Fixes for running LFM3 GGUFs on the llama-server runner, plus GGUF
text-to-speech that turns on once llama.cpp is bumped past b11901.
- BOS: honor an explicit tokenizer.ggml.add_bos_token. LFM3 GGUFs (and
lfm2:24b) set it to false and expect the template's BOS, which the
lfm2 default was stripping. The lfm2 default still applies when the
key is absent. On lfm2:24b, WikiText PPL goes from 81.3 to 72.0.
- repeat_last_n -1 now maps to the context size; llama-server rejects
negative values.
- Audio-only projectors (LFM3-Audio) no longer advertise vision, so
clients don't send images the model can't take.
- /v1/audio/speech on GGUF models calls llama-server /tts chunk by
chunk. Renderers opt in through a new SpeechPrompter (voices,
voice parsing, chunking), so other speech models can add their own.
The LFM3 voice and chunking helpers move from the MLX runner into
model/renderers and are shared by both runners.
* renderers: add gemma4-nemotron renderer
Add a Gemma 4 renderer variant that renders assistant content before
its tool calls rather than after the tool responses. Models opt in with
RENDERER gemma4-nemotron; gemma4 auto-detection is unchanged.
Adds the corresponding chat template as a test fixture for Jinja parity
checks.
* mlx: support Gemma 4 targets with DFlash drafts
Load a draft-owned output head when its config requires one, while preserving the target-head path used by existing DFlash models.
Expose Gemma 4 hidden-state taps and raw logits for speculative decoding. Report safetensors target precision independently of quantized draft weights.
* flint: fix replies with thinking disabled
Close the thinking section when think:false so Flint answers directly. Default and think:true prompts are unchanged.
* update template
Preserve source tool JSON alongside decoded API values so Flint can retain
numeric types and schema order in prompts. Existing API marshaling is unchanged.
Assemble ModelOpt expert banks during safetensors import, and reject direct
Flint NVFP4 creation before conversion. Other models retain their existing
import and quantization paths.
Add optional learned sinks and device causal masks to shared MLX attention.
Existing callers retain the no-sink and host-mask paths; Flint opts into the
new behavior.
* ci: shorten Windows Vulkan build path
Keep the nested shader-generator compiler probes below Windows path limits in checkouts with longer repository names.
* mlx: fix Nemotron bugs and support global scales
Accept published Nemotron layer names while retaining legacy aliases, and
fix layer-count inference and detached MTP quantization. Stabilize Mamba
softplus, preserve FP32 vision normalization, and compile Mamba gate and
norm scaling.
Support per-expert F32 global scales and preserve activation-scale
companions in ModelOpt and compressed-tensors NVFP4 imports. Normalize
reciprocal globals and reject invalid scales. Keep activation scales
separate from weight-only inference.
* mlx: add Nemotron 3.5 Super
* minor adjustments
* mlxrunner: restore System One scoring
Carry the MLX implementation and its tests from the original System One
commit. The API/compiler and llama-server implementation already exist on
the release branch, so retain those versions. Adapt the optional model test
to the renamed decision package and its explicit rendering/context setup.
(cherry picked from commit 26e546ddb3)
* Enable MLX System One and add regression coverage
* mlxrunner: add Laya support for System One
Import Laya checkpoints and implement bidirectional decision scoring.
* mlxrunner: release cached buffers after System One scoring
* decision: match Tev1 and Nimble publisher prompt formats
Select Tev1 prompts using renderer metadata and fix JSON formatting, with regression coverage for GGUF and MLX.
* mlx: support Clef and Clef Flash decision models
Add model import, quantization safeguards, and text/image scoring with the Clef joint decision head.
* support vision capabilities for decision models
* Make Clef scoring cancellable between prefill chunks
Preserve joint-head inputs and test cancellation, cleanup, and recovery.
* mlx: separate decision preparation and readout from forward
Let serial and batched scoring share model-owned preparation and readout
without duplicating architecture-specific logic in the runner.
* mlx: add Strands Decider support
* implement caching
---------
Co-authored-by: ParthSareen <parth.sareen@ollama.com>
A format on a thinking model has to leave the thinking free and constrain
only the content after it, so whatever enforces the format needs to know
where the thinking ends. Today the server guesses whether a parser's
response starts inside thinking from the think value alone, which is wrong
for parsers whose default differs, and it has no way to learn the closing
string at all.
