173be251b
 LLAMA_REPO?=https://github.com/ggerganov/llama.cpp
 
 CMAKE_ARGS?=

backend/cpp/llama-cpp/grpc-server.cpp

@@ -32,6 +32,7 @@
 #include <grpcpp/health_check_service_interface.h>
 #include <grpcpp/security/server_credentials.h>
 #include <regex>
+#include <algorithm>
 #include <atomic>
 #include <cstdlib>
 #include <fstream>
@@ -450,6 +451,8 @@ static void params_parse(server_context& /*ctx_server*/, const backend::ModelOpt
         // vector; the turboquant fork still uses the legacy scalar. The
         // LOCALAI_LEGACY_LLAMA_CPP_SPEC macro is injected by
         // backend/cpp/turboquant/patch-grpc-server.sh for fork builds only.
+        // Upstream renamed COMMON_SPECULATIVE_TYPE_DRAFT -> ..._DRAFT_SIMPLE
+        // in ggml-org/llama.cpp#22964; the fork still uses the old name.
 #ifdef LOCALAI_LEGACY_LLAMA_CPP_SPEC
         if (params.speculative.type == COMMON_SPECULATIVE_TYPE_NONE) {
             params.speculative.type = COMMON_SPECULATIVE_TYPE_DRAFT;
@@ -458,7 +461,7 @@ static void params_parse(server_context& /*ctx_server*/, const backend::ModelOpt
         const bool no_spec_type = params.speculative.types.empty() ||
             (params.speculative.types.size() == 1 && params.speculative.types[0] == COMMON_SPECULATIVE_TYPE_NONE);
         if (no_spec_type) {
-            params.speculative.types = { COMMON_SPECULATIVE_TYPE_DRAFT };
+            params.speculative.types = { COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE };
         }
 #endif
     }
@@ -685,6 +688,136 @@ static void params_parse(server_context& /*ctx_server*/, const backend::ModelOpt
                     // If conversion fails, keep default value (8)
                 }
             }
+
+        // --- physical batch size (upstream -ub / --ubatch-size) ---
+        // Note: line ~482 already aliases n_ubatch to n_batch as a default; this
+        // option lets users decouple the two (useful for embeddings/rerank).
+        } else if (!strcmp(optname, "n_ubatch") || !strcmp(optname, "ubatch")) {
+            if (optval != NULL) {
+                try { params.n_ubatch = std::stoi(optval_str); } catch (...) {}
+            }
+
+        // --- main-model batch threads (upstream -tb / --threads-batch) ---
+        } else if (!strcmp(optname, "threads_batch") || !strcmp(optname, "n_threads_batch")) {
+            if (optval != NULL) {
+                try {
+                    int n = std::stoi(optval_str);
+                    if (n <= 0) n = (int)std::thread::hardware_concurrency();
+                    params.cpuparams_batch.n_threads = n;
+                } catch (...) {}
+            }
+
+        // --- pooling type for embeddings (upstream --pooling) ---
+        } else if (!strcmp(optname, "pooling_type") || !strcmp(optname, "pooling")) {
+            if (optval != NULL) {
+                if      (optval_str == "none") params.pooling_type = LLAMA_POOLING_TYPE_NONE;
+                else if (optval_str == "mean") params.pooling_type = LLAMA_POOLING_TYPE_MEAN;
+                else if (optval_str == "cls")  params.pooling_type = LLAMA_POOLING_TYPE_CLS;
+                else if (optval_str == "last") params.pooling_type = LLAMA_POOLING_TYPE_LAST;
+                else if (optval_str == "rank") params.pooling_type = LLAMA_POOLING_TYPE_RANK;
+                // unknown values silently leave UNSPECIFIED (auto-detect)
+            }
+
+        // --- llama log verbosity threshold (upstream -lv / --verbosity) ---
+        } else if (!strcmp(optname, "verbosity")) {
+            if (optval != NULL) {
+                try { params.verbosity = std::stoi(optval_str); } catch (...) {}
+            }
+
+        // --- O_DIRECT model loading (upstream --direct-io) ---
+        } else if (!strcmp(optname, "direct_io") || !strcmp(optname, "use_direct_io")) {
+            if (optval_str == "true" || optval_str == "1" || optval_str == "yes" || optval_str == "on" || optval_str == "enabled") {
+                params.use_direct_io = true;
+            } else if (optval_str == "false" || optval_str == "0" || optval_str == "no" || optval_str == "off" || optval_str == "disabled") {
+                params.use_direct_io = false;
+            }
+
+        // --- embedding normalization (upstream --embd-normalize) ---
+        // -1 none, 0 max-abs, 1 taxicab, 2 L2 (default), >2 p-norm
+        } else if (!strcmp(optname, "embd_normalize") || !strcmp(optname, "embedding_normalize")) {
+            if (optval != NULL) {
+                try { params.embd_normalize = std::stoi(optval_str); } catch (...) {}
+            }
+
+        // --- reasoning parser (upstream --reasoning-format) ---
+        // Picks the parser for <think> blocks emitted by reasoning models.
+        // none / auto / deepseek / deepseek-legacy
+        } else if (!strcmp(optname, "reasoning_format")) {
+            if (optval != NULL) {
+                if      (optval_str == "none")             params.reasoning_format = COMMON_REASONING_FORMAT_NONE;
+                else if (optval_str == "auto")             params.reasoning_format = COMMON_REASONING_FORMAT_AUTO;
+                else if (optval_str == "deepseek")         params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK;
+                else if (optval_str == "deepseek-legacy" || optval_str == "deepseek_legacy")
+                                                            params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY;
+                // unknown values silently keep the upstream default (DEEPSEEK)
+            }
+
+        // --- reasoning budget (upstream --reasoning-budget) ---
+        // -1 unlimited, 0 disabled, >0 token budget for thinking blocks.
+        // Distinct from per-request `enable_thinking` (chat_template_kwargs).
+        } else if (!strcmp(optname, "enable_reasoning") || !strcmp(optname, "reasoning_budget")) {
+            if (optval != NULL) {
+                try { params.enable_reasoning = std::stoi(optval_str); } catch (...) {}
+            }
+
+        // --- prefill assistant turn (upstream --no-prefill-assistant) ---
+        } else if (!strcmp(optname, "prefill_assistant")) {
+            if (optval_str == "true" || optval_str == "1" || optval_str == "yes" || optval_str == "on" || optval_str == "enabled") {
+                params.prefill_assistant = true;
+            } else if (optval_str == "false" || optval_str == "0" || optval_str == "no" || optval_str == "off" || optval_str == "disabled") {
+                params.prefill_assistant = false;
+            }
+
+        // --- mmproj GPU offload (upstream --no-mmproj-offload, inverted) ---
+        } else if (!strcmp(optname, "mmproj_use_gpu") || !strcmp(optname, "mmproj_offload")) {
+            if (optval_str == "true" || optval_str == "1" || optval_str == "yes" || optval_str == "on" || optval_str == "enabled") {
+                params.mmproj_use_gpu = true;
+            } else if (optval_str == "false" || optval_str == "0" || optval_str == "no" || optval_str == "off" || optval_str == "disabled") {
+                params.mmproj_use_gpu = false;
+            }
+
+        // --- per-image vision token budget (upstream --image-min/max-tokens) ---
+        } else if (!strcmp(optname, "image_min_tokens")) {
+            if (optval != NULL) {
+                try { params.image_min_tokens = std::stoi(optval_str); } catch (...) {}
+            }
+        } else if (!strcmp(optname, "image_max_tokens")) {
+            if (optval != NULL) {
+                try { params.image_max_tokens = std::stoi(optval_str); } catch (...) {}
+            }
+
+        // --- main-model tensor buffer overrides (upstream --override-tensor) ---
+        // Format: <tensor regex>=<buffer type>,<tensor regex>=<buffer type>,...
+        // Mirrors the existing `draft_override_tensor` parser below.
+        } else if (!strcmp(optname, "override_tensor") || !strcmp(optname, "tensor_buft_overrides")) {
+            ggml_backend_load_all();
+            std::map<std::string, ggml_backend_buffer_type_t> buft_list;
+            for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
+                auto * dev = ggml_backend_dev_get(i);
+                auto * buft = ggml_backend_dev_buffer_type(dev);
+                if (buft) {
+                    buft_list[ggml_backend_buft_name(buft)] = buft;
+                }
+            }
+            static std::list<std::string> override_names;
+            std::string cur;
+            auto flush = [&](const std::string & spec) {
+                auto pos = spec.find('=');
+                if (pos == std::string::npos) return;
+                const std::string name = spec.substr(0, pos);
+                const std::string type = spec.substr(pos + 1);
+                auto it = buft_list.find(type);
+                if (it == buft_list.end()) return; // unknown buffer type: ignore
+                override_names.push_back(name);
+                params.tensor_buft_overrides.push_back(
+                    {override_names.back().c_str(), it->second});
+            };
+            for (char c : optval_str) {
+                if (c == ',') { if (!cur.empty()) { flush(cur); cur.clear(); } }
+                else { cur.push_back(c); }
+            }
+            if (!cur.empty()) flush(cur);
+
         // Speculative decoding options
         } else if (!strcmp(optname, "spec_type") || !strcmp(optname, "speculative_type")) {
 #ifdef LOCALAI_LEGACY_LLAMA_CPP_SPEC
@@ -701,16 +834,27 @@ static void params_parse(server_context& /*ctx_server*/, const backend::ModelOpt
             // Upstream switched to a vector of types (comma-separated for multi-type
             // chaining via common_speculative_types_from_names). We keep accepting a
             // single value here, but also tolerate comma-separated lists.
+            //
+            // ggml-org/llama.cpp#22964 also renamed the registered names from
+            // underscore- to dash-separated form, and replaced the bare
+            // `draft`/`eagle3` aliases with `draft-simple`/`draft-eagle3`. We
+            // normalize each token here so existing model configs keep working.
+            auto normalize_spec_name = [](std::string s) -> std::string {
+                std::replace(s.begin(), s.end(), '_', '-');
+                if (s == "draft")  return "draft-simple";
+                if (s == "eagle3") return "draft-eagle3";
+                return s;
+            };
             std::vector<std::string> names;
             std::string item;
             for (char c : optval_str) {
                 if (c == ',') {
-                    if (!item.empty()) { names.push_back(item); item.clear(); }
+                    if (!item.empty()) { names.push_back(normalize_spec_name(item)); item.clear(); }
                 } else {
                     item.push_back(c);
                 }
             }
-            if (!item.empty()) names.push_back(item);
+            if (!item.empty()) names.push_back(normalize_spec_name(item));
             auto parsed = common_speculative_types_from_names(names);
             if (!parsed.empty()) {
                 params.speculative.types = parsed;
@@ -2794,7 +2938,9 @@ public:
             }
         }
 
-        int embd_normalize = 2; // default to Euclidean/L2 norm
+        // Honor the load-time embd_normalize set via options:embd_normalize.
+        // -1 none, 0 max-abs, 1 taxicab, 2 L2 (default), >2 p-norm.
+        int embd_normalize = params_base.embd_normalize;
         // create and queue the task
         auto rd = ctx_server.get_response_reader();
         {

backend/go/stablediffusion-ggml/Makefile

@@ -8,7 +8,7 @@ JOBS?=$(shell nproc --ignore=1)
 
 # stablediffusion.cpp (ggml)
 STABLEDIFFUSION_GGML_REPO?=https://github.com/leejet/stable-diffusion.cpp
-STABLEDIFFUSION_GGML_VERSION?=90e87bc846f17059771efb8aaa31e9ef0cab6f78
+STABLEDIFFUSION_GGML_VERSION?=bd17f53b7386fb5f60e8587b75e73c4b2fed3426
 
 CMAKE_ARGS+=-DGGML_MAX_NAME=128
 

backend/go/whisper/Makefile

@@ -8,7 +8,7 @@ JOBS?=$(shell nproc --ignore=1)
 
 # whisper.cpp version
 WHISPER_REPO?=https://github.com/ggml-org/whisper.cpp
-WHISPER_CPP_VERSION?=3e9b7d0fef3528ee2208da3cdb873a2c53d2ae2f
+WHISPER_CPP_VERSION?=968eebe77225d25e57a3f981da7c696310f0e881
 SO_TARGET?=libgowhisper.so
 
 CMAKE_ARGS+=-DBUILD_SHARED_LIBS=OFF

backend/python/vllm/requirements-cublas13-after.txt

@@ -3,5 +3,5 @@
 # on a cu130 host. Pull the cu130-flavoured wheel from vLLM's per-tag index
 # instead — the cublas13 case in install.sh adds --index-strategy=unsafe-best-match
 # so uv consults this index alongside PyPI.
---extra-index-url https://wheels.vllm.ai/0.20.2/cu130
-vllm==0.20.2
+--extra-index-url https://wheels.vllm.ai/0.21.0/cu130
+vllm==0.21.0

core/config/gguf.go

@@ -54,6 +54,13 @@ func guessGGUFFromFile(cfg *ModelConfig, f *gguf.GGUFFile, defaultCtx int) {
 		cfg.modelTemplate = chatTemplate.ValueString()
 	}
 
+	// Auto-enable Multi-Token Prediction (ggml-org/llama.cpp#22673) when the
+	// GGUF carries an embedded MTP head. Skipped silently for non-MTP models
+	// and when the user already configured a spec_type.
+	if n, ok := HasEmbeddedMTPHead(f); ok {
+		ApplyMTPDefaults(cfg, n)
+	}
+
 	// Thinking support detection is done after model load via DetectThinkingSupportFromBackend
 
