The OpenAI-compatible endpoints filled in sampler options the client
left out: temperature and top_p fell back to 1.0, and /v1/completions
always sent frequency_penalty and presence_penalty as 0. Request
options are applied last, so these values replaced the model's packaged
defaults. /api/chat honored those defaults; /v1 did not.
Omitted options are now left unset on /v1/chat/completions,
/v1/completions and /v1/responses, so the model's parameters apply, the
same as on /api/chat. Values the client sends still win, including an
explicit 0. On /v1/completions, top_p and the penalties are now
pointers so "omitted" and "0" can be told apart.
Clients that send no sampler options now get the model's defaults
instead of 1.0/1.0. A model with no sampler parameters gets Ollama's
defaults (temperature 0.8, top_p 0.9), as on /api/chat.
Fixes#17744Fixes#18690
The [img] placeholders mixed internal markers into tool text: the native
llama-server path sends them as literal text, so an image-only result
gained a text part, and tool output containing [img] or [img-N] moved or
dropped image tags. api.Message cannot carry image positions without
in-band markers, so stop merging tool results that include images.
Text-only tool results, the only array form the Chat Completions spec
allows for tool messages, are still merged into one message (now with a
strings.Builder). Results with images keep the per-part split in order,
which preserves image positions, with the tool call id and name set on
every part.
Merging a tool result's parts collected its images separately from the
text, so renderers placed them all before the text: "Before: <A> After:
<B>" rendered as "<A><B>Before: After:". Write an [img] placeholder at
each image's position so renderContentWithImageTags and chatPrompt put
each image back where it was.
A tool message with "content": [] produced no message at all, so the
tool call it answers had no result in the rendered prompt. Emit an empty
tool message that keeps its ToolCallID and ToolName, and skip it only
when it has no parts, call id, or name.
FromChatRequest splits array content into one api.Message per part. That
is intended for user content, but a tool message is a single result: the
split also dropped its tool_call_id and tool name, since those were only
copied when the message had tool_calls. Renderers that resolve tool
results by ID then see extra, unnamed results; for example, Kimi K3 fails
with "tool message 2 has no resolvable tool name" on a tool result with
two text parts, which the OpenAI spec allows.
Merge the parts of a tool message into one message with the text joined,
the images kept, and ToolCallID and ToolName set, matching how
function_call_output items are converted for /v1/responses.
* bench: use code prompts for prompt-token runs
Replace the synthetic word-list prompt with packed HumanEval Python prompts so speculative draft models see code-like continuations.
Keep the benchmark on the chat path, and calibrate -prompt-tokens with untimed chat probes that read PromptEvalCount instead of relying on a separate token-count API.
Each request still gets a nonce prefix to avoid prefix-cache hits while timed epochs measure the chat-rendered workload.
* review comments
* use different windows for each epoch
* add OpenAI API support
* address comments
* Report cached prompt tokens
Add prompt_eval_cached_count to native responses and expose equivalent cached-token fields through the OpenAI- and Anthropic-compatible APIs. Keep prompt_eval_count as the logical input total while excluding cache hits from CLI and benchmark prefill rates. Surface processed and cached prompt counts in benchmark output.
Collect cache counts from llama-server and MLX, preserve coherent metrics across two-pass structured generation.
Fixes#8008
Related to #15758
* review comments
* launch: add Muse Code integration
Add `ollama launch muse` for Meta's Muse Code CLI.
Muse only takes a model catalog from settings.json (normally it fetches one from its provider and refuses to start otherwise), and that file's endpoint_transport is a global provider switch. So the integration writes a settings file under its own config root (~/.ollama/launch/muse-config via XDG_CONFIG_HOME), leaving a Meta-backed muse install untouched, and re-seeds it from muse's own persisted copy on later runs.
The launched model is preloaded so its catalog row carries the context length the server actually allocated, not the trained maximum; the loaded-context helpers move from cmd/agent_tui.go into cmd/launch for reuse.
Muse sends reasoning efforts outside Ollama's scale (minimal, xhigh, ultra), which were hard 400s; clamp them to the nearest tier in one helper shared by the chat and responses converters.
The registry entry stays Hidden (alias "muse-code"), like kimi and vscode.