Each parser now answers ThinkingClose after Init: the strings any of which
ends the thinking its response begins with, or none when the response
starts in content because thinking is off, an assistant prefill continues
content, or the parser suppresses thinking for tools. Parsers whose models
open a new message before content end the thinking at that message's
header. Nothing consumes the answer yet.
* nemotron_h: add MLX vision support
Implement the RADIO vision encoder and projector on the shared MLX media pipeline, including dynamic-resolution preprocessing, deterministic placeholder expansion, chunked feature scattering, and MTP offsets.
Expose source-advertised Nemotron vision while continuing to suppress unsupported audio, and preserve both modality towers at source precision during create.
Harden Nemotron streaming parser termination and add focused coverage for vision configuration, media placement, capability reporting, and tool-call parsing.
* review comments
* address comments
The MLX runner is the only Go inference runner left and is no longer
experimental, so its packages leave x/. The bindings become a top-level
mlx package beside the carried patches in mlx/compat, mirroring how
llama/ holds the llama.cpp integration, and the runner becomes mlxrunner
with the architectures nested under the package they implement.
Subpackages move with their parent unless listed.
x/mlxrunner/mlx mlx
x/internal/mlxthread mlx/mlxthread
x/internal/mlxthreadtest mlx/mlxthread/mlxthreadtest
x/internal/mlxtest mlx/mlxtest
x/quant mlx/quant
mlx/compat/*.patch mlx/compat/mlx-c (MLX patches go in mlx/compat/mlx)
x/mlxrunner mlxrunner
x/models/nn mlxrunner/nn
x/models/<arch> mlxrunner/model/<arch>
x/mlxrunner/imports.go mlxrunner/model/architectures (new package)
x/create create
x/safetensors fs/safetensors
x/tokenizer mlxrunner/tokenizer
Every package keeps its name, so the Go changes are the import path
rewrites the moves force, and the CMake, Dockerfile, CI cache keys, drift
check and Darwin payload script follow the new paths. Four edits are not
paths: the runner's blank architecture imports become the package
mlxrunner/model/architectures, so the list to extend for a new model sits
beside the architecture directories; a depguard rule keeps the two test
harnesses out of non-test code, as the x/internal placement used to; the
CI change filter's two entries for the long-deleted x/imagegen/mlx now
name the bindings' CMake project and the carried patches, so a change to
either builds the payload; and the tokenizer parity test reads its
fixtures from its own testdata instead of walking out of x/.
x/server and x/imagegen/manifest stay for the next two commits.
* fix(docs): correct typos found during code review
Non-functional changes only:
- Fixed minor spelling mistakes in comments
- Corrected typos in user-facing strings
- No variables, logic, or functional code was modified.
Signed-off-by: Marcel Petrick <mail@marcelpetrick.it>
* fix additional typos and shell-unsafe example in docs
---------
Co-authored-by: Patrick Devine <patrick@ollama.com>
Coding clients may insert runtime system messages after the initial user turn. The shared Qwen renderer rejected these transcripts before rendering, turning a potentially usable non-standard request into an HTTP 500.
Pass non-leading system turns through the existing raw ChatML path and warn when qwen3.8 encounters one. Extend the Anthropic tool-route integration scenario to cover this message pattern and remove the obsolete rejection test.
* qwen3.8: support developer instructions
Qwen3.8 does not define a developer role, while OpenAI-compatible coding agents commonly send developer instructions before user messages. Fold the leading system/developer instruction prefix into a single system turn before Qwen3.8 validation, preserving instruction precedence without changing Qwen3.5 or other renderer behavior.
Add streaming tool-call integration coverage for the native Ollama, OpenAI Chat Completions, OpenAI Responses, and Anthropic Messages request shapes. Each case exercises prior assistant tool calls, tool results, follow-up rendering, and parsed tool-call output. Add Qwen3.8 to the release tools sweep.
Removes an unnecessary unit test that should not have been included in the original 3.8 PR.
* review comments
Qwen3.8 keeps the Qwen3.5 model architecture and parser, but its chat template adds reasoning-effort and preserved-thinking semantics. Detect those template markers during safetensors import, select the qwen3.8 renderer, and cover thinking, tools, continuation, and malformed parser input.
Make indexed safetensors imports use the weight map's shard names instead of independently filtering files by the model-* convention. Reject unsafe shard paths, ignore unindexed tensors, and fail when an indexed weight is missing or stored in a different shard. Retain the conservative model-* scan when no index is present.