 	// template estimations

core/config/mtp.go

@@ -0,0 +1,84 @@
+package config
+
+import (
+	"strings"
+
+	gguf "github.com/gpustack/gguf-parser-go"
+	"github.com/mudler/xlog"
+)
+
+// mtpSpecOptions lists the speculative-decoding option keys auto-applied when
+// an MTP head is detected on a llama-cpp GGUF. Defaults track the upstream
+// MTP PR (ggml-org/llama.cpp#22673):
+//
+//   - spec_type:draft-mtp      activates Multi-Token Prediction
+//   - spec_n_max:6             draft window
+//   - spec_p_min:0.75          pinned because upstream marked the 0.75 default
+//     with a "change to 0.0f" TODO; locking it here keeps acceptance
+//     thresholds stable across future bumps
+var mtpSpecOptions = []string{
+	"spec_type:draft-mtp",
+	"spec_n_max:6",
+	"spec_p_min:0.75",
+}
+
+// MTPSpecOptions returns a copy of the option keys auto-applied when an MTP
+// head is detected. Exported for testing and for the importer.
+func MTPSpecOptions() []string {
+	out := make([]string, len(mtpSpecOptions))
+	copy(out, mtpSpecOptions)
+	return out
+}
+
+// HasEmbeddedMTPHead reports whether the parsed GGUF declares a Multi-Token
+// Prediction head. Detection reads `<arch>.nextn_predict_layers`, which is
+// what `gguf_writer.add_nextn_predict_layers(n)` emits in upstream's
+// `conversion/qwen.py` MTP mixin. A positive layer count means the head is
+// present in the same GGUF as the trunk.
+func HasEmbeddedMTPHead(f *gguf.GGUFFile) (uint32, bool) {
+	if f == nil {
+		return 0, false
+	}
+	arch := f.Architecture().Architecture
+	if arch == "" {
+		return 0, false
+	}
+	v, ok := f.Header.MetadataKV.Get(arch + ".nextn_predict_layers")
+	if !ok {
+		return 0, false
+	}
+	n := gguf.ValueNumeric[uint32](v)
+	return n, n > 0
+}
+
+// hasSpecTypeOption returns true when the slice already contains a
+// user-configured `spec_type:` / `speculative_type:` entry. Used to avoid
+// clobbering an explicit choice with the MTP auto-defaults.
+func hasSpecTypeOption(opts []string) bool {
+	for _, o := range opts {
+		if strings.HasPrefix(o, "spec_type:") || strings.HasPrefix(o, "speculative_type:") {
+			return true
+		}
+	}
+	return false
+}
+
+// ApplyMTPDefaults appends the auto-MTP option keys to cfg.Options when none
+// is already configured. It is a no-op when the user already picked a
+// `spec_type` (either via YAML or via the importer's preferences flow).
+//
+// `layers` is the value read from `<arch>.nextn_predict_layers` and is only
+// used for the diagnostic log line.
+func ApplyMTPDefaults(cfg *ModelConfig, layers uint32) {
+	if cfg == nil {
+		return
+	}
+	if hasSpecTypeOption(cfg.Options) {
+		xlog.Debug("[mtp] embedded MTP head detected but spec_type already configured; leaving user choice intact",
+			"name", cfg.Name, "nextn_layers", layers)
+		return
+	}
+	cfg.Options = append(cfg.Options, mtpSpecOptions...)
+	xlog.Info("[mtp] embedded MTP head detected; enabling draft-mtp speculative decoding",
+		"name", cfg.Name, "nextn_layers", layers, "spec_n_max", 6, "spec_p_min", 0.75)
+}

core/config/mtp_test.go

@@ -0,0 +1,86 @@
+package config_test
+
+import (
+	. "github.com/mudler/LocalAI/core/config"
+
+	. "github.com/onsi/ginkgo/v2"
+	. "github.com/onsi/gomega"
+)
+
+var _ = Describe("MTP auto-defaults", func() {
+	Context("MTPSpecOptions", func() {
+		It("returns the upstream-recommended speculative tuple", func() {
+			Expect(MTPSpecOptions()).To(Equal([]string{
+				"spec_type:draft-mtp",
+				"spec_n_max:6",
+				"spec_p_min:0.75",
+			}))
+		})
+
+		It("returns a defensive copy so callers cannot mutate the package default", func() {
+			opts := MTPSpecOptions()
+			opts[0] = "spec_type:none"
+			Expect(MTPSpecOptions()[0]).To(Equal("spec_type:draft-mtp"))
+		})
+	})
+
+	Context("ApplyMTPDefaults", func() {
+		It("appends MTP options when nothing is configured", func() {
+			cfg := &ModelConfig{Name: "qwen-mtp"}
+			ApplyMTPDefaults(cfg, 1)
+			Expect(cfg.Options).To(Equal([]string{
+				"spec_type:draft-mtp",
+				"spec_n_max:6",
+				"spec_p_min:0.75",
+			}))
+		})
+
+		It("preserves unrelated options already on the config", func() {
+			cfg := &ModelConfig{
+				Name:    "qwen-mtp",
+				Options: []string{"use_jinja:true", "cache_reuse:256"},
+			}
+			ApplyMTPDefaults(cfg, 1)
+			Expect(cfg.Options).To(Equal([]string{
+				"use_jinja:true",
+				"cache_reuse:256",
+				"spec_type:draft-mtp",
+				"spec_n_max:6",
+				"spec_p_min:0.75",
+			}))
+		})
+
+		It("is a no-op when the user already configured spec_type", func() {
+			cfg := &ModelConfig{
+				Name:    "qwen-mtp",
+				Options: []string{"spec_type:ngram-simple", "use_jinja:true"},
+			}
+			ApplyMTPDefaults(cfg, 1)
+			Expect(cfg.Options).To(Equal([]string{
+				"spec_type:ngram-simple",
+				"use_jinja:true",
+			}))
+		})
+
+		It("also respects the legacy speculative_type alias", func() {
+			cfg := &ModelConfig{
+				Name:    "qwen-mtp",
+				Options: []string{"speculative_type:ngram-mod"},
+			}
+			ApplyMTPDefaults(cfg, 1)
+			Expect(cfg.Options).To(Equal([]string{"speculative_type:ngram-mod"}))
+		})
+
+		It("tolerates a nil config", func() {
+			Expect(func() { ApplyMTPDefaults(nil, 1) }).ToNot(Panic())
+		})
+	})
+
+	Context("HasEmbeddedMTPHead", func() {
+		It("returns false on a nil GGUF file", func() {
+			n, ok := HasEmbeddedMTPHead(nil)
+			Expect(ok).To(BeFalse())
+			Expect(n).To(BeZero())
+		})
+	})
+})

core/gallery/importers/llama-cpp.go

@@ -1,10 +1,13 @@
 package importers
 
 import (
+	"context"
 	"encoding/json"
 	"path/filepath"
 	"strings"
+	"time"
 
+	gguf "github.com/gpustack/gguf-parser-go"
 	"github.com/mudler/LocalAI/core/config"
 	"github.com/mudler/LocalAI/core/gallery"
 	"github.com/mudler/LocalAI/core/schema"
@@ -261,6 +264,13 @@ func (i *LlamaCPPImporter) Import(details Details) (gallery.ModelConfig, error)
 	// Apply per-model-family inference parameter defaults
 	config.ApplyInferenceDefaults(&modelConfig, details.URI)
 
+	// Auto-detect Multi-Token Prediction heads (ggml-org/llama.cpp#22673) and
+	// enable speculative decoding. Mirrors the load-time hook so freshly
+	// imported configs already carry spec_type:draft-mtp before the model is
+	// ever loaded - users see it in the YAML preview rather than discovering
+	// it after the first start.
+	maybeApplyMTPDefaults(&modelConfig, details, &cfg)
+
 	data, err := yaml.Marshal(modelConfig)
 	if err != nil {
 		return gallery.ModelConfig{}, err
@@ -291,6 +301,85 @@ func pickPreferredGroup(groups []hfapi.ShardGroup, prefs []string) *hfapi.ShardG
 	return &groups[len(groups)-1]
 }
 
+// maybeApplyMTPDefaults parses the picked GGUF header (range-fetched over
+// HTTP for HF/URL imports) and, if the file declares a Multi-Token Prediction
+// head, appends the auto-MTP option keys to modelConfig.Options. Failures
+// during the probe are non-fatal: the importer keeps the config without MTP
+// so an unrelated network blip or weird header doesn't break the import.
+//
+// OCI/Ollama URIs are skipped because the artifact isn't directly fetchable
+// as a GGUF byte stream - the load-time hook (core/config/gguf.go) covers
+// those once the model is materialised on disk.
+func maybeApplyMTPDefaults(modelConfig *config.ModelConfig, details Details, cfg *gallery.ModelConfig) {
+	probeURL := pickMTPProbeURL(details, cfg)
+	if probeURL == "" {
+		return
+	}
+
+	ctx, cancel := context.WithTimeout(context.Background(), 30*time.Second)
+	defer cancel()
+
+	defer func() {
+		if r := recover(); r != nil {
+			xlog.Debug("[mtp-importer] panic while probing GGUF header", "uri", probeURL, "recover", r)
+		}
+	}()
+
+	f, err := gguf.ParseGGUFFileRemote(ctx, probeURL)
+	if err != nil {
+		xlog.Debug("[mtp-importer] failed to read remote GGUF header for MTP detection", "uri", probeURL, "error", err)
+		return
+	}
+
+	n, ok := config.HasEmbeddedMTPHead(f)
+	if !ok {
+		return
+	}
+	config.ApplyMTPDefaults(modelConfig, n)
+}
+
+// pickMTPProbeURL returns an HTTP(S) URL pointing at the main (non-mmproj)
+// GGUF shard that should be inspected for an MTP head, or "" when no
+// suitable URL is available. Custom URI schemes (`huggingface://`,
+// `ollama://`, etc.) are run through `downloader.URI.ResolveURL` so the
+// resulting URL is something `gguf.ParseGGUFFileRemote` can actually open.
+// OCI/Ollama URIs are skipped because the artifact is not directly
+// streamable as a GGUF byte range.
+func pickMTPProbeURL(details Details, cfg *gallery.ModelConfig) string {
+	uri := downloader.URI(details.URI)
+
+	if uri.LooksLikeOCI() {
+		return ""
+	}
+
+	if strings.HasSuffix(strings.ToLower(details.URI), ".gguf") {
+		return resolveHTTPProbe(details.URI)
+	}
+
+	for _, f := range cfg.Files {
+		lower := strings.ToLower(f.Filename)
+		if strings.Contains(lower, "mmproj") {
+			continue
+		}
+		if !strings.HasSuffix(lower, ".gguf") {
+			continue
+		}
+		return resolveHTTPProbe(f.URI)
+	}
+	return ""
+}
+
+// resolveHTTPProbe resolves an importer-side URI to the HTTP(S) URL that
+// `gguf.ParseGGUFFileRemote` can range-fetch. Returns "" if the URI can't
+// be reduced to an HTTP(S) endpoint (e.g. local path, unsupported scheme).
+func resolveHTTPProbe(uri string) string {
+	resolved := downloader.URI(uri).ResolveURL()
+	if downloader.URI(resolved).LooksLikeHTTPURL() {
+		return resolved
+	}
+	return ""
+}
+
 // appendShardGroup copies every shard of group into cfg.Files under dest,
 // skipping any entry whose target filename is already present so repeated
 // calls (e.g. the rare case of mmproj + model picking the same group)

core/http/endpoints/localai/video.go

@@ -22,12 +22,19 @@ import (
 	"github.com/mudler/LocalAI/core/backend"
 
 	model "github.com/mudler/LocalAI/pkg/model"
+	"github.com/mudler/LocalAI/pkg/utils"
 	"github.com/mudler/xlog"
 )
 
+var videoDownloadClient = http.Client{Timeout: 30 * time.Second}
+
 func downloadFile(url string) (string, error) {
+	if err := utils.ValidateExternalURL(url); err != nil {
+		return "", fmt.Errorf("URL validation failed: %w", err)
+	}
+
 	// Get the data
-	resp, err := http.Get(url)
+	resp, err := videoDownloadClient.Get(url)
 	if err != nil {
 		return "", err
 	}

core/http/endpoints/openai/chat.go

@@ -131,13 +131,19 @@ func ChatEndpoint(cl *config.ModelConfigLoader, ml *model.ModelLoader, evaluator
 				delta.Reasoning = &reasoningDelta
 			}
 
+			// Usage rides as a struct field for the consumer to track the
+			// running cumulative — it is stripped before JSON marshal so the
+			// wire chunk stays spec-compliant (no `usage` on intermediate
+			// chunks). The dedicated trailer chunk (when include_usage=true)
+			// carries the final totals.
+			usageForChunk := usage
 			resp := schema.OpenAIResponse{
 				ID:      id,
 				Created: created,
 				Model:   req.Model, // we have to return what the user sent here, due to OpenAI spec.
 				Choices: []schema.Choice{{Delta: delta, Index: 0, FinishReason: nil}},
 				Object:  "chat.completion.chunk",
-				Usage:   usage,
+				Usage:   &usageForChunk,
 			}
 
 			responses <- resp
@@ -164,7 +170,7 @@ func ChatEndpoint(cl *config.ModelConfigLoader, ml *model.ModelLoader, evaluator
 		hasChatDeltaToolCalls := false
 		hasChatDeltaContent := false
 