* review comments
* skip muse test on windows (unsupported platform)
The Responses API groups related tools by domain: a tool with type "namespace" carries the real function definitions in a nested tools array. The conversion dropped that array, leaving the model a single schema-less pseudo-function and making every namespaced call undeclarable.
Expand namespace declarations into their member functions with namespace-qualified names, since api.Tool carries only a flat function name.
Relates to #15921: full Responses API parity also wants the namespace preserved as a separate field on tool calls in the output, which needs new api surface and is not addressed here.
Reworked our /v1/chat/completions streaming to match what api.openai.com actually sends,
chunk-for-chunk, based on captures I took of real OpenAI traffic.
What changed:
- finish_reason now goes on its own chunk with an empty delta {}, instead of riding on the last content
chunk. Precedence is length > tool_calls > the response's done reason > stop.
- role is only sent on the first chunk of a stream, not on every chunk.
- With stream_options.include_usage, usage goes out on its own chunk with choices: [] after the finish
chunk.
- A truncated response keeps finish_reason: "length" even when tool calls were streamed — it used to get
overwritten with "tool_calls". Fixed in both streaming and non-streaming paths.
- The metrics-only trailer response (empty message at end of stream) no longer produces a stray
delta:{"content":""} chunk before the finish chunk. A wholly empty completion still opens with a role
chunk.
- Every chunk in a stream shares one timestamp, from the response's CreatedAt.
Remove the x/imagegen tree (MLX image generation engine, Flux2/zimage
models, cache, C bindings) and all imagegen integration points:
- server: drop imagegen routes, scheduling, and generate handling
- api/cmd/docs: remove image generation API surface and docs
- middleware/openai: remove image endpoint support
- integration: remove imagegen test suites
- x/create: adopt the rewritten create pipeline from main; drop
imagegen create path (CreateImageGenModel, IsTensorModelDir,
model_index.json detection, Flux2KleinPipeline vision hack)
- retain x/imagegen/manifest (Ollama-store safetensors manifest
loader), still used by x/mlxrunner and x/create/client
- fix Windows MLX dl.dll install, MLX CMake version path, and the
show command after removing safetensors models
* broad lint fixes to sidestep CI scope glitch
* runner: Remove CGO engines, use llama-server exclusively for GGML models
Remove the vendored GGML and llama.cpp backend, CGO runner, Go model
implementations, and sample. llama-server (built from upstream llama.cpp via
FetchContent) is now the sole inference engine for GGUF-based models.
(Safetensor based models continue to run on the new MLX engine.) This allows
us to more rapidly pick up new capabilities and fixes from llama.cpp as they
come out.
On windows this now requires recent AMD driver versions to support ROCm v7 as
llama.cpp currently does not support building against v6.
* llama/compat: load Ollama-format GGUFs in llama-server
Squashed from upstream/jmorganca/llama-compat on 2026-04-29.
Source tip: 0c33775d37.
Original source commits:
- 25223160d llama/compat: add in-memory shim so llama-server can load Ollama-format GGUFs
- 7449b539a llm,server: route Ollama-format gemma3 blobs through llama/compat
- 436f2e2b1 llama/compat: make patch-apply idempotent
- 8c2c9d4c8 llama/compat: extend gemma3 handler to cover 1B and 270M blobs
- 021389f7b llama/compat: shrink clip.cpp injection from 18 lines to 1
- 61b367ec2 llama/compat: shrink patch to pure call-site hooks (34 -> 20 lines)
- 36049361c llama/compat: simplify shim (gemma3-tested)
- 8fa664865 llama/compat: add qwen35moe text handler
- db0c74530 llama/compat: add qwen35moe vision (clip) support
- 2a388da77 llama/compat: split shared infra into a util TU
- 9a69a17dc llama/compat: document non-public API dependencies
- d0f38a915 llama/compat: add gpt-oss and lfm2 handlers
- 086071822 llama/compat: add mistral3 text handler (vision TODO)
- 63bde9ff7 llama/compat: add mistral3 vision (clip) support
- 3a57b89d5 llama/compat: apply LLaMA RoPE permute to mistral3 vision Q/K
