Adding/Multiplying a tensor by a scalar w/ a different data type
can cause the tensor to be promoted and cause performance issues.
This change adds several guards against over-promotion.
Speculation used a parallel hierarchy of wrapper cache types that shadowed
the live caches and reconciled against them on commit. Replace it with
snapshot/restore on the live caches themselves: a cache snapshots itself as
a write crosses each offset, and the runner commits a batched draft by
restoring to the accepted count. The wrappers and the comparison plumbing
around them are gone.
Snapshots are lazy. A KV or rotating capture indexes into the live buffer and
owns no memory until a destructive write forces a copy-out, so rejecting a
draft is free.
Recurrent layers now validate in the same batched pass rather than falling
back to serial. A gated-delta layer reports its interior split offsets and
hands back the recurrent state at each one, which the cache records as a
snapshot.
CausalConv1D and GatedDelta now run their scan in segments cut at optional
WithSnapshotSplits offsets and return the recurrent state at each boundary
instead of just the final state. The output is identical to the unsegmented
scan; segmenting only adds a few kernel launches, not extra recurrence compute.
This lets a batched forward capture interior recurrent state without re-running
the scan, which the cache will use for speculative validation rollback points.
RecurrentCache.Put and the Qwen3.5 layer now thread the boundary-state slices,
committing the final entry as the live state.
* 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>
Split the gated-delta Metal/CUDA kernels' dtype template into separate
input (InT) and state (StT) types so activations can stay in bf16/fp16
while the accumulated delta state stays in float32. Allocate the delta
state and qwen3_5's no-cache zero state in float32 to match.
This reverts commit 98e26b8c37.
The DFlash integration is too invasive to keep at this stage: it
threads DFlash-specific logic through the pipeline, base model
interfaces, and the cache layer. The recurrent cache also now
has qwen3.5 model-specific code. Revert it now and reintroduce
the self-contained, generally-useful pieces (YaRN RoPE DRY-out, draft
architecture autodetection, gated-delta fp32 state) as separate
follow-up commits.
This change adds dflash block diffusion speculative decoding to the MLX runner. Included in this change:
support for qwen3.6 moe/dense speculative decoding
draft model recurrent cache playback
RoPE/YaRN changes (DRY out the laguna/dflash MoE YaRN implementation)
support for greedy sampling / leviathan/chen sampling
This change adds support for MTP (multi-token prediction) speculative decoding for the
gemma4 model family.
It includes:
* support for importing safetensors based gemma4 draft models with `ollama create`
* a new DRAFT command in the Modelfile for specifying draft models
* a --quantize-draft flag for the ollama create command to quantize the draft model
* cache support for speculation
* changes to the rotating cache to be able to handle MTP correctly
* sampling support for draft model token prediction
---------
Co-authored-by: Daniel Hiltgen <daniel@ollama.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>
Models build their own attention masks and read K/V directly from
the cache's buffers, which ties them to the cache's storage layout.
That blocks multi-sequence batching — right-padded rows need a
query-padding mask composed onto every model — and rules out
variants like paged attention where K/V isn't one contiguous tensor.
Caches now hand back a per-layer KVHistory holding post-update K, V,
and a MaskApplier that merges the cache's storage restrictions into
the model's logical mask. Models describe their mask in logical
terms; SDPA composes model, padding, and applier contributions and
dispatches to the kernel's causal or no-mask fast path when it can.
KVHistory still exposes K, V, and the composed mask for manual
attention paths (e.g. CUDA prefill at head_dim > 128).
Performance for single-sequence inference is unchanged.
Switch RoPE from the scalar-offset kernel (mlx_fast_rope) to the
array-offset one (mlx_fast_rope_dynamic) so each batch row can start
at its own position. The pipeline tracks the current position locally
and passes it to the model through Batch.SeqOffsets; each model
materializes that slice into an int32 array for the RoPE call.
Single-sequence behavior is unchanged; this is the wiring needed
before the runner can batch independent sequences.
Gives a single extension point for per-call context (positions,
sequence IDs, masks) as multi-sequence batching grows, without having
to churn every model's Forward signature again.
* mlx: Support NVIDIA TensorRT Model Optimizer import
* x/create: support FP8 safetensors import
Decode HF F8_E4M3 safetensors with block scale companions into MLX-importable tensor blobs, including compressed-tensors weight_scale metadata, packed NVFP4 layouts, and mixed-precision tensor headers.