-		_, tokenUsage, chatDeltas, err := ComputeChoices(req, prompt, config, cl, startupOptions, loader, func(s string, c *[]schema.Choice) {}, func(s string, usage backend.TokenUsage) bool {
+		_, _, chatDeltas, err := ComputeChoices(req, prompt, config, cl, startupOptions, loader, func(s string, c *[]schema.Choice) {}, func(s string, usage backend.TokenUsage) bool {
 			result += s
 
 			// Track whether ChatDeltas from the C++ autoparser contain
@@ -387,16 +393,11 @@ func ChatEndpoint(cl *config.ModelConfigLoader, ml *model.ModelLoader, evaluator
 
 		switch {
 		case noActionToRun:
-			usage := schema.OpenAIUsage{
-				PromptTokens:     tokenUsage.Prompt,
-				CompletionTokens: tokenUsage.Completion,
-				TotalTokens:      tokenUsage.Prompt + tokenUsage.Completion,
-			}
-			if extraUsage {
-				usage.TimingTokenGeneration = tokenUsage.TimingTokenGeneration
-				usage.TimingPromptProcessing = tokenUsage.TimingPromptProcessing
-			}
-
+			// Token-cumulative usage is communicated to the streaming
+			// consumer via the per-token callback's chunk struct (stripped
+			// before wire marshal). The final usage trailer — when the
+			// caller opted in with stream_options.include_usage — is built
+			// by the outer streaming loop, not here.
 			var result string
 			if !sentInitialRole {
 				var hqErr error
@@ -409,7 +410,7 @@ func ChatEndpoint(cl *config.ModelConfigLoader, ml *model.ModelLoader, evaluator
 			for _, chunk := range buildNoActionFinalChunks(
 				id, req.Model, created,
 				sentInitialRole, sentReasoning,
-				result, reasoning, usage,
+				result, reasoning,
 			) {
 				responses <- chunk
 			}
@@ -724,7 +725,13 @@ func ChatEndpoint(cl *config.ModelConfigLoader, ml *model.ModelLoader, evaluator
 							xlog.Debug("No choices in the response, skipping")
 							continue
 						}
-						usage = &ev.Usage // Copy a pointer to the latest usage chunk so that the stop message can reference it
+						// Capture the running cumulative usage from this chunk
+						// (when present) so the include_usage trailer can carry
+						// the final totals. Usage is stripped before marshal
+						// below so the wire chunk stays spec-compliant.
+						if ev.Usage != nil {
+							usage = ev.Usage
+						}
 						if len(ev.Choices[0].Delta.ToolCalls) > 0 {
 							toolsCalled = true
 							// Collect and merge tool call deltas for MCP execution
@@ -740,6 +747,11 @@ func ChatEndpoint(cl *config.ModelConfigLoader, ml *model.ModelLoader, evaluator
 								collectedContent += *sp
 							}
 						}
+						// OpenAI streaming spec: intermediate chunks must NOT
+						// carry a `usage` field. Strip the tracking copy
+						// before marshalling — usage is delivered via the
+						// dedicated trailer chunk when include_usage=true.
+						ev.Usage = nil
 						respData, err := json.Marshal(ev)
 						if err != nil {
 							xlog.Debug("Failed to marshal response", "error", err)
@@ -888,6 +900,9 @@ func ChatEndpoint(cl *config.ModelConfigLoader, ml *model.ModelLoader, evaluator
 					finishReason = FinishReasonFunctionCall
 				}
 
+				// Final delta chunk: empty delta with finish_reason set. Per
+				// OpenAI streaming spec this chunk does NOT carry usage —
+				// the optional trailer (below) does, gated on include_usage.
 				resp := &schema.OpenAIResponse{
 					ID:      id,
 					Created: created,
@@ -899,11 +914,18 @@ func ChatEndpoint(cl *config.ModelConfigLoader, ml *model.ModelLoader, evaluator
 							Delta:        &schema.Message{},
 						}},
 					Object: "chat.completion.chunk",
-					Usage:  *usage,
 				}
 				respData, _ := json.Marshal(resp)
-
 				fmt.Fprintf(c.Response().Writer, "data: %s\n\n", respData)
+
+				// Trailing usage chunk per OpenAI spec: emit only when the
+				// caller opted in via stream_options.include_usage. Shape:
+				// {"choices":[],"usage":{...},"object":"chat.completion.chunk",...}
+				if input.StreamOptions != nil && input.StreamOptions.IncludeUsage && usage != nil {
+					trailer := streamUsageTrailerJSON(id, input.Model, created, *usage)
+					_, _ = fmt.Fprintf(c.Response().Writer, "data: %s\n\n", trailer)
+				}
+
 				fmt.Fprintf(c.Response().Writer, "data: [DONE]\n\n")
 				c.Response().Flush()
 				xlog.Debug("Stream ended")
@@ -1263,7 +1285,7 @@ func ChatEndpoint(cl *config.ModelConfigLoader, ml *model.ModelLoader, evaluator
 					Model:   input.Model, // we have to return what the user sent here, due to OpenAI spec.
 					Choices: result,
 					Object:  "chat.completion",
-					Usage:   usage,
+					Usage:   &usage,
 				}
 				respData, _ := json.Marshal(resp)
 				xlog.Debug("Response", "response", string(respData))

core/http/endpoints/openai/chat_emit.go

@@ -1,12 +1,45 @@
 package openai
 
 import (
+	"encoding/json"
 	"fmt"
 
 	"github.com/mudler/LocalAI/core/schema"
 	"github.com/mudler/LocalAI/pkg/functions"
 )
 
+// streamUsageTrailerJSON returns the bytes of the OpenAI-spec trailing usage
+// chunk emitted in streaming completions when the request opts in via
+// `stream_options.include_usage: true`. The shape is:
+//
+//	{"id":"...","object":"chat.completion.chunk","created":N,
+//	 "model":"...","choices":[],"usage":{...}}
+//
+// `choices` is intentionally an empty array (not absent, not null) — that is
+// what the OpenAI spec mandates, and what consumers like the official OpenAI
+// SDK and Continue's openai-adapter look for to recognise this as the usage
+// chunk rather than a content chunk. schema.OpenAIResponse has `omitempty`
+// on Choices, so we cannot reuse it for the trailer.
+func streamUsageTrailerJSON(id, model string, created int, usage schema.OpenAIUsage) []byte {
+	trailer := struct {
+		ID      string             `json:"id"`
+		Created int                `json:"created"`
+		Model   string             `json:"model"`
+		Object  string             `json:"object"`
+		Choices []schema.Choice    `json:"choices"`
+		Usage   schema.OpenAIUsage `json:"usage"`
+	}{
+		ID:      id,
+		Created: created,
+		Model:   model,
+		Object:  "chat.completion.chunk",
+		Choices: []schema.Choice{},
+		Usage:   usage,
+	}
+	b, _ := json.Marshal(trailer)
+	return b
+}
+
 // hasRealCall reports whether functionResults contains at least one
 // entry whose Name is something other than the noAction sentinel.
 // Used by processTools to decide between the "answer the question"
@@ -25,10 +58,10 @@ func hasRealCall(functionResults []functions.FuncCallResults, noAction string) b
 // pseudo-function or emitted no tool calls at all).
 //
 // When content was already streamed (contentAlreadyStreamed=true) the
-// helper emits a single trailing usage chunk, optionally carrying
-// reasoning that was produced but not streamed incrementally. When
-// content was not streamed it emits a role chunk followed by a
-// content+reasoning+usage chunk — the "send everything at once" fallback.
+// helper emits a trailing reasoning chunk if any non-streamed reasoning
+// remains, else nothing. When content was not streamed it emits a role
+// chunk followed by a content (+reasoning) chunk — the "send everything
+// at once" fallback.
 //
 // Reasoning re-emission is guarded by reasoningAlreadyStreamed, not by
 // probing the extractor's Go-side state: the C++ autoparser delivers
@@ -36,6 +69,10 @@ func hasRealCall(functionResults []functions.FuncCallResults, noAction string) b
 // separate accumulator that extractor.Reasoning() does not expose.
 // Without this guard the callback would stream reasoning incrementally
 // and the final chunk would duplicate it.
+//
+// The returned chunks intentionally do NOT carry a `usage` field. The
+// usage trailer is emitted separately by the streaming handler when
+// `stream_options.include_usage` is true, per OpenAI spec.
 func buildNoActionFinalChunks(
 	id, model string,
 	created int,
@@ -43,26 +80,26 @@ func buildNoActionFinalChunks(
 	reasoningAlreadyStreamed bool,
 	content string,
 	reasoning string,
-	usage schema.OpenAIUsage,
 ) []schema.OpenAIResponse {
 	var out []schema.OpenAIResponse
 
 	if contentAlreadyStreamed {
-		delta := &schema.Message{}
-		if reasoning != "" && !reasoningAlreadyStreamed {
-			r := reasoning
-			delta.Reasoning = &r
+		if reasoning == "" || reasoningAlreadyStreamed {
+			return nil
 		}
+		r := reasoning
 		out = append(out, schema.OpenAIResponse{
 			ID: id, Created: created, Model: model,
-			Choices: []schema.Choice{{Delta: delta, Index: 0}},
-			Object:  "chat.completion.chunk",
-			Usage:   usage,
+			Choices: []schema.Choice{{
+				Delta: &schema.Message{Reasoning: &r},
+				Index: 0,
+			}},
+			Object: "chat.completion.chunk",
 		})
 		return out
 	}
 
-	// Content was not streamed — send role, then content (+reasoning) + usage.
+	// Content was not streamed — send role, then content (+reasoning).
 	out = append(out, schema.OpenAIResponse{
 		ID: id, Created: created, Model: model,
 		Choices: []schema.Choice{{
@@ -82,7 +119,6 @@ func buildNoActionFinalChunks(
 		ID: id, Created: created, Model: model,
 		Choices: []schema.Choice{{Delta: delta, Index: 0}},
 		Object:  "chat.completion.chunk",
-		Usage:   usage,
 	})
 	return out
 }

core/http/endpoints/openai/chat_emit_test.go

@@ -609,54 +609,52 @@ var _ = Describe("buildNoActionFinalChunks", func() {
 		testModel   = "test-model"
 		testCreated = 1700000000
 	)
-	usage := schema.OpenAIUsage{PromptTokens: 5, CompletionTokens: 7, TotalTokens: 12}
 
-	Describe("Content streamed — trailing usage chunk", func() {
-		It("emits just one chunk with usage, no content, no reasoning when reasoning was streamed", func() {
+	Describe("Content streamed — trailing reasoning only", func() {
+		It("emits nothing when content and reasoning were already streamed", func() {
+			// Before the streaming-usage-spec fix this branch emitted a
+			// content-less chunk solely to carry `usage`. Per the OpenAI
+			// spec usage no longer rides on delta chunks; the dedicated
+			// trailer (when include_usage=true) carries it instead — so
+			// with nothing to deliver the helper returns no chunks.
 			chunks := buildNoActionFinalChunks(
 				testID, testModel, testCreated,
 				true, true,
-				"", "already-streamed-reasoning", usage,
+				"", "already-streamed-reasoning",
 			)
-
-			Expect(chunks).To(HaveLen(1))
-			Expect(chunks[0].Usage.TotalTokens).To(Equal(12))
-			Expect(contentOf(chunks[0])).To(BeEmpty())
-			Expect(reasoningOf(chunks[0])).To(BeEmpty(),
-				"reasoning must not be re-emitted once it was streamed via the callback")
+			Expect(chunks).To(BeEmpty())
 		})
 
 		It("emits a trailing reasoning delivery when reasoning came only at end", func() {
 			chunks := buildNoActionFinalChunks(
 				testID, testModel, testCreated,
 				true, false,
-				"", "autoparser final reasoning", usage,
+				"", "autoparser final reasoning",
 			)
 
 			Expect(chunks).To(HaveLen(1))
 			Expect(reasoningOf(chunks[0])).To(Equal("autoparser final reasoning"))
 			Expect(contentOf(chunks[0])).To(BeEmpty())
-			Expect(chunks[0].Usage.TotalTokens).To(Equal(12))
+			Expect(chunks[0].Usage).To(BeNil(),
+				"intermediate chunks must not carry usage per OpenAI spec")
 		})
 
-		It("omits reasoning when it's empty regardless of streamed flag", func() {
+		It("returns no chunks when reasoning is empty and content was streamed", func() {
 			chunks := buildNoActionFinalChunks(
 				testID, testModel, testCreated,
 				true, false,
-				"", "", usage,
+				"", "",
 			)
-
-			Expect(chunks).To(HaveLen(1))
-			Expect(reasoningOf(chunks[0])).To(BeEmpty())
+			Expect(chunks).To(BeEmpty())
 		})
 	})
 
-	Describe("Content not streamed — role, then content+usage", func() {
+	Describe("Content not streamed — role, then content", func() {
 		It("emits role chunk then content chunk without reasoning when reasoning was streamed", func() {
 			chunks := buildNoActionFinalChunks(
 				testID, testModel, testCreated,
 				false, true,
-				"the answer", "already-streamed-reasoning", usage,
+				"the answer", "already-streamed-reasoning",
 			)
 