- 99cb87439 llama/compat: add qwen35, gemma4, deepseek-ocr handlers
- 2c7850dba llama/compat: add nemotron_h_moe handler (latent FFN + MTP skip)
- 9e3b54225 llama/compat: add llama4 text + clip handlers
- 034fee349 llama/compat: add gemma4 clip handler (gemma4v projector)
- 9945c5a93 server: remove dhiltgen/* compat redirect table
- 5d4539101 llama/compat: rewrite gemma4 tokenizer model to BPE
- 7e0765327 llama/compat: add glm-ocr text handler + text-loader load-op hook
- f1bd1a25a llama/compat: add glm-ocr clip handler (glm4v projector)
- 4b5cf3420 llama/compat: collapse text-loader hook back to one new patch line
- eb4ecf4fc llama/compat: extend gemma4 clip handler to gemma4a (audio)
- a23a5e76f llama/compat: fix gemma4a per-block norm tensor mapping
- cd2dcaff4 llama/compat: add embeddinggemma handler
- 1ce8a6b26 llama/compat: add qwen3-vl + qwen2.5-vl handlers
- fd98ffa1e llama/compat: add gemma3n + glm4moelite handlers
- cc7bdf0bc llama/compat: handle null buft in maybe_load_tensor
- 0c33775d3 llama/compat: disable mmap when load_op transforms text-side tensors
* refine implementation
* ci: fix windows MLX build
* ci: fix windows llama-server build
* ci: fix windows rocm build
* ci: windows mlx tuning
Shorten long-tail on build, and get OllamaSetup.exe back under 2g limit
* ci: fix windows dependencies
* win: fix dependency gathering
* disable openmp
* win: arm64 cross-compile build
also DRY out CI steps
* scheduler improvements
* ci: improvements from #15982
* win: favor ninja for faster developer builds
* win: fix build
* win: fix arm64 cross-compile
* win: avoid spaces in compiler path
* misc discovery fixes, and bos handling
* lint fixes
* win: fix arm cross-compile build/CI bugs
* llama.cpp update
* win: handle multiple CRT dirs
* vulkan: add windows iGPU detection
* fix creation bugs for patched models, other refactoring work
* tune batch size for better performance
* ci and lint fixes
* fix repeat_last_n bug
* build: revamp build for better developer UX
* amd, sampler, qwen3next fixes
* version bump
* fix mlx build
* revamp GPU discovery
Scanning the output of llama-server is turning out to be too error prone across
llama.cpp updates, so this switches to a thin dynamic library load against the
bundled GGML libraries so more details can be gathered from the API.
* version bump
* missing file
* ci: fix cache miss on rocm build
* refine vulkan dep handling
* fix ps reporting bug on full GPU load
* improve cmake wiring for customized local builds
* version bump
* docker build arg cleanup
* improve windows exit error logs
* fix community gemma4 support and ci flakes
* fix mlx unit test
* tighten up ps logic to avoid double counting fit log lines
* version bump
* fix ps view for full gpu layer offload
* add MTP wiring for llama-server and create with GGUFs
* pick best template by capabilities
* version bump
* ci: harden apt repos
* remove unused cpu core discovery
* adjust batch default logic to reduce OOMs
* support larger tool calls
* fix audio support, template show
* qwen35 mtp patch support
* flesh out dtypes
* rocm deps
* version bump
* lint fix
* block broken gfx1150 on windows
* fix qwen3.5 moe mtp tensors in patch
* mmproj oom fallback and vulkan on by default
* qwen MTP compat fix
* version bump
* ci: fix WoA cross-compile
* ci: workaround ui tool in cross-compile
* version bump
* win: enable OpenMP for CPU builds
* build: improve developer UX
* ci: windows path workaround for CPU build
* win: fix WoA dependencies
* win: fix large offset reads for mmproj patched loads
* version bump
* fix vulkan dup detection
* add OLLAMA_IGPU_ENABLE and largely disable iGPUs by default
* opt-in MTP, win large offset, integraton fixes
* fix unit test scheduler interaction hang
* fix multi-gpu filtering
* version bump
* review comments
* fix thinking level
* fix linux rocm ordering and granite 3.3 template
* version bump
* ci fix - non-shallow MLX checkout
* bypass linux sysfs unit test on windows
---------
Co-authored-by: jmorganca <jmorganca@gmail.com>
The default branch in unmarshalResponsesInputItem had two issues:
- It referenced typeField.Type instead of itemType; these differ when the
shorthand role-based format promotes an empty type to "message", meaning
an unhandled type would show the wrong value in the error string.