Treat Classification.Quantize as the effective tensor format and pass it to the manifest writer. This records file_type for automatic block-FP8-to-MXFP8 conversion and recognized prequantized inputs, preserves requested quantization and base-plus-draft behavior, and avoids claiming one type for mixed or unknown formats.
Normalize both supported convolution weight layouts with an explicit reshape. Add focused unit coverage for renderer selection, parser behavior, shard inventory, manifest metadata, and convolution layout; heavyweight reference-forward and release integration checks remain bring-up artifacts.
Updates Muse Glimmer Jinja reference template to the latest publisher version and mirror its explicit-system reasoning handling in the Go renderer.
Explicit system prompts now normalize "Reasoning effort" to "Reasoning strength" and skip adding a renderer-provided reasoning line when the prompt already contains one. This prevents duplicate or conflicting reasoning directives while preserving the default-system behavior.
Add reference tests for both normalization and deduplication, including Jinja-backed validation.
Select the 3.5 parser and renderer from its checkpoint template, preserve its prompt semantics, and map medium reasoning effort to the final-user annotation expected by the reference template.
Exercise parser and renderer registration, create-time metadata inference, and exact Jinja parity so created models cannot silently fall back to the Nemotron 3 renderer.
The model occasionally emits a <|message|> boundary token in the invoke
name region, echoing the header form `to=read<|message|>`. The existing
recovery handled the tag inside a terminated name (`name="read<|message|>">`)
but not the fleet-observed shape where the tag replaces the `">` terminator
itself (`name="read<|message|><atem:parameter ...`), which failed the call
with "malformed ATEM parameter".
Replace the strip-after-cut recovery with a single name scan shared by
parseGlimmerATEM and the content fallback: the name ends at the first `">`,
boundary tokens before it are dropped, and a parameter element immediately
after a dropped token means the token replaced the terminator. Well-formed
calls are unaffected — a boundary token is never legitimate before the
terminator, and parameter values (where the literal text is preserved) only
appear after it. Murkier garbles still fail loudly, the recipient
cross-check still applies, and the recovery WARN is retained.
Add MLX support for Nemotron 3 Nano Omni, including the model implementation, Mamba2/recurrent pieces, MoE routing, and quantized NVFP4/MXFP8 expert paths.
Use a shared mapped MoE GatherQMM fast path under the generic moe_gather_qmm_mapped naming, with Metal-optimized NVFP4/MXFP8 block-mapped kernels and generic fallbacks for unsupported backends.
Serve the model's multi-token prediction head as a self-draft speculator, so speculative decoding needs no separate draft model.
Render the Nemotron prompt from the published chat template. The template the renderer was based on had drifted from the current reference; refreshing it surfaced five mismatches: stray leading newlines, the wrong turn separator and a trailing newline before the generation prompt; /think and /no_think toggles left in user turns; a trimmed system message the template leaves intact; a user block opened by a leading tool message; and Go scalar syntax for schema extras where the template applies Python str(), sending true/false/<nil> in place of True/False/None. Reference tests now render every case through the template itself.
Also harden the Nemotron parser path shared by both backends: while collecting thinking, preserve whitespace before partial </think>, <think>, and <tool_call> fakeouts, with streaming tests covering those cases.
* glimmer: implement the Muse Glimmer model
MLX model (language + vision encoder) with DFlash draft wiring, llama-server DFlash support and rope-interleave fix, renderer and parser, tokenizer fixes, and the import quantization policy.
* mlxrunner: report committed prefill chunks after the sweep and eval
The drafter's flush evaluates its report, and an eval that runs while the chunk's construction handles are still live cannot free any intermediate buffer. On media chunks that retention keeps the whole vision tower resident and grinds the Metal allocator at its limit until the request dies. Pin the report's inputs across the sweep, report after the chunk materializes, and release media items after the report so a drafter can still capture the rows its deferred flush embeds.
* ci: retry CUDA pre-release download
Update llama.cpp to pick up upstream Laguna implementation and remove Ollama's local Laguna implementation. Retain a narrow Metal-only scaling workaround for routed-MoE prompt overflow.
Translate older Ollama GGUF attention-gate and SWA metadata names so existing models continue to load.