 			Expect(chunks).To(HaveLen(2))
@@ -666,14 +664,14 @@ var _ = Describe("buildNoActionFinalChunks", func() {
 			Expect(contentOf(chunks[1])).To(Equal("the answer"))
 			Expect(reasoningOf(chunks[1])).To(BeEmpty(),
 				"reasoning must not be re-emitted if it was streamed earlier")
-			Expect(chunks[1].Usage.TotalTokens).To(Equal(12))
+			Expect(chunks[1].Usage).To(BeNil())
 		})
 
 		It("emits role, then content+reasoning when reasoning was not streamed", func() {
 			chunks := buildNoActionFinalChunks(
 				testID, testModel, testCreated,
 				false, false,
-				"the answer", "autoparser final reasoning", usage,
+				"the answer", "autoparser final reasoning",
 			)
 
 			Expect(chunks).To(HaveLen(2))
@@ -681,14 +679,14 @@ var _ = Describe("buildNoActionFinalChunks", func() {
 
 			Expect(contentOf(chunks[1])).To(Equal("the answer"))
 			Expect(reasoningOf(chunks[1])).To(Equal("autoparser final reasoning"))
-			Expect(chunks[1].Usage.TotalTokens).To(Equal(12))
+			Expect(chunks[1].Usage).To(BeNil())
 		})
 
 		It("still emits content even when reasoning is empty", func() {
 			chunks := buildNoActionFinalChunks(
 				testID, testModel, testCreated,
 				false, false,
-				"just an answer", "", usage,
+				"just an answer", "",
 			)
 
 			Expect(chunks).To(HaveLen(2))
@@ -702,7 +700,7 @@ var _ = Describe("buildNoActionFinalChunks", func() {
 			chunks := buildNoActionFinalChunks(
 				testID, testModel, testCreated,
 				false, false,
-				"hi", "reasoning", usage,
+				"hi", "reasoning",
 			)
 			for i, ch := range chunks {
 				Expect(ch.ID).To(Equal(testID), "chunk[%d] ID", i)

core/http/endpoints/openai/chat_stream_usage_test.go

@@ -0,0 +1,179 @@
+package openai
+
+import (
+	"encoding/json"
+
+	"github.com/mudler/LocalAI/core/schema"
+	"github.com/mudler/LocalAI/pkg/functions"
+	. "github.com/onsi/ginkgo/v2"
+	. "github.com/onsi/gomega"
+)
+
+// These tests pin LocalAI's streaming chunks to the OpenAI spec for the
+// `usage` field. The regression that motivated them (issue #8546) was that
+// LocalAI emitted `"usage":{...zeros...}` on every chunk, which made the
+// official OpenAI Node SDK consumers (Continue, Kilo Code, Roo Code, Zed,
+// IntelliJ Continue) drop every content chunk via the filter at
+// continuedev/continue packages/openai-adapters/src/apis/OpenAI.ts:275-288.
+//
+// Per OpenAI's chat-completion streaming contract:
+//   - intermediate chunks MUST NOT carry a `usage` field
+//   - usage is only delivered when the request opts in via
+//     `stream_options.include_usage: true`, on a final extra chunk whose
+//     `choices` is an empty array.
+
+var _ = Describe("streaming usage spec compliance", func() {
+	Describe("OpenAIResponse JSON shape", func() {
+		It("does not emit a 'usage' key when Usage is unset", func() {
+			// A typical intermediate token chunk: no Usage populated.
+			content := "hello"
+			resp := schema.OpenAIResponse{
+				ID:      "req-1",
+				Created: 1,
+				Model:   "m",
+				Object:  "chat.completion.chunk",
+				Choices: []schema.Choice{{
+					Index: 0,
+					Delta: &schema.Message{Content: &content},
+				}},
+			}
+			data, err := json.Marshal(resp)
+			Expect(err).ToNot(HaveOccurred())
+
+			var raw map[string]any
+			Expect(json.Unmarshal(data, &raw)).To(Succeed())
+			_, present := raw["usage"]
+			Expect(present).To(BeFalse(),
+				"intermediate chunk must not include a 'usage' key; got: %s", string(data))
+		})
+
+		It("emits the usage object when Usage is explicitly set", func() {
+			usage := &schema.OpenAIUsage{PromptTokens: 11, CompletionTokens: 22, TotalTokens: 33}
+			resp := schema.OpenAIResponse{
+				ID:      "req-1",
+				Created: 1,
+				Model:   "m",
+				Object:  "chat.completion.chunk",
+				Usage:   usage,
+			}
+			data, err := json.Marshal(resp)
+			Expect(err).ToNot(HaveOccurred())
+
+			var raw map[string]any
+			Expect(json.Unmarshal(data, &raw)).To(Succeed())
+			u, ok := raw["usage"].(map[string]any)
+			Expect(ok).To(BeTrue(), "expected 'usage' object, got: %s", string(data))
+			Expect(u["prompt_tokens"]).To(BeNumerically("==", 11))
+			Expect(u["completion_tokens"]).To(BeNumerically("==", 22))
+			Expect(u["total_tokens"]).To(BeNumerically("==", 33))
+		})
+	})
+
+	Describe("buildNoActionFinalChunks", func() {
+		It("returns chunks with no Usage embedded", func() {
+			// Whatever the caller is doing, helpers must not bake usage
+			// into intermediate or final delta chunks. The usage trailer
+			// (when requested via include_usage) is emitted separately.
+			chunks := buildNoActionFinalChunks(
+				"req-1", "m", 1,
+				false, false,
+				"hi", "",
+			)
+			Expect(chunks).ToNot(BeEmpty())
+			for i, ch := range chunks {
+				Expect(ch.Usage).To(BeNil(),
+					"chunk[%d] must not carry Usage; got %+v", i, ch.Usage)
+			}
+		})
+
+		It("returns chunks with no Usage when only trailing reasoning needs delivery", func() {
+			chunks := buildNoActionFinalChunks(
+				"req-1", "m", 1,
+				true, false,
+				"", "autoparser late reasoning",
+			)
+			Expect(chunks).ToNot(BeEmpty())
+			for i, ch := range chunks {
+				Expect(ch.Usage).To(BeNil(),
+					"chunk[%d] must not carry Usage; got %+v", i, ch.Usage)
+			}
+		})
+	})
+
+	Describe("buildDeferredToolCallChunks", func() {
+		It("returns chunks with no Usage embedded", func() {
+			calls := []functions.FuncCallResults{{
+				Name: "do_thing", Arguments: `{"x":1}`,
+			}}
+			chunks := buildDeferredToolCallChunks(
+				"req-1", "m", 1, calls, 0,
+				false, "", false, "",
+			)
+			Expect(chunks).ToNot(BeEmpty())
+			for i, ch := range chunks {
+				Expect(ch.Usage).To(BeNil(),
+					"chunk[%d] must not carry Usage; got %+v", i, ch.Usage)
+			}
+		})
+	})
+
+	Describe("streamUsageTrailerJSON", func() {
+		It("produces JSON matching the OpenAI spec for the trailer chunk", func() {
+			// Trailing usage chunk shape (OpenAI streaming spec):
+			//   {"id":"...","object":"chat.completion.chunk","created":...,
+			//    "model":"...","choices":[],"usage":{...}}
+			usage := schema.OpenAIUsage{
+				PromptTokens: 18, CompletionTokens: 14, TotalTokens: 32,
+			}
+			data := streamUsageTrailerJSON("req-1", "m", 1, usage)
+
+			var raw map[string]any
+			Expect(json.Unmarshal(data, &raw)).To(Succeed(),
+				"trailer must be valid JSON, got: %s", string(data))
+
+			Expect(raw["id"]).To(Equal("req-1"))
+			Expect(raw["model"]).To(Equal("m"))
+			Expect(raw["object"]).To(Equal("chat.completion.chunk"))
+			Expect(raw["created"]).To(BeNumerically("==", 1))
+
+			// `choices` MUST be present as an empty array (not absent, not null).
+			rawChoices, present := raw["choices"]
+			Expect(present).To(BeTrue(), "choices key must be present, got: %s", string(data))
+			choicesArr, ok := rawChoices.([]any)
+			Expect(ok).To(BeTrue(), "choices must serialize as an array, got: %s", string(data))
+			Expect(choicesArr).To(BeEmpty(), "choices must be empty in usage trailer, got: %s", string(data))
+
+			// `usage` MUST be present and non-null with the populated counts.
+			u, ok := raw["usage"].(map[string]any)
+			Expect(ok).To(BeTrue(), "usage object must be present, got: %s", string(data))
+			Expect(u["prompt_tokens"]).To(BeNumerically("==", 18))
+			Expect(u["completion_tokens"]).To(BeNumerically("==", 14))
+			Expect(u["total_tokens"]).To(BeNumerically("==", 32))
+		})
+	})
+
+	Describe("OpenAIRequest.StreamOptions", func() {
+		It("parses stream_options.include_usage=true", func() {
+			body := []byte(`{
+                "model": "m",
+                "stream": true,
+                "stream_options": {"include_usage": true},
+                "messages": []
+            }`)
+			var req schema.OpenAIRequest
+			Expect(json.Unmarshal(body, &req)).To(Succeed())
+			Expect(req.StreamOptions).ToNot(BeNil())
+			Expect(req.StreamOptions.IncludeUsage).To(BeTrue())
+		})
+
+		It("defaults IncludeUsage to false when stream_options is absent", func() {
+			body := []byte(`{"model":"m","stream":true,"messages":[]}`)
+			var req schema.OpenAIRequest
+			Expect(json.Unmarshal(body, &req)).To(Succeed())
+			// Either a nil StreamOptions or one with IncludeUsage=false is acceptable.
+			if req.StreamOptions != nil {
+				Expect(req.StreamOptions.IncludeUsage).To(BeFalse())
+			}
+		})
+	})
+})

core/http/endpoints/openai/completion.go

@@ -39,6 +39,10 @@ func CompletionEndpoint(cl *config.ModelConfigLoader, ml *model.ModelLoader, eva
 				usage.TimingTokenGeneration = tokenUsage.TimingTokenGeneration
 				usage.TimingPromptProcessing = tokenUsage.TimingPromptProcessing
 			}
+			// Usage rides on the struct for the consumer to track the
+			// running cumulative; the consumer strips it before marshalling
+			// so intermediate chunks stay OpenAI-spec compliant.
+			usageForChunk := usage
 			resp := schema.OpenAIResponse{
 				ID:      id,
 				Created: created,
@@ -51,7 +55,7 @@ func CompletionEndpoint(cl *config.ModelConfigLoader, ml *model.ModelLoader, eva
 					},
 				},
 				Object: "text_completion",
-				Usage:  usage,
+				Usage:  &usageForChunk,
 			}
 			xlog.Debug("Sending goroutine", "text", s)
 
@@ -127,6 +131,8 @@ func CompletionEndpoint(cl *config.ModelConfigLoader, ml *model.ModelLoader, eva
 				ended <- process(id, predInput, input, config, ml, responses, extraUsage)
 			}()
 
+			var latestUsage *schema.OpenAIUsage
+
 		LOOP:
 			for {
 				select {
@@ -135,6 +141,14 @@ func CompletionEndpoint(cl *config.ModelConfigLoader, ml *model.ModelLoader, eva
 						xlog.Debug("No choices in the response, skipping")
 						continue
 					}
+					// Capture running cumulative usage for the optional trailer
+					// emitted after the final stop chunk when include_usage=true.
+					if ev.Usage != nil {
+						latestUsage = ev.Usage
+					}
+					// OpenAI streaming spec: intermediate chunks must NOT
+					// carry a `usage` field. Strip the tracking copy now.
+					ev.Usage = nil
 					respData, err := json.Marshal(ev)
 					if err != nil {
 						xlog.Debug("Failed to marshal response", "error", err)
@@ -194,8 +208,15 @@ func CompletionEndpoint(cl *config.ModelConfigLoader, ml *model.ModelLoader, eva
 				Object: "text_completion",
 			}
 			respData, _ := json.Marshal(resp)
-
 			fmt.Fprintf(c.Response().Writer, "data: %s\n\n", respData)
+
+			// Trailing usage chunk per OpenAI spec: emit only when the caller
+			// opted in via stream_options.include_usage.
+			if input.StreamOptions != nil && input.StreamOptions.IncludeUsage && latestUsage != nil {
+				trailer := streamUsageTrailerJSON(id, input.Model, created, *latestUsage)
+				_, _ = fmt.Fprintf(c.Response().Writer, "data: %s\n\n", trailer)
+			}
+
 			fmt.Fprintf(c.Response().Writer, "data: [DONE]\n\n")
 			c.Response().Flush()
 			return nil
@@ -247,7 +268,7 @@ func CompletionEndpoint(cl *config.ModelConfigLoader, ml *model.ModelLoader, eva
 			Model:   input.Model, // we have to return what the user sent here, due to OpenAI spec.
 			Choices: result,
 			Object:  "text_completion",
-			Usage:   usage,
+			Usage:   &usage,
 		}
 
 		jsonResult, _ := json.Marshal(resp)

core/http/endpoints/openai/edit.go

@@ -92,7 +92,7 @@ func EditEndpoint(cl *config.ModelConfigLoader, ml *model.ModelLoader, evaluator
 			Model:   input.Model, // we have to return what the user sent here, due to OpenAI spec.
 			Choices: result,
 			Object:  "edit",
-			Usage:   usage,
+			Usage:   &usage,
 		}
 
 		jsonResult, _ := json.Marshal(resp)

core/http/endpoints/openai/image.go

@@ -233,7 +233,7 @@ func ImageEndpoint(cl *config.ModelConfigLoader, ml *model.ModelLoader, appConfi
 			ID:      id,
 			Created: created,
 			Data:    result,
-			Usage: schema.OpenAIUsage{
+			Usage: &schema.OpenAIUsage{
 				PromptTokens:     0,
 				CompletionTokens: 0,
 				TotalTokens:      0,

core/http/endpoints/openai/inpainting.go

@@ -258,7 +258,7 @@ func InpaintingEndpoint(cl *config.ModelConfigLoader, ml *model.ModelLoader, app
 			Data: []schema.Item{{
 				URL: imgPath,
 			}},
-			Usage: schema.OpenAIUsage{
+			Usage: &schema.OpenAIUsage{
 				PromptTokens:     0,
 				CompletionTokens: 0,
 				TotalTokens:      0,

core/http/endpoints/openai/realtime.go

@@ -54,6 +54,30 @@ const (
 		"Avoid parenthetical asides, URLs, and anything that cannot be clearly vocalized."
 )
 