- It used %s formatting, so an empty type field produced the unhelpful
message "unknown input item type: " with no indication what was missing.
Fix by using itemType (the resolved value) with %q quoting, and add a
dedicated message when itemType is empty (both type and role absent):
"input item missing required 'type' field".
Tests added for the empty-type and missing-type cases.
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
In addition to strings (which we already supported), OpenResponses
supports arrays of text content, image content, or file content (see
<https://www.openresponses.org/reference#object-FunctionCallOutput-title>).
We were missing support for these arrays, which caused unmarshal errors
like
```
json: cannot unmarshal array into Go struct field ResponsesFunctionCallOutput.output of type string
```
This change adds support for text content and image content, as those
are more straightforwardly mappable to Ollama message formats (though
image and text interleaving is lost), but it's less clear what to do for
files. In the future we can partially support this by inlining
reasonably sized text files, but wanted to get this change out first.
Fixes: #15250
* bench: add prompt calibration, context size flag, and NumCtx reporting
Add --num-ctx flag to set context size, and report NumCtx in model info
header. Calibrate tokens-per-word ratio during warmup using actual
tokenization metrics from the model, replacing the fixed 1.3 heuristic.
This produces more accurate prompt token counts for --prompt-tokens.
Also add fetchContextLength() to query running model context via /api/ps.
* integration: improve vision test robustness and add thinking tests
Add skipIfNoVisionOverride() to skip vision tests when OLLAMA_TEST_MODEL
is set to a non-vision model. Add Think:false to context exhaustion test
to prevent thinking models from using all context before the test can
measure it. Add third test image (ollama homepage) and replace OCR test
with ImageDescription test using it. Relax match strings for broader
model compatibility. Add TestThinkingEnabled and TestThinkingSuppressed
to verify thinking output and channel tag handling.
* gemma4: add Gemma 4 GGML model support
Add full Gemma 4 model family support (E2B, E4B, 26B MoE, 31B Dense)
for the GGML backend including text, vision, converter, parser, and
renderer.
Text model features:
- Sliding window + full attention with per-layer patterns
- KV sharing across layers with donor map
- Per-layer embeddings (PLE) with learned projections
- MoE routing with RMSNorm + learned scale
- Proportional RoPE with freq_factors for global attention
- Final logit softcapping
Vision model features:
- SigLIP vision encoder with 2D RoPE
- ClippableLinear with input/output clamping via packed v.clamp_data
- Adaptive average pooling with nMerge kernel
- Multi-modal projection with unweighted RMSNorm
Converter:
- Safetensors to GGUF with vision tensor renaming
- Fused MoE gate_up_proj splitting
- Vision patch embedding reshape (HF to Conv2D layout)
- Packed clamp data tensor for ClippableLinear bounds
- Proportional RoPE freq_factors generation
Also includes:
- BackendGet() on ml.Tensor for reading weight tensor data
- Q6_K CUDA get_rows kernel support
- MoE-aware ffn_down quantization layer counting
- Gemma4 parser with tool calling and thinking support
- Gemma4 renderer with structured tool format
- Architecture-based auto-detection of renderer/parser/stop tokens
- Integration test gemma4 model list additions
* gemma4: add audio support with USM conformer encoder
Add audio encoding for Gemma 4 using the USM conformer architecture:
- Converter: audio tensor mapping, SSCP/conformer/embedder name replacements,
softplus repacker for per_dim_scale, F32 enforcement for conv weights
- GGML backend: Conv1DDW and PadExt tensor ops
- Audio encoder: SSCP Conv2D, 12 conformer blocks (FFW + block-local
attention with relative position embeddings + LightConv1d + FFW),
output projection, audio-to-text embedding projector
- Audio preprocessing: WAV decode, mel spectrogram, FFT (pure Go)
- Model wiring: WAV detection, audio token handling, unified PostTokenize
Correctly transcribes "why is the sky blue" from test audio.