The GLM parser buffered tool calls until it observed </tool_call>, but ignored the terminal done signal. If the model omitted or partially emitted the outer closing tag, Ollama returned a successful empty response instead of a tool call or an actionable error, leaving coding agents unable to continue.
On end-of-stream, finalize only structurally complete calls for declared tools with all required arguments. Complete calls missing only the outer delimiter now proceed through the existing parser, while genuinely truncated calls return an explicit error rather than being silently dropped.
Fixes#16497
Add a laguna-v8 renderer/parser matching the Laguna XS 2.1 template, and fix v2 handling of embedded thinking and structured tool arguments.
Prevent FP16 overflow in Metal's quantized routed-MoE prefill path by scaling the linear branch and folding the inverse into the routing scale. Other backends and token-generation paths are unchanged.
Add comprehensive v2/v8 Jinja parity and parser tests.
Incorporate the upstream Gemma4 chat template refinements for tool-calling stability, turn closure, and multi-turn reasoning. This updates the native renderer and checked-in HF template fixtures to keep adjacent assistant/tool continuations in the same model turn, add the post-tool thought-channel cue when thinking is enabled, and match Google's default of not replaying historical thinking before a later user turn.
Also preserve null tool arguments through Gemma4 rendering/parsing and extend the Jinja2 parity coverage for these upstream behaviors.
* 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>
* mlx: add laguna model support
* convert: support fp8 safetensors import
Decode HF F8_E4M3 safetensors with block scale companions into GGUF-supported tensor types, and record which output tensors came from FP8 source weights.
Use that source-precision metadata during create quantization: default FP8-sourced GGUFs to Q8_0, keep non-FP8 tensors at their original precision for Q8_0, and promote non-FP8 quantizable tensors to Q8_0 for Q4_K requests.
* ggml: add laguna model support
* server: preserve generate logprobs with builtin parsers
Generate requests were dropping logprob-only chunks whenever a builtin parser buffered visible content. Chat already handled this case, but generate only forwarded chunks with visible response, thinking, or tool-call output.
Keep generate chunks that carry logprobs even when the builtin parser has not flushed visible content yet, and add a regression test that exercises the behavior with a generic thinking parser.
* review comments - perf improvements
* ggml: implement nemotron 3 nano omni
* add poolside integration
* update poolside doc
* adapt to new cache setup
* fix test
* fix test
---------
Co-authored-by: Eva Ho <hoyyeva@gmail.com>
Following up on #15560, this change now has e2b/e4b render differently
from 26b/31b.
For backwards compatibility, we take the existing renderer name `gemma4`
and make it do dynamic resolution based on the model name/size, but the
intended use is for the models to be republished with the renderer
variant specified explicitly: `gemma4-small` or `gemma4-large`.
Gemma 4 prompts differ when thinking is disabled for different sized
models: 26b/31b emit an empty thought block, while e2b/e4b do not.
Before #15490, our shared Gemma 4 renderer effectively matched the
e2b behavior. #15490 changed it to always emit the empty thought block,
which regressed e2b/e4b nothink behavior and led to #15536 (and possibly
This change restores the previous shared behavior by removing the empty
trailing thought block. It also renames the checked-in upstream chat
templates so the e2b and 31b fixtures are tracked separately.
A follow-up will split Gemma 4 rendering by model size.
Fixes: #15536
* gemma4: update renderer to match new jinja template
Google has updated their jinja template for gemma4, and so this change
gives us parity with the new template. The parsing also slightly changed
upstream, so we make a small change to our parser as well.
I've also corrected a few probably existing edge cases, especially
around type unions. The upstream output format is weird (a stringified
array), but in practice the models seem to understand it well.
* gemma4: special case simple `AnyOf`s
The upstream template doesn't handle `AnyOf`s, but since in the previous
commit we saw type unions work reasonably well, I'm now treating very
simple `AnyOf`s as type unions to help in cases where they might be used
* fix lint
* gemma4: prefer empty instead of `None`
We can't currently distinguish between a result being not-present vs.
empty. The empty case seems more important (e.g., a legitimately empty
tool call)
* gemma4: be more careful for tool results with missing IDs
We were missing setting the function index for several models that can
make parallel tool calls.
In the future we may want to consider putting some sort of post-parse
hook and relieve the parsers of this duty.
Fixes: #15457
Daniel HiltgenandGitHub
Eva HandGitHub
Jeffrey MorganandGitHub
Jesse Gross
Devon Rifkin