+// resolveOutputModalities returns the effective output modalities for a
+// response: response-level overrides session-level, and the OpenAI Realtime
+// spec default is ["audio"] when neither is set.
+func resolveOutputModalities(session, response []types.Modality) []types.Modality {
+	if len(response) > 0 {
+		return response
+	}
+	if len(session) > 0 {
+		return session
+	}
+	return []types.Modality{types.ModalityAudio}
+}
+
+// modalitiesContainAudio reports whether the resolved modalities include audio
+// output.
+func modalitiesContainAudio(m []types.Modality) bool {
+	for _, x := range m {
+		if x == types.ModalityAudio {
+			return true
+		}
+	}
+	return false
+}
+
 // A model can be "emulated" that is: transcribe audio to text -> feed text to the LLM -> generate audio as result
 // If the model support instead audio-to-audio, we will use the specific gRPC calls instead
 
@@ -82,6 +106,10 @@ type Session struct {
 	InputSampleRate  int
 	OutputSampleRate int
 	MaxOutputTokens  types.IntOrInf
+	// OutputModalities mirrors the OpenAI Realtime spec field of the same
+	// name. Empty means "use the spec default" (audio). ["text"] suppresses
+	// TTS so the client receives only response.output_text.* events.
+	OutputModalities []types.Modality
 	// MaxHistoryItems caps the number of MessageItems passed to the LLM each
 	// turn (0 = unlimited). Small models — especially the LFM2.5-Audio 1.5B
 	// served via the liquid-audio backend — degrade quickly past a handful
@@ -162,13 +190,14 @@ func (s *Session) ToServer() types.SessionUnion {
 	} else {
 		return types.SessionUnion{
 			Realtime: &types.RealtimeSession{
-				ID:              s.ID,
-				Object:          "realtime.session",
-				Model:           s.Model,
-				Instructions:    s.Instructions,
-				Tools:           s.Tools,
-				ToolChoice:      s.ToolChoice,
-				MaxOutputTokens: s.MaxOutputTokens,
+				ID:               s.ID,
+				Object:           "realtime.session",
+				Model:            s.Model,
+				Instructions:     s.Instructions,
+				Tools:            s.Tools,
+				ToolChoice:       s.ToolChoice,
+				MaxOutputTokens:  s.MaxOutputTokens,
+				OutputModalities: s.OutputModalities,
 				Audio: &types.RealtimeSessionAudio{
 					Input: &types.SessionAudioInput{
 						TurnDetection: s.TurnDetection,
@@ -1015,6 +1044,10 @@ func updateSession(session *Session, update *types.SessionUnion, cl *config.Mode
 		session.MaxOutputTokens = rt.MaxOutputTokens
 	}
 
+	if len(rt.OutputModalities) > 0 {
+		session.OutputModalities = rt.OutputModalities
+	}
+
 	return nil
 }
 
@@ -1654,106 +1687,130 @@ func triggerResponseAtTurn(ctx context.Context, session *Session, conv *Conversa
 			})
 		}
 
-		// Check for cancellation before TTS
-		if ctx.Err() != nil {
-			xlog.Debug("Response cancelled before TTS (barge-in)")
-			sendCancelledResponse()
-			return
-		}
-
-		audioFilePath, res, err := session.ModelInterface.TTS(ctx, finalSpeech, session.Voice, session.InputAudioTranscription.Language)
-		if err != nil {
-			if ctx.Err() != nil {
-				xlog.Debug("TTS cancelled (barge-in)")
-				sendCancelledResponse()
-				return
-			}
-			xlog.Error("TTS failed", "error", err)
-			sendError(t, "tts_error", fmt.Sprintf("TTS generation failed: %v", err), "", item.Assistant.ID)
-			return
-		}
-		if !res.Success {
-			xlog.Error("TTS failed", "message", res.Message)
-			sendError(t, "tts_error", fmt.Sprintf("TTS generation failed: %s", res.Message), "", item.Assistant.ID)
-			return
-		}
-		defer os.Remove(audioFilePath)
-
-		audioBytes, err := os.ReadFile(audioFilePath)
-		if err != nil {
-			xlog.Error("failed to read TTS file", "error", err)
-			sendError(t, "tts_error", fmt.Sprintf("Failed to read TTS audio: %v", err), "", item.Assistant.ID)
-			return
-		}
-
-		// Parse WAV header to get raw PCM and the actual sample rate from the TTS backend.
-		pcmData, ttsSampleRate := laudio.ParseWAV(audioBytes)
-		if ttsSampleRate == 0 {
-			ttsSampleRate = localSampleRate
-		}
-		xlog.Debug("TTS audio parsed", "raw_bytes", len(audioBytes), "pcm_bytes", len(pcmData), "sample_rate", ttsSampleRate)
-
-		// SendAudio (WebRTC) passes PCM at the TTS sample rate directly to the
-		// Opus encoder, which resamples to 48kHz internally. This avoids a
-		// lossy intermediate resample through 16kHz.
-		// XXX: This is a noop in websocket mode; it's included in the JSON instead
-		if err := t.SendAudio(ctx, pcmData, ttsSampleRate); err != nil {
-			if ctx.Err() != nil {
-				xlog.Debug("Audio playback cancelled (barge-in)")
-				sendCancelledResponse()
-				return
-			}
-			xlog.Error("failed to send audio via transport", "error", err)
-		}
-
-		_, isWebRTC := t.(*WebRTCTransport)
-
-		// For WebSocket clients, resample to the session's output rate and
-		// deliver audio as base64 in JSON events. WebRTC clients already
-		// received audio over the RTP track, so skip the base64 payload.
 		var audioString string
-		if !isWebRTC {
-			wsPCM := pcmData
-			if ttsSampleRate != session.OutputSampleRate {
-				samples := sound.BytesToInt16sLE(pcmData)
-				resampled := sound.ResampleInt16(samples, ttsSampleRate, session.OutputSampleRate)
-				wsPCM = sound.Int16toBytesLE(resampled)
-			}
-			audioString = base64.StdEncoding.EncodeToString(wsPCM)
+		_, isWebRTC := t.(*WebRTCTransport)
+		var respMods []types.Modality
+		if overrides != nil {
+			respMods = overrides.OutputModalities
 		}
+		modalities := resolveOutputModalities(session.OutputModalities, respMods)
+		if modalitiesContainAudio(modalities) {
+			// Check for cancellation before TTS
+			if ctx.Err() != nil {
+				xlog.Debug("Response cancelled before TTS (barge-in)")
+				sendCancelledResponse()
+				return
+			}
 
-		sendEvent(t, types.ResponseOutputAudioTranscriptDeltaEvent{
-			ServerEventBase: types.ServerEventBase{},
-			ResponseID:      responseID,
-			ItemID:          item.Assistant.ID,
-			OutputIndex:     0,
-			ContentIndex:    0,
-			Delta:           finalSpeech,
-		})
-		sendEvent(t, types.ResponseOutputAudioTranscriptDoneEvent{
-			ServerEventBase: types.ServerEventBase{},
-			ResponseID:      responseID,
-			ItemID:          item.Assistant.ID,
-			OutputIndex:     0,
-			ContentIndex:    0,
-			Transcript:      finalSpeech,
-		})
+			audioFilePath, res, err := session.ModelInterface.TTS(ctx, finalSpeech, session.Voice, session.InputAudioTranscription.Language)
+			if err != nil {
+				if ctx.Err() != nil {
+					xlog.Debug("TTS cancelled (barge-in)")
+					sendCancelledResponse()
+					return
+				}
+				xlog.Error("TTS failed", "error", err)
+				sendError(t, "tts_error", fmt.Sprintf("TTS generation failed: %v", err), "", item.Assistant.ID)
+				return
+			}
+			if !res.Success {
+				xlog.Error("TTS failed", "message", res.Message)
+				sendError(t, "tts_error", fmt.Sprintf("TTS generation failed: %s", res.Message), "", item.Assistant.ID)
+				return
+			}
+			defer func() { _ = os.Remove(audioFilePath) }()
 
-		if !isWebRTC {
-			sendEvent(t, types.ResponseOutputAudioDeltaEvent{
+			audioBytes, err := os.ReadFile(audioFilePath)
+			if err != nil {
+				xlog.Error("failed to read TTS file", "error", err)
+				sendError(t, "tts_error", fmt.Sprintf("Failed to read TTS audio: %v", err), "", item.Assistant.ID)
+				return
+			}
+
+			// Parse WAV header to get raw PCM and the actual sample rate from the TTS backend.
+			pcmData, ttsSampleRate := laudio.ParseWAV(audioBytes)
+			if ttsSampleRate == 0 {
+				ttsSampleRate = localSampleRate
+			}
+			xlog.Debug("TTS audio parsed", "raw_bytes", len(audioBytes), "pcm_bytes", len(pcmData), "sample_rate", ttsSampleRate)
+
+			// SendAudio (WebRTC) passes PCM at the TTS sample rate directly to the
+			// Opus encoder, which resamples to 48kHz internally. This avoids a
+			// lossy intermediate resample through 16kHz.
+			// XXX: This is a noop in websocket mode; it's included in the JSON instead
+			if err := t.SendAudio(ctx, pcmData, ttsSampleRate); err != nil {
+				if ctx.Err() != nil {
+					xlog.Debug("Audio playback cancelled (barge-in)")
+					sendCancelledResponse()
+					return
+				}
+				xlog.Error("failed to send audio via transport", "error", err)
+			}
+
+			// For WebSocket clients, resample to the session's output rate and
+			// deliver audio as base64 in JSON events. WebRTC clients already
+			// received audio over the RTP track, so skip the base64 payload.
+			if !isWebRTC {
+				wsPCM := pcmData
+				if ttsSampleRate != session.OutputSampleRate {
+					samples := sound.BytesToInt16sLE(pcmData)
+					resampled := sound.ResampleInt16(samples, ttsSampleRate, session.OutputSampleRate)
+					wsPCM = sound.Int16toBytesLE(resampled)
+				}
+				audioString = base64.StdEncoding.EncodeToString(wsPCM)
+			}
+
+			sendEvent(t, types.ResponseOutputAudioTranscriptDeltaEvent{
 				ServerEventBase: types.ServerEventBase{},
 				ResponseID:      responseID,
 				ItemID:          item.Assistant.ID,
 				OutputIndex:     0,
 				ContentIndex:    0,
-				Delta:           audioString,
+				Delta:           finalSpeech,
 			})
-			sendEvent(t, types.ResponseOutputAudioDoneEvent{
+			sendEvent(t, types.ResponseOutputAudioTranscriptDoneEvent{
 				ServerEventBase: types.ServerEventBase{},
 				ResponseID:      responseID,
 				ItemID:          item.Assistant.ID,
 				OutputIndex:     0,
 				ContentIndex:    0,
+				Transcript:      finalSpeech,
+			})
+
+			if !isWebRTC {
+				sendEvent(t, types.ResponseOutputAudioDeltaEvent{
+					ServerEventBase: types.ServerEventBase{},
+					ResponseID:      responseID,
+					ItemID:          item.Assistant.ID,
+					OutputIndex:     0,
+					ContentIndex:    0,
+					Delta:           audioString,
+				})
+				sendEvent(t, types.ResponseOutputAudioDoneEvent{
+					ServerEventBase: types.ServerEventBase{},
+					ResponseID:      responseID,
+					ItemID:          item.Assistant.ID,
+					OutputIndex:     0,
+					ContentIndex:    0,
+				})
+			}
+		} else {
+			// Text-only mode: skip TTS, emit only the text events.
+			sendEvent(t, types.ResponseOutputTextDeltaEvent{
+				ServerEventBase: types.ServerEventBase{},
+				ResponseID:      responseID,
+				ItemID:          item.Assistant.ID,
+				OutputIndex:     0,
+				ContentIndex:    0,
+				Delta:           finalSpeech,
+			})
+			sendEvent(t, types.ResponseOutputTextDoneEvent{
+				ServerEventBase: types.ServerEventBase{},
+				ResponseID:      responseID,
+				ItemID:          item.Assistant.ID,
+				OutputIndex:     0,
+				ContentIndex:    0,
+				Text:            finalSpeech,
 			})
 		}
 

core/http/endpoints/openai/realtime_modality_test.go

@@ -0,0 +1,39 @@
+package openai
+
+import (
+	"github.com/mudler/LocalAI/core/http/endpoints/openai/types"
+	. "github.com/onsi/ginkgo/v2"
+	. "github.com/onsi/gomega"
+)
+
+var _ = Describe("resolveOutputModalities", func() {
+	It("defaults to audio when neither session nor response specify", func() {
+		got := resolveOutputModalities(nil, nil)
+		Expect(got).To(ConsistOf(types.ModalityAudio))
+	})
+
+	It("uses session modalities when response omits them", func() {
+		sess := []types.Modality{types.ModalityText}
+		got := resolveOutputModalities(sess, nil)
+		Expect(got).To(ConsistOf(types.ModalityText))
+	})
+
+	It("response modalities override session", func() {
+		sess := []types.Modality{types.ModalityAudio}
+		resp := []types.Modality{types.ModalityText}
+		got := resolveOutputModalities(sess, resp)
+		Expect(got).To(ConsistOf(types.ModalityText))
+	})
+
+	It("returns false from modalitiesContainAudio for text-only", func() {
+		Expect(modalitiesContainAudio([]types.Modality{types.ModalityText})).To(BeFalse())
+	})
+
+	It("returns true from modalitiesContainAudio for audio (default)", func() {
+		Expect(modalitiesContainAudio([]types.Modality{types.ModalityAudio})).To(BeTrue())
+	})
+
+	It("returns true when both audio and text are present", func() {
+		Expect(modalitiesContainAudio([]types.Modality{types.ModalityText, types.ModalityAudio})).To(BeTrue())
+	})
+})

core/http/react-ui/src/hooks/useChat.js

@@ -255,7 +255,10 @@ export function useChat(initialModel = '') {
     )
     messages.push(...historyForApi, { role: 'user', content: messageContent })
 