* integration: add gemma4 audio tests including OpenAI API coverage
Test audio transcription and response via the Ollama native API, plus
two new tests exercising the OpenAI-compatible endpoints:
- /v1/audio/transcriptions (multipart form upload)
- /v1/chat/completions with input_audio content type
All tests use capability checks and skip models without audio support.
* gemma4: add OpenAI audio API support and capability detection
- Add CapabilityAudio and detect from audio.block_count in GGUF
- Add /v1/audio/transcriptions endpoint with TranscriptionMiddleware
- Add input_audio content type support in /v1/chat/completions
- Add TranscriptionRequest/Response types in openai package
* gemma4: add audio input support for run command
- /audio toggle in interactive mode for voice chat
- Platform-specific microphone recording (AVFoundation on macOS,
PulseAudio/ALSA on Linux, WASAPI on Windows)
- Space to start/stop recording, automatic chunking for long audio
* gemma4: add transcribe command (ollama transcribe MODEL)
- Interactive mode with readline prompt and slash commands
- Non-interactive mode for piped audio or record-until-Ctrl+C
- Chunked streaming transcription for long recordings
- Word-wrapped output matching run command style
* gemma4: add parser, renderer, and integration test plumbing
* gemma4: fix renderer to emit BOS token
* gemma4: add OpenAI audio transcription API and input_audio support
* gemma4: update converter for new weight drop naming
* gemma4: add per_expert_scale to MoE router and fix moe_intermediate_size config
* gemma4: rewrite renderer to match HF Jinja2 template exactly
Fix 8 bugs found by building 55 reference tests verified against the
HF Jinja2 chat template (VERIFY_JINJA2=1 shells out to Python):
- Tool responses use separate <|turn>tool turns (not inline tags)
- Tool calls emitted before content in assistant messages
- Thinking content stripped from assistant history (strip_thinking)
- User, tool, and system content trimmed (template does | trim)
- Empty system message still emits system turn (check role, not content)
- Nested object properties rendered recursively with required field
- Array items specification rendered for array-type properties
- OBJECT/ARRAY type-specific rendering comma logic matches template
Also adds Required field to api.ToolProperty for nested object schemas,
replaces old gemma4_test.go with comprehensive gemma4_reference_test.go,
and commits the Jinja2 template as testdata for verification.
* gemma4: fix MoE fused gate_up split and multiline tool-call arg parsing
- Text MoE: split `ffn_gate_up_exps` into contiguous `[gate|up]` halves instead of stride-2 slices.
- Parser: escape control characters in `<|"|>...<|"|>` string literals when converting tool-call args to JSON.
- Fixes warnings like `invalid character '\n' in string literal` for multiline tool arguments.
- Add Gemma4 parser regressions for multiline tool-call args and `gemma4ArgsToJSON`.
* cmd: simplify audio input to dropped file attachments
* gemma4: use full SWA memory for better cache reuse
* gemma4: initialize clamps after backend load
* convert: align gemma4 audio tensor renames with llama.cpp
* Remove redundant comments in gemma4 vision model
* Format Gemma4 MoE block field alignment
* use 4096 kvcache.NewSWAMemCache
* convert: support new Gemma4 audio_tower tensor naming (#15221)
Co-authored-by: jmorganca <jmorganca@gmail.com>
* fix integration test defaults for audio
* review comments and lint fixes
* remove unused audio/video files
---------
Co-authored-by: jmorganca <jmorganca@gmail.com>
* preserve tool definition and call JSON ordering
This is another iteration of
<https://github.com/ollama/ollama/pull/12518>, but this time we've
simplified things by relaxing the competing requirements of being
compatible AND order-preserving with templates (vs. renderers). We
maintain backwards compatibility at the cost of not guaranteeing order
for templates. We plan on moving more and more models to renderers,
which have been updated to use these new data types, and additionally
we could add an opt-in way of templates getting an order-preserved list
(e.g., via sibling template vars)
* orderedmap_test: remove testify
Adds logprobs support to Ollama's API including support for Ollama's
OpenAI-compatible API. By specifying the new 'logprobs' boolean parameter
in the API, Ollama will return the log probabilities for each token generated.
'top_logprobs', an integer value can also be specified up to the value 20.
When specified, the API will also provide the number of most likely tokens to
return at each token position
Co-authored-by: Baptiste Jamin <baptiste@crisp.chat>
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