Use that source-precision metadata during create quantization: default FP8-sourced imports to mxfp8, allow source FP8 to target MLX low-bit formats, preserve source-quantized NVFP4 layouts, selectively keep or promote tensors based on their source precision, and detect quantized dtype from mixed-precision safetensors manifests.
* review comments
Match the ollamarunner and OpenAI semantics: raw, full-vocab log-softmax
with the top-K ranked by probability. Skipped on the GPU when the request
doesn't ask for logprobs so decode doesn't pay for it otherwise.
DeepSeek-V2-style aux-loss-free routing computes sigmoid(gates) once but
needs it twice: the raw sigmoid output is gathered after top-k, while the
post-bias negation is the argpartition key. Fuse into a single multi-output
Compiled kernel returning both, saving two launches on the routing path
per token. Exposed as a general SigmoidRouter since the same pattern is
shared across DeepSeek-V2 descendants.
Improves glm4.7 generation performance by approximately 1%.
Converts SiLU/GELUApprox to compiled kernels and adds SwiGLU,
matching upstream mlx/mlx_lm's activations pattern. Routes llama,
qwen3, qwen3_5 (dense + MoE), and glm4_moe_lite MLP paths through
mlx.SwiGLU so each MLP invocation runs as one fused Metal/CUDA
kernel rather than a chain of per-op launches.
* gemma4: implement Gemma 4 model for MLX (text-only runtime)
* gemma4: two MoE + SWA prefill perf fixes
Two performance optimizations in the gemma4 forward pass
1. Memoize the sliding-window prefill mask across layers.
2. Softmax only over the selected experts in Router.Forward.
* review comments
Add QuantizedEmbedding and EmbeddingLayer interface so models can
use quantized embedding weights and expose tied output projections.
This change updates gemma3, glm4_moe_lite, llama, qwen3, and qwen3_5
to use the new interface.
* prefer rocm v6 on windows
Avoid building with v7 - more changes are needed
* MLX: add header vendoring and remove go build tag
This switches to using a vendoring approach for the mlx-c headers so that Go
can build without requiring a cmake first. This enables building the new MLX
based code by default. Every time cmake runs, the headers are refreshed, so we
can easily keep them in sync when we bump mlx versions. Basic Windows
and Linux support are verified.
* ci: harden for flaky choco repo servers
CI sometimes fails due to choco not actually installing cache. Since it just speeds up the build, we can proceed without.
* review comments
GLM models sometimes omits </arg_value> closing tags in tool call XML, causing xml.Unmarshal to fail with "element <arg_value> closed by </tool_call>".
This is a known issue across the GLM family.
Sanitize the input to fix closing arg_key values so encoding/xml can handle it.
This change adds support for qwen3.5-next-moe models (qwen3-next/qwen3.5-next/qwen3-coder) to the MLX runner. It also:
* introduces recurrent cache support and related MLX ops
* updates pipeline/runner integration and adds tests
* properly quantizes stacked expert tensors
* a Gated Delta Metal kernel for fast SSM inference
* adds new MLX calls for Conv1d, DepthwideConv1d, Contiguous, Exp, Log, SoftmaxAxis
Currently, context length is unbounded - the cache will keep
growing forever independent of the model's trained context
length. This caps it and enforces semantics similar to most
cloud services:
- Long prompts will result in an error, not truncation.
- Generation that exceeds the context will be stopped
The previous approach tracked array lifecycles through reference
counting, where each array recorded its inputs and a reference count
that was decremented as dependents were freed. This is not really
necessary as MLX tracks references internally. It is also error
prone as it is easy to create new arrays and forget to free them
when the Go variable goes out of scope.
Instead, we can pin just the arrays we want (typically outputs and
specific intermediates, like the cache). All other arrays are freed
by default when we run sweep. This avoids most causes of memory leaks
while still giving the freedom to save what we want.
This change adds a new x/tokenizer package which includes:
* New BPE and SentencePiece tokenizers
* Removing the dependency on the imagegen tokenizers
* Fixes to multibyte decoding in the pipeline
* Various correctness and benchmark tests
Not included in this PR is the WordPiece tokenizer for BERT models which will be
added when we add embedding models. The imagegen tokenizers will also be removed in
a follow-up PR.
This change adds a new MLX based runner which includes:
* Method-based MLX bindings
* Subprocess-based MLX runner (x/mlxrunner)
* KV cache with tree management
* A basic sampler
The GLM4-MoE-Lite model has been ported to use the new bindings.
---------
Co-authored-by: Michael Yang <git@mxy.ng>