-    const requestBody = { model, messages, stream: true }
+    // include_usage tells LocalAI to emit a trailing chunk with token totals;
+    // without it the spec-compliant server drops `usage` from the stream and
+    // the token-count badge would never populate.
+    const requestBody = { model, messages, stream: true, stream_options: { include_usage: true } }
     if (temperature !== null && temperature !== undefined) requestBody.temperature = temperature
     if (topP !== null && topP !== undefined) requestBody.top_p = topP
     if (topK !== null && topK !== undefined) requestBody.top_k = topK

core/http/static/chat.js

@@ -1212,6 +1212,9 @@ async function promptGPT(systemPrompt, input) {
 
   // Add stream parameter for both regular chat and MCP (MCP now supports SSE streaming)
   requestBody.stream = true;
+  // include_usage tells LocalAI to emit a trailing chunk with token totals;
+  // the spec-compliant server otherwise drops `usage` from the stream.
+  requestBody.stream_options = { include_usage: true };
   
   // Add generation parameters if they are set (null means use default)
   if (activeChat.temperature !== null && activeChat.temperature !== undefined) {

core/schema/ollama.go

@@ -2,6 +2,8 @@ package schema
 
 import (
 	"context"
+	"encoding/json"
+	"fmt"
 	"time"
 )
 
@@ -18,6 +20,79 @@ type OllamaOptions struct {
 	NumCtx        int      `json:"num_ctx,omitempty"`
 }
 
+// UnmarshalJSON accepts integer parameters encoded as either JSON ints
+// (`8192`) or JSON floats (`8192.0`). Some clients - notably Home Assistant's
+// Ollama integration - serialize ints as floats, which stdlib json refuses
+// to decode into int fields. See https://github.com/mudler/LocalAI/issues/9837.
+func (o *OllamaOptions) UnmarshalJSON(data []byte) error {
+	type aux struct {
+		Temperature   *float64     `json:"temperature,omitempty"`
+		TopP          *float64     `json:"top_p,omitempty"`
+		TopK          *json.Number `json:"top_k,omitempty"`
+		NumPredict    *json.Number `json:"num_predict,omitempty"`
+		RepeatPenalty float64      `json:"repeat_penalty,omitempty"`
+		RepeatLastN   *json.Number `json:"repeat_last_n,omitempty"`
+		Seed          *json.Number `json:"seed,omitempty"`
+		Stop          []string     `json:"stop,omitempty"`
+		NumCtx        *json.Number `json:"num_ctx,omitempty"`
+	}
+	var a aux
+	if err := json.Unmarshal(data, &a); err != nil {
+		return err
+	}
+
+	o.Temperature = a.Temperature
+	o.TopP = a.TopP
+	o.RepeatPenalty = a.RepeatPenalty
+	o.Stop = a.Stop
+
+	var err error
+	if o.TopK, err = jsonNumberToIntPtr(a.TopK); err != nil {
+		return fmt.Errorf("options.top_k: %w", err)
+	}
+	if o.NumPredict, err = jsonNumberToIntPtr(a.NumPredict); err != nil {
+		return fmt.Errorf("options.num_predict: %w", err)
+	}
+	if o.Seed, err = jsonNumberToIntPtr(a.Seed); err != nil {
+		return fmt.Errorf("options.seed: %w", err)
+	}
+	if o.RepeatLastN, err = jsonNumberToInt(a.RepeatLastN); err != nil {
+		return fmt.Errorf("options.repeat_last_n: %w", err)
+	}
+	if o.NumCtx, err = jsonNumberToInt(a.NumCtx); err != nil {
+		return fmt.Errorf("options.num_ctx: %w", err)
+	}
+	return nil
+}
+
+// jsonNumberToInt parses a json.Number literal as an int, tolerating both
+// integer (`8192`) and float (`8192.0`) encodings. A nil pointer or empty
+// string yields 0, matching the zero-value semantics of the int fields.
+func jsonNumberToInt(n *json.Number) (int, error) {
+	if n == nil || *n == "" {
+		return 0, nil
+	}
+	if i, err := n.Int64(); err == nil {
+		return int(i), nil
+	}
+	f, err := n.Float64()
+	if err != nil {
+		return 0, err
+	}
+	return int(f), nil
+}
+
+func jsonNumberToIntPtr(n *json.Number) (*int, error) {
+	if n == nil {
+		return nil, nil
+	}
+	i, err := jsonNumberToInt(n)
+	if err != nil {
+		return nil, err
+	}
+	return &i, nil
+}
+
 // OllamaMessage represents a message in Ollama chat format
 type OllamaMessage struct {
 	Role      string   `json:"role"`

core/schema/ollama_test.go

@@ -84,3 +84,94 @@ var _ = Describe("OllamaEmbedRequest", func() {
 		})
 	})
 })
+
+// Several Ollama clients (notably Home Assistant's Python client) encode
+// integer parameters as JSON floats (`8192.0`). Stdlib json refuses to
+// unmarshal those into `int` fields, so OllamaOptions has a custom
+// UnmarshalJSON that accepts both forms. See
+// https://github.com/mudler/LocalAI/issues/9837.
+var _ = Describe("OllamaOptions JSON unmarshaling", func() {
+	It("accepts integer literals for int fields", func() {
+		body := []byte(`{"num_ctx": 8192, "num_predict": 256, "top_k": 40, "seed": 7, "repeat_last_n": 64}`)
+
+		var opts OllamaOptions
+		Expect(json.Unmarshal(body, &opts)).To(Succeed())
+
+		Expect(opts.NumCtx).To(Equal(8192))
+		Expect(opts.NumPredict).NotTo(BeNil())
+		Expect(*opts.NumPredict).To(Equal(256))
+		Expect(opts.TopK).NotTo(BeNil())
+		Expect(*opts.TopK).To(Equal(40))
+		Expect(opts.Seed).NotTo(BeNil())
+		Expect(*opts.Seed).To(Equal(7))
+		Expect(opts.RepeatLastN).To(Equal(64))
+	})
+
+	It("accepts float literals for int fields (Home Assistant Ollama client)", func() {
+		body := []byte(`{"num_ctx": 8192.0, "num_predict": 256.0, "top_k": 40.0, "seed": 7.0, "repeat_last_n": 64.0}`)
+
+		var opts OllamaOptions
+		Expect(json.Unmarshal(body, &opts)).To(Succeed())
+
+		Expect(opts.NumCtx).To(Equal(8192))
+		Expect(opts.NumPredict).NotTo(BeNil())
+		Expect(*opts.NumPredict).To(Equal(256))
+		Expect(opts.TopK).NotTo(BeNil())
+		Expect(*opts.TopK).To(Equal(40))
+		Expect(opts.Seed).NotTo(BeNil())
+		Expect(*opts.Seed).To(Equal(7))
+		Expect(opts.RepeatLastN).To(Equal(64))
+	})
+
+	It("preserves float fields and stop list", func() {
+		body := []byte(`{"temperature": 0.7, "top_p": 0.9, "repeat_penalty": 1.1, "stop": ["<|end|>", "</s>"]}`)
+
+		var opts OllamaOptions
+		Expect(json.Unmarshal(body, &opts)).To(Succeed())
+
+		Expect(opts.Temperature).NotTo(BeNil())
+		Expect(*opts.Temperature).To(Equal(0.7))
+		Expect(opts.TopP).NotTo(BeNil())
+		Expect(*opts.TopP).To(Equal(0.9))
+		Expect(opts.RepeatPenalty).To(Equal(1.1))
+		Expect(opts.Stop).To(Equal([]string{"<|end|>", "</s>"}))
+	})
+
+	It("leaves optional int fields nil when absent", func() {
+		body := []byte(`{}`)
+
+		var opts OllamaOptions
+		Expect(json.Unmarshal(body, &opts)).To(Succeed())
+
+		Expect(opts.NumPredict).To(BeNil())
+		Expect(opts.TopK).To(BeNil())
+		Expect(opts.Seed).To(BeNil())
+		Expect(opts.NumCtx).To(Equal(0))
+		Expect(opts.RepeatLastN).To(Equal(0))
+	})
+
+	It("accepts nested options on a chat request with float num_ctx", func() {
+		// Mirrors the payload Home Assistant sends; reproduces issue #9837.
+		body := []byte(`{
+			"model": "qwen2",
+			"messages": [{"role": "user", "content": "hi"}],
+			"options": {"num_ctx": 8192.0, "top_k": 40.0}
+		}`)
+
+		var req OllamaChatRequest
+		Expect(json.Unmarshal(body, &req)).To(Succeed())
+
+		Expect(req.Options).NotTo(BeNil())
+		Expect(req.Options.NumCtx).To(Equal(8192))
+		Expect(req.Options.TopK).NotTo(BeNil())
+		Expect(*req.Options.TopK).To(Equal(40))
+	})
+
+	It("rejects non-numeric values with a clear error", func() {
+		body := []byte(`{"num_ctx": "not-a-number"}`)
+
+		var opts OllamaOptions
+		err := json.Unmarshal(body, &opts)
+		Expect(err).To(HaveOccurred())
+	})
+})

core/schema/openai.go

@@ -82,7 +82,21 @@ type OpenAIResponse struct {
 	Choices []Choice `json:"choices,omitempty"`
 	Data    []Item   `json:"data,omitempty"`
 
-	Usage OpenAIUsage `json:"usage"`
+	// Usage is intentionally a pointer with omitempty: per the OpenAI
+	// chat-completion streaming spec, intermediate chunks must not carry
+	// a `usage` field. Marshalling a value-typed usage would emit
+	// `"usage":{"prompt_tokens":0,...}` on every chunk and break
+	// OpenAI-SDK consumers that filter on a truthy `result.usage`
+	// (continuedev/continue, Kilo Code, Roo Code, etc.).
+	Usage *OpenAIUsage `json:"usage,omitempty"`
+}
+
+// StreamOptions mirrors OpenAI's `stream_options` request field. The only
+// member currently honored is IncludeUsage; when true, the streaming
+// chat-completion response emits a trailing chunk with `choices:[]` and a
+// populated `usage` object.
+type StreamOptions struct {
+	IncludeUsage bool `json:"include_usage,omitempty" yaml:"include_usage,omitempty"`
 }
 
 type Choice struct {
@@ -198,6 +212,9 @@ type OpenAIRequest struct {
 
 	Stream bool `json:"stream"`
 
+	// StreamOptions opts into OpenAI streaming extensions, e.g. include_usage.
+	StreamOptions *StreamOptions `json:"stream_options,omitempty" yaml:"stream_options,omitempty"`
+
 	// Image (not supported by OpenAI)
 	Quality string `json:"quality"`
 	Step    int    `json:"step"`

core/services/galleryop/list_models.go

@@ -16,6 +16,14 @@ const (
 
 func ListModels(bcl *config.ModelConfigLoader, ml *model.ModelLoader, filter config.ModelConfigFilterFn, looseFilePolicy LooseFilePolicy) ([]string, error) {
 
+	// Callers (e.g. the Ollama /api/tags handler) pass nil to mean "no
+	// filtering". Without this guard the loose-file loop below dereferences
+	// filter and panics, which Echo surfaces to clients as a dropped
+	// connection (see issue #9817).
+	if filter == nil {
+		filter = config.NoFilterFn
+	}
+
 	skipMap := map[string]struct{}{}
 
 	dataModels := []string{}

core/services/galleryop/list_models_test.go

@@ -0,0 +1,64 @@
+package galleryop_test
+
+import (
+	"os"
+	"path/filepath"
+
+	"github.com/mudler/LocalAI/core/config"
+	"github.com/mudler/LocalAI/core/services/galleryop"
+	"github.com/mudler/LocalAI/pkg/model"
+	"github.com/mudler/LocalAI/pkg/system"
+
+	. "github.com/onsi/ginkgo/v2"
+	. "github.com/onsi/gomega"
+)
+
+// Regression test for issue #9817: the Ollama /api/tags handler calls
+// ListModels with a nil filter, which used to panic as soon as a loose file
+// existed under ModelsPath. The panic surfaced to Ollama clients (e.g. Home
+// Assistant) as "Server disconnected without sending a response".
+var _ = Describe("ListModels", func() {
+	var (
+		tempDir     string
+		bcl         *config.ModelConfigLoader
+		ml          *model.ModelLoader
+		systemState *system.SystemState
+	)
+
+	BeforeEach(func() {
+		var err error
+		tempDir, err = os.MkdirTemp("", "list-models-test-*")
+		Expect(err).NotTo(HaveOccurred())
+
+		systemState, err = system.GetSystemState(system.WithModelPath(tempDir))
+		Expect(err).NotTo(HaveOccurred())
+		ml = model.NewModelLoader(systemState)
+		bcl = config.NewModelConfigLoader(tempDir)
+	})
+
+	AfterEach(func() {
+		os.RemoveAll(tempDir)
+	})
+
+	It("does not panic with a nil filter when loose files exist", func() {
+		// ListFilesInModelPath skips well-known weight-file extensions
+		// (.gguf, .bin, ...) so use an extension-less file to ensure the
+		// filter path is exercised.
+		Expect(os.WriteFile(filepath.Join(tempDir, "loose-model"), []byte("x"), 0o644)).To(Succeed())
+
+		var names []string
+		var err error
+		Expect(func() {
+			names, err = galleryop.ListModels(bcl, ml, nil, galleryop.SKIP_IF_CONFIGURED)
+		}).ToNot(Panic())
+		Expect(err).ToNot(HaveOccurred())
+		Expect(names).To(ContainElement("loose-model"))
+	})
+
+	It("does not panic with a nil filter when ModelsPath is empty", func() {
+		Expect(func() {
+			_, err := galleryop.ListModels(bcl, ml, nil, galleryop.SKIP_IF_CONFIGURED)
+			Expect(err).ToNot(HaveOccurred())
+		}).ToNot(Panic())
+	})
+})

docs/content/advanced/model-configuration.md

@@ -316,23 +316,132 @@ These are set via the `options:` array in the model configuration (format: `key:
 
 #### Speculative Type Values
 
-| Type | Description |
-|------|-------------|
-| `none` | No speculative decoding (default) |
-| `draft` | Draft model-based speculation (auto-set when `draft_model` is configured) |
-| `eagle3` | EAGLE3 draft model architecture |
-| `ngram_simple` | Simple self-speculative using token history |
-| `ngram_map_k` | N-gram with key-only map |
-| `ngram_map_k4v` | N-gram with keys and 4 m-gram values |
-| `ngram_mod` | Modified n-gram speculation |
-| `ngram_cache` | 3-level n-gram cache |
+The canonical names match upstream llama.cpp (dash-separated). For backward compatibility LocalAI also accepts the underscore-separated forms and the bare `draft` / `eagle3` aliases.
 
-Multiple types can be chained by passing a comma-separated list to `spec_type` (e.g. `spec_type:ngram_simple,ngram_mod`). The runtime tries them in order and accepts the first proposal that meets the acceptance criteria.
+| Type | Aliases accepted | Description |
+|------|------------------|-------------|
+| `none` | | No speculative decoding (default) |
+| `draft-simple` | `draft`, `draft_simple` | Draft model-based speculation (auto-set when `draft_model` is configured) |
+| `draft-eagle3` | `eagle3`, `draft_eagle3` | EAGLE3 draft model architecture |
+| `draft-mtp` | `draft_mtp` | Multi-Token Prediction. Reuses the target model's embedded MTP head; no separate draft GGUF required (`draft_model` can be omitted). |
+| `ngram-simple` | `ngram_simple` | Simple self-speculative using token history |
+| `ngram-map-k` | `ngram_map_k` | N-gram with key-only map |
+| `ngram-map-k4v` | `ngram_map_k4v` | N-gram with keys and 4 m-gram values |
+| `ngram-mod` | `ngram_mod` | Modified n-gram speculation |
+| `ngram-cache` | `ngram_cache` | 3-level n-gram cache |
+
+Multiple types can be chained by passing a comma-separated list to `spec_type` (e.g. `spec_type:ngram-simple,ngram-mod`). The runtime tries them in order and accepts the first proposal that meets the acceptance criteria.
 
 {{% notice note %}}
 Speculative decoding is automatically disabled when multimodal models (with `mmproj`) are active. The `n_draft` parameter can also be overridden per-request.
 {{% /notice %}}
 
+##### Multi-Token Prediction (MTP)
+
+`draft-mtp` enables [Multi-Token Prediction](https://github.com/ggml-org/llama.cpp/pull/22673) (ggml-org/llama.cpp#22673). MTP uses a small prediction head trained into the target model: the head runs alongside the main forward pass and proposes the next few tokens, which the target then verifies in a single batched step. Upstream reports ~1.85x-2.1x token throughput at ~72-82% draft acceptance on Qwen3.6 27B / 35B A3B.
+
+**Auto-detection (default).** When a GGUF declares an MTP head (the upstream `<arch>.nextn_predict_layers` metadata key, set by `convert_hf_to_gguf.py` for Qwen3.5/3.6 family models and similar), LocalAI auto-enables MTP with the following defaults:
+
+```yaml
+options:
+  - spec_type:draft-mtp
+  - spec_n_max:6
+  - spec_p_min:0.75
+```
+
+Detection runs both at **import time** (the `/import-model` UI / `POST /models/import-uri` flow range-fetches the GGUF header and writes the options into the generated YAML before you save it) and at **load time** (every llama-cpp model start re-checks the local header and appends the options if `spec_type` isn't already set). To opt out, set an explicit `spec_type:` / `speculative_type:` in your YAML - auto-detection always preserves the user value, including `spec_type:none`.
+
+**Two ways to load the MTP head:**
+
+1. **Embedded in the target GGUF** (the recommended path for LocalAI, and what auto-detection assumes). When `spec_type` includes `draft-mtp` and `draft_model` is empty, the backend builds the MTP draft context directly from the target model's weights. The GGUF must have been converted with the MTP tensors included.
+2. **Separate `mtp-*.gguf` sibling file.** If you point `draft_model` at the separate MTP-head GGUF that ships next to the main weights on HuggingFace, the backend will load it as a draft model. Note: upstream's `-hf` auto-discovery of `mtp-*.gguf` siblings is **not** wired into LocalAI's gRPC layer - you need to download the sibling file and configure `draft_model` explicitly.
+
+**Manual override knobs** (overlap with the auto-detect defaults above):
+
+| Option | Recommended | Notes |
+|--------|------------|-------|
+| `spec_type` | `draft-mtp` | Activates MTP. Can be chained with other types (see below). |
+| `spec_n_max` / `draft_max` | `2`-`6` | Number of draft tokens per step. Upstream's PR suggests 2-3 for the tightest acceptance window; LocalAI's auto-default is 6 to favour throughput on models with high acceptance. |
+| `spec_p_min` | `0.75` | Pinned because upstream marks the current default with a "change to 0.0f" TODO; locking it here keeps acceptance thresholds stable across future llama.cpp bumps. |
+| `mmproj_use_gpu` | `false` (or unset `mmproj`) | MTP has a prompt-processing overhead; if the model is non-vision, drop the mmproj entirely to save VRAM. |
+
+**Minimal config** (override-only, since auto-detection already covers this for MTP-capable GGUFs):
+
+```yaml
+name: qwen3-mtp
+backend: llama-cpp
+parameters:
+  model: qwen3-27b-with-mtp.gguf
+options:
+  - spec_type:draft-mtp
+  - spec_n_max:3
+```
+
+**With a separate MTP head file:**
+
+```yaml
+name: qwen3-mtp
+backend: llama-cpp
+parameters:
+  model: qwen3-27b.gguf
+  draft_model: qwen3-27b-mtp-head.gguf
+options:
+  - spec_type:draft-mtp
+  - spec_n_max:3
+```
+
+**Chaining MTP with n-gram fallback** (experimental, from the PR's usage notes - useful when MTP acceptance drops on highly repetitive output):
+
+```yaml
+options:
+  - spec_type:draft-mtp,ngram-mod
+  - spec_n_max:3
+  - spec_ngram_mod_n_match:24
+```
+
+Pre-converted GGUFs with MTP heads are published on the [ggml-org HuggingFace org](https://huggingface.co/ggml-org) (initially Qwen3.6 27B and Qwen3.6 35B A3B).
+
+### Reasoning Models (DeepSeek-R1, Qwen3, etc.)
+
+These load-time options control how the backend parses `<think>` reasoning blocks and how much budget the model is allowed for thinking. They are set per model via the `options:` array.
+
+| Option | Type | Default | Description |
+|--------|------|---------|-------------|
+| `reasoning_format` | string | `deepseek` | Parser for reasoning/thinking blocks. One of `none`, `auto`, `deepseek`, `deepseek-legacy` (alias `deepseek_legacy`). |
+| `enable_reasoning` / `reasoning_budget` | int | `-1` | Reasoning budget in tokens: `-1` unlimited, `0` disabled, `>0` token cap for the thinking section. |
+| `prefill_assistant` | bool | `true` | When `false`, the trailing assistant message is not pre-filled by the chat template. |
+
+{{% notice note %}}
+This is the load-time reasoning configuration. The orthogonal per-request `enable_thinking` chat-template kwarg (set via the YAML `reasoning.disable` field) toggles thinking on/off per call without restarting the model.
+{{% /notice %}}
+
+### Multimodal Backend Options
+
+| Option | Type | Default | Description |
+|--------|------|---------|-------------|
+| `mmproj_use_gpu` / `mmproj_offload` | bool | `true` | Set `false` to keep the multimodal projector on CPU (saves VRAM at cost of speed). |
+| `image_min_tokens` | int | `-1` | Minimum vision tokens per image. `-1` keeps the model default. |
+| `image_max_tokens` | int | `-1` | Maximum vision tokens per image. `-1` keeps the model default. |
+
+### Embedding & Reranking Backend Options
+
+| Option | Type | Default | Description |
+|--------|------|---------|-------------|
+| `pooling_type` / `pooling` | string | auto | Pooling strategy for embeddings: `none`, `mean`, `cls`, `last`, `rank`. Reranking automatically uses `rank`. |
+| `embd_normalize` / `embedding_normalize` | int | `2` | Normalization: `-1` none, `0` max-abs, `1` taxicab, `2` Euclidean (L2), `>2` p-norm. |
+
+### Other Backend Tuning Options
+
+These llama.cpp options are passed through the `options:` array.
+
+| Option | Type | Default | Description |
+|--------|------|---------|-------------|
+| `n_ubatch` / `ubatch` | int | same as `batch` | Physical batch size. Decouple from `n_batch` when an embedding/rerank workload needs a different value. |
+| `threads_batch` / `n_threads_batch` | int | same as `threads` | Threads used during prompt processing. `<= 0` means `hardware_concurrency()`. |
+| `direct_io` / `use_direct_io` | bool | `false` | Open the model with `O_DIRECT` (faster cold loads on NVMe; ignored if not supported). |
+| `verbosity` | int | `3` | llama.cpp internal log verbosity threshold. Higher = more verbose. |
+| `override_tensor` / `tensor_buft_overrides` | string | "" | Per-tensor buffer-type overrides for the main model. Format: `<tensor regex>=<buffer type>,<tensor regex>=<buffer type>,...`. Mirrors the existing `draft_override_tensor` syntax for the draft model. |
+
 ### Prompt Caching
 
 | Field | Type | Description |

docs/data/version.json

@@ -1,3 +1,3 @@
 {
-  "version": "v4.2.3"
+  "version": "v4.2.5"
 }

go.mod

@@ -163,7 +163,7 @@ require (
 	github.com/gocolly/colly v1.2.0 // indirect
 	github.com/gofiber/fiber/v2 v2.52.13 // indirect
 	github.com/golang/protobuf v1.5.4 // indirect
-	github.com/gomarkdown/markdown v0.0.0-20250311123330-531bef5e742b // indirect
+	github.com/gomarkdown/markdown v0.0.0-20260411013819-759bbc3e3207 // indirect
 	github.com/google/go-github/v69 v69.2.0 // indirect
 	github.com/google/go-querystring v1.1.0 // indirect
 	github.com/jackc/pgpassfile v1.0.0 // indirect
@@ -359,7 +359,7 @@ require (
 	github.com/jaypipes/pcidb v1.1.1 // indirect
 	github.com/jbenet/go-temp-err-catcher v0.1.0 // indirect
 	github.com/josharian/intern v1.0.0 // indirect
-	github.com/klauspost/compress v1.18.5 // indirect
+	github.com/klauspost/compress v1.18.5
 	github.com/klauspost/pgzip v1.2.5 // indirect
 	github.com/koron/go-ssdp v0.0.6 // indirect
 	github.com/libp2p/go-buffer-pool v0.1.0 // indirect

go.sum

@@ -472,8 +472,8 @@ github.com/golang/protobuf v1.5.4/go.mod h1:lnTiLA8Wa4RWRcIUkrtSVa5nRhsEGBg48fD6
 github.com/golang/snappy v0.0.2/go.mod h1:/XxbfmMg8lxefKM7IXC3fBNl/7bRcc72aCRzEWrmP2Q=
 github.com/golang/snappy v0.0.5-0.20231225225746-43d5d4cd4e0e h1:4bw4WeyTYPp0smaXiJZCNnLrvVBqirQVreixayXezGc=
 github.com/golang/snappy v0.0.5-0.20231225225746-43d5d4cd4e0e/go.mod h1:/XxbfmMg8lxefKM7IXC3fBNl/7bRcc72aCRzEWrmP2Q=
-github.com/gomarkdown/markdown v0.0.0-20250311123330-531bef5e742b h1:EY/KpStFl60qA17CptGXhwfZ+k1sFNJIUNR8DdbcuUk=
-github.com/gomarkdown/markdown v0.0.0-20250311123330-531bef5e742b/go.mod h1:JDGcbDT52eL4fju3sZ4TeHGsQwhG9nbDV21aMyhwPoA=
+github.com/gomarkdown/markdown v0.0.0-20260411013819-759bbc3e3207 h1:p7t34F7K4OCRQblcDhNJnP46Uaarz3z2cLcvOZYxWn8=
+github.com/gomarkdown/markdown v0.0.0-20260411013819-759bbc3e3207/go.mod h1:JDGcbDT52eL4fju3sZ4TeHGsQwhG9nbDV21aMyhwPoA=
 github.com/google/btree v0.0.0-20180813153112-4030bb1f1f0c/go.mod h1:lNA+9X1NB3Zf8V7Ke586lFgjr2dZNuvo3lPJSGZ5JPQ=
 github.com/google/btree v1.0.0/go.mod h1:lNA+9X1NB3Zf8V7Ke586lFgjr2dZNuvo3lPJSGZ5JPQ=
 github.com/google/btree v1.1.3 h1:CVpQJjYgC4VbzxeGVHfvZrv1ctoYCAI8vbl07Fcxlyg=

pkg/downloader/huggingface.go

@@ -33,6 +33,7 @@ func HuggingFaceScan(uri URI) (*HuggingFaceScanResult, error) {
 	if err != nil {
 		return nil, err
 	}
+	defer results.Body.Close()
 	if results.StatusCode != 200 {
 		return nil, fmt.Errorf("unexpected status code during HuggingFaceScan: %d", results.StatusCode)
 	}

pkg/utils/untar.go

@@ -1,9 +1,13 @@
 package utils
 
 import (
+	"archive/tar"
 	"fmt"
 	"os"
+	"path/filepath"
+	"strings"
 
+	"github.com/klauspost/compress/zip"
 	"github.com/mholt/archiver/v3"
 )
 
@@ -54,7 +58,15 @@ func ExtractArchive(archive, dst string) error {
 		v.Tar = mytar
 	}
 
+	extractRoot, err := filepath.Abs(dst)
+	if err != nil {
+		return err
+	}
+
 	err = archiver.Walk(archive, func(f archiver.File) error {
+		if err := validateArchiveMemberPath(extractRoot, archiveMemberName(f)); err != nil {
+			return err
+		}
 		if f.FileInfo.Mode()&os.ModeSymlink != 0 {
 			return fmt.Errorf("archive contains a symlink")
 		}
@@ -67,3 +79,41 @@ func ExtractArchive(archive, dst string) error {
 
 	return un.Unarchive(archive, dst)
 }
+
+func archiveMemberName(f archiver.File) string {
+	switch h := f.Header.(type) {
+	case tar.Header:
+		return h.Name
+	case *tar.Header:
+		return h.Name
+	case zip.FileHeader:
+		return h.Name
+	case *zip.FileHeader:
+		return h.Name
+	default:
+		return f.Name()
+	}
+}
+
+func validateArchiveMemberPath(root, name string) error {
+	if name == "" {
+		return fmt.Errorf("archive contains an empty path")
+	}
+
+	normalizedName := filepath.FromSlash(strings.ReplaceAll(name, "\\", "/"))
+	cleanedName := filepath.Clean(normalizedName)
+	if filepath.IsAbs(cleanedName) || cleanedName == ".." || strings.HasPrefix(cleanedName, ".."+string(os.PathSeparator)) {
+		return fmt.Errorf("archive contains an unsafe path: %s", name)
+	}
+
+	targetPath := filepath.Join(root, cleanedName)
+	relativePath, err := filepath.Rel(root, targetPath)
+	if err != nil {
+		return err
+	}
+	if relativePath == ".." || strings.HasPrefix(relativePath, ".."+string(os.PathSeparator)) || filepath.IsAbs(relativePath) {
+		return fmt.Errorf("archive contains an unsafe path: %s", name)
+	}
+
+	return nil
+}

pkg/utils/untar_test.go

@@ -0,0 +1,128 @@
+package utils_test
+
+import (
+	"archive/tar"
+	"archive/zip"
+	"os"
+	"path/filepath"
+
+	. "github.com/mudler/LocalAI/pkg/utils"
+	. "github.com/onsi/ginkgo/v2"
+	. "github.com/onsi/gomega"
+)
+
+var _ = Describe("utils/archive tests", func() {
+	It("extracts regular nested zip members", func() {
+		tmpDir := GinkgoT().TempDir()
+		archivePath := filepath.Join(tmpDir, "model.zip")
+		extractPath := filepath.Join(tmpDir, "models")
+
+		Expect(writeZipArchive(archivePath, map[string]string{
+			"nested/model.yaml": "name: test",
+		})).To(Succeed())
+
+		Expect(ExtractArchive(archivePath, extractPath)).To(Succeed())
+
+		extracted, err := os.ReadFile(filepath.Join(extractPath, "nested", "model.yaml"))
+		Expect(err).ToNot(HaveOccurred())
+		Expect(string(extracted)).To(Equal("name: test"))
+	})
+
+	It("rejects zip members that escape the destination", func() {
+		tmpDir := GinkgoT().TempDir()
+		archivePath := filepath.Join(tmpDir, "model.zip")
+		extractPath := filepath.Join(tmpDir, "models")
+
+		Expect(writeZipArchive(archivePath, map[string]string{
+			"../escaped.txt": "escaped",
+		})).To(Succeed())
+
+		err := ExtractArchive(archivePath, extractPath)
+
+		Expect(err).To(HaveOccurred())
+		Expect(err.Error()).To(ContainSubstring("unsafe path"))
+		Expect(filepath.Join(tmpDir, "escaped.txt")).ToNot(BeAnExistingFile())
+	})
+
+	It("rejects tar members that escape the destination", func() {
+		tmpDir := GinkgoT().TempDir()
+		archivePath := filepath.Join(tmpDir, "model.tar")
+		extractPath := filepath.Join(tmpDir, "models")
+
+		Expect(writeTarArchive(archivePath, map[string]string{
+			"../escaped.txt": "escaped",
+		})).To(Succeed())
+
+		err := ExtractArchive(archivePath, extractPath)
+
+		Expect(err).To(HaveOccurred())
+		Expect(err.Error()).To(ContainSubstring("unsafe path"))
+		Expect(filepath.Join(tmpDir, "escaped.txt")).ToNot(BeAnExistingFile())
+	})
+})
+
+func writeZipArchive(path string, files map[string]string) (err error) {
+	out, err := os.Create(path)
+	if err != nil {
+		return err
+	}
+	defer func() {
+		if closeErr := out.Close(); err == nil {
+			err = closeErr
+		}
+	}()
+
+	writer := zip.NewWriter(out)
+	defer func() {
+		if closeErr := writer.Close(); err == nil {
+			err = closeErr
+		}
+	}()
+
+	for name, contents := range files {
+		fileWriter, err := writer.Create(name)
+		if err != nil {
+			return err
+		}
+		if _, err := fileWriter.Write([]byte(contents)); err != nil {
+			return err
+		}
+	}
+
+	return nil
+}
+
+func writeTarArchive(path string, files map[string]string) (err error) {
+	out, err := os.Create(path)
+	if err != nil {
+		return err
+	}
+	defer func() {
+		if closeErr := out.Close(); err == nil {
+			err = closeErr
+		}
+	}()
+
+	writer := tar.NewWriter(out)
+	defer func() {
+		if closeErr := writer.Close(); err == nil {
+			err = closeErr
+		}
+	}()
+
+	for name, contents := range files {
+		data := []byte(contents)
+		if err := writer.WriteHeader(&tar.Header{
+			Name: name,
+			Mode: 0o600,
+			Size: int64(len(data)),
+		}); err != nil {
+			return err
+		}
+		if _, err := writer.Write(data); err != nil {
+			return err
+		}
+	}
+
+	return nil
+}

swagger/docs.go

@@ -5347,6 +5347,14 @@ const docTemplate = `{
                 "stream": {
                     "type": "boolean"
                 },
+                "stream_options": {
+                    "description": "StreamOptions opts into OpenAI streaming extensions, e.g. include_usage.",
+                    "allOf": [
+                        {
+                            "$ref": "#/definitions/schema.StreamOptions"
+                        }
+                    ]
+                },
                 "temperature": {
                     "type": "number"
                 },
@@ -5412,7 +5420,12 @@ const docTemplate = `{
                     "type": "string"
                 },
                 "usage": {
-                    "$ref": "#/definitions/schema.OpenAIUsage"
+                    "description": "Usage is intentionally a pointer with omitempty: per the OpenAI\nchat-completion streaming spec, intermediate chunks must not carry\na ` + "`" + `usage` + "`" + ` field. Marshalling a value-typed usage would emit\n` + "`" + `\"usage\":{\"prompt_tokens\":0,...}` + "`" + ` on every chunk and break\nOpenAI-SDK consumers that filter on a truthy ` + "`" + `result.usage` + "`" + `\n(continuedev/continue, Kilo Code, Roo Code, etc.).",
+                    "allOf": [
+                        {
+                            "$ref": "#/definitions/schema.OpenAIUsage"
+                        }
+                    ]
                 }
             }
         },
@@ -5578,6 +5591,14 @@ const docTemplate = `{
                 }
             }
         },
+        "schema.StreamOptions": {
+            "type": "object",
+            "properties": {
+                "include_usage": {
+                    "type": "boolean"
+                }
+            }
+        },
         "schema.SysInfoModel": {
             "type": "object",
             "properties": {

swagger/swagger.json

@@ -5344,6 +5344,14 @@
                 "stream": {
                     "type": "boolean"
                 },
+                "stream_options": {
+                    "description": "StreamOptions opts into OpenAI streaming extensions, e.g. include_usage.",
+                    "allOf": [
+                        {
+                            "$ref": "#/definitions/schema.StreamOptions"
+                        }
+                    ]
+                },
                 "temperature": {
                     "type": "number"
                 },
@@ -5409,7 +5417,12 @@
                     "type": "string"
                 },
                 "usage": {
-                    "$ref": "#/definitions/schema.OpenAIUsage"
+                    "description": "Usage is intentionally a pointer with omitempty: per the OpenAI\nchat-completion streaming spec, intermediate chunks must not carry\na `usage` field. Marshalling a value-typed usage would emit\n`\"usage\":{\"prompt_tokens\":0,...}` on every chunk and break\nOpenAI-SDK consumers that filter on a truthy `result.usage`\n(continuedev/continue, Kilo Code, Roo Code, etc.).",
+                    "allOf": [
+                        {
+                            "$ref": "#/definitions/schema.OpenAIUsage"
+                        }
+                    ]
                 }
             }
         },
@@ -5575,6 +5588,14 @@
                 }
             }
         },
+        "schema.StreamOptions": {
+            "type": "object",
+            "properties": {
+                "include_usage": {
+                    "type": "boolean"
+                }
+            }
+        },
         "schema.SysInfoModel": {
             "type": "object",
             "properties": {

swagger/swagger.yaml

@@ -1650,6 +1650,10 @@ definitions:
       stop: {}
       stream:
         type: boolean
+      stream_options:
+        allOf:
+        - $ref: '#/definitions/schema.StreamOptions'
+        description: StreamOptions opts into OpenAI streaming extensions, e.g. include_usage.
       temperature:
         type: number
       tfz:
@@ -1698,7 +1702,15 @@ definitions:
       object:
         type: string
       usage:
-        $ref: '#/definitions/schema.OpenAIUsage'
+        allOf:
+        - $ref: '#/definitions/schema.OpenAIUsage'
+        description: |-
+          Usage is intentionally a pointer with omitempty: per the OpenAI
+          chat-completion streaming spec, intermediate chunks must not carry
+          a `usage` field. Marshalling a value-typed usage would emit
+          `"usage":{"prompt_tokens":0,...}` on every chunk and break
+          OpenAI-SDK consumers that filter on a truthy `result.usage`
+          (continuedev/continue, Kilo Code, Roo Code, etc.).
     type: object
   schema.OpenAIUsage:
     properties:
@@ -1813,6 +1825,11 @@ definitions:
           $ref: '#/definitions/schema.NodeData'
         type: array
     type: object
+  schema.StreamOptions:
+    properties:
+      include_usage:
+        type: boolean
+    type: object
   schema.SysInfoModel:
     properties:
       id: