Qwen3.5/Qwen3-Next architecture strings contain the substring "qwen3", so the
broad qwen3 match claimed them for the generic parser and qwen3-coder
renderer, whose template doesn't frame the thinking block — an empty
<think></think> leaked into content and think=false was ignored. Match the
family first via isQwen35Family so the parser, renderer, and
thinking-capability checks share one variant list.
* llm: allow iGPU mmproj offload with fit padding
llama.cpp's fit pass sizes text-model placement before the multimodal projector is loaded. Ollama had been avoiding that risk on non-Metal iGPUs by disabling projector offload entirely, which forces CLIP onto CPU on GB10 and Strix Halo even when the projector has ample memory available.
Let integrated GPUs use the same projector-memory check as other GPUs. When projector offload is enabled, add the estimated projector memory plus the existing 1 GiB headroom to Ollama-owned LLAMA_ARG_FIT_TARGET so fit leaves space for the later projector allocation. If Ollama/device setup already supplied a fit target, add the projector pad to it. If the user set LLAMA_ARG_FIT_TARGET explicitly, leave it exactly as provided.
Fixes#16419
* review comments
This is a rewrite of the create functionality for the MLX engine.
The core idea behind the create functionality is to break the import/convert into a pipeline of distinct phases:
* Read (scan the safetensors directory for the various bits of metadata)
* Classify (determine what the import type)
* Plan (determine any transforms that need to be done)
* Write (transform any data as necessary and write out the blobs)
* Create the manifest
Each architecture has a "policy" which determines how to convert the model correctly. A number of different formats for safetensors are supported including:
* nvfp4 (two formats: model optimized, torch)
* fp8 datatypes (convert to mxfp8)
* standard bf16 based weights
A number of cleanups/simplifications have been done including:
* using the baked in names for the tensors instead of munging them into something else
* unified 3d expert tensors (instead of separate per expert tensors)
* fewer unnecessary transforms to the various tensors in a model (keep a model as close to the source as possible)
* unified capability checking
* draft model handling (for MTP) is done on the same path
Image generation has been intentionally removed.
Recent upstream Pascal kernel fixes let us compile native SM60/SM61 kernels again instead of relying on PTX JIT, so allow Flash Attention auto at runtime for CC 6.x devices.
Fixes#16591Fixes#16754
Use ggml_fopen for compat tensor reads so Windows paths with Unicode characters are converted through the same UTF-8-to-wide path as llama.cpp model loading.
Fixes#16493
The presets and docs had fallen out of sync with what our current ROCm versions on Linux and Windows actually support. We rely on Vulkan now to cover these older unsupported devices.
* discover: use the SBSA CUDA build on JetPack 7 (L4T r38+)
JetPack 7 supports SBSA-based CUDA, so the standard cuda_v13 build — shipped
in the base linux-arm64 package, and given the Orin arch (CC 8.7) in #16628
— runs on these devices.
JETSON_JETPACK=7 previously selected a nonexistent jetpack7 runner, so
runner.go skipped every CUDA library and discovery fell back to CPU. The L4T
releases JetPack 7 uses (r38 on Thor, r39 on Orin) also hit the unrecognized
branch, and install.sh warned the version was unsupported. Map JetPack 7+
(L4T r38 and newer) to cuda_v13 (returned as "" from cudaJetpack); no
Jetson-specific download is needed, so install.sh no longer warns.
Fixes#16602
* discover: fall back to standard CUDA when the JetPack runner is absent
Per review, drop the L4T-version mapping (in cudaJetpack and install.sh) and
instead clear the jetpack override in runner.go when the detected cuda_jetpack
runner isn't installed. Normal discovery then selects the standard cuda_v13
build, which supports Orin (CC 8.7) on JetPack 7.
This change allows .experts.gate_proj / .up_proj / .down_proj tensor names to each
be used for both quantized (i.e. nvfp4 and mxfp8) and non-quantized (bf16) models.
Previous to this only non-quantized models used that tensor naming scheme.
Parser.done() counted the tag's open/close characters ({}, []) without
tracking JSON string context, so a streamed tool call whose string
argument value contained a closing brace or bracket (e.g.
{"code": "if (x) { y }"}) was treated as complete too early and flushed to
the user as plain text instead of being parsed as a tool call.
findArguments() in the same file already tracks string context; apply the
same handling in done() so open/close characters inside string values are
ignored.
In the PS output, expose the scheduler selected size (clamped by model context size) instead of always reporting the model max context. This will help provide a hint to clients to keep the context size below this value to avoid paging and poor performance on smaller VRAM systems.
Our cuda_v12 build requires nvcc fatbin compression, which in turn requires driver 550 or newer. This change filters incompatible CUDA devices based on the runtime and driver version. This allows users to build from source with older toolkits to support older drivers.
Fixes#16449
* server: align generate with native chat templates
/api/generate rebuilt chat-like prompts through the Go template path even when the model selected its native GGUF Jinja chat template, so the same model rendered differently between generate and chat.
Route chat-like generate requests through the shared native chat preparation path, keep deprecated context and image handling working there, and keep explicit OLLAMA_GO_TEMPLATE overrides intact.
Fixes#16792
* review comments
Fall back to "{{ .Prompt }}" when lacking templates
* llm: fix ollama ps double-counting mmap'd weights on partial offload
With mmap enabled, llama-server reports each CPU_Mapped model buffer as the
file-offset span of its CPU-resident tensors. During partial offload that span
covers nearly the whole file because the first and last tensors stay on CPU, so
the parsed buffer sizes count the offloaded weights twice and ollama ps shows
roughly 2x the real size with a false CPU/GPU split. Model weights can never
exceed the model file on disk, so trim the excess over the file size from the
mmap-backed portion when computing MemorySize. This makes the reported size
independent of use_mmap; VRAM accounting and scheduler placement are unchanged.
* llm: exclude repacked model buffers from the mmap overlap trim
The trim that corrects mmap double-counting computed the overlap from all
model buffers, including real copies such as CPU_REPACK. On a CPU-only
repacked model that inflated the excess and trimmed the repack out,
undercounting by the repack size (llama3.2 reported ~1918 MiB instead of
~3218 MiB).
Compute the overlap from file-backed buffers only: mmap views and direct
device copies, whose spans can overlap the file on partial offload.
Repacked or host-pinned CPU copies are separate allocations that never
overlap the on-disk weights, so leave them intact. Adds a CPU_Mapped +
CPU_REPACK regression test and corrects the Metal case to the real total.
Bump MLX to the latest selected upstream ref and update the MLX/imagegen
wrappers and tests for the new API behavior.
Fix the CUDA MLX archive so runtime NVRTC kernels work after deployment:
package CUTE/CUTLASS headers, include the CUDA runtime header closure, and
stage a coherent CUDA-toolkit-matched CCCL tree instead of MLX's fetched CCCL
for CUDA payloads. The previous archive could build successfully but crash at
runtime due to missing or incompatible JIT headers.
Existing qwen2.5vl GGUFs can contain an empty qwen25vl.vision.fullatt_block_indexes array. The compat layer translated the projector metadata but left clip.vision.n_wa_pattern unset, causing llama-server to fail loading the CLIP model.
Default the runtime compat value to the standard Qwen2.5-VL pattern when the key cannot be derived, and make the converter emit the same default for nil or empty fullatt block metadata.
Fixes#16540
* llm: size mmproj offload by projector memory
Replace the blanket 10 GiB VRAM cutoff with a projector tensor-size estimate plus backend headroom, while preserving the existing CPU-only, partial text offload, shared-memory GPU, and startup OOM retry gates.
This is a stopgap until fit accounts for mmproj memory directly.
The same limited-vram path appears in the qwen3.5 vision hang report: the logs show --no-mmproj-offload on a 7.5 GiB RTX 5050 with about 6.4 GiB free while llama-server estimates the inline mmproj at about 962 MiB.
Fixes#16496Fixes#16570
* review comments
The llama_cuda_v13_windows preset in llama/server/CMakePresets.json was missing sm_86 and sm_80 architectures, causing RTX 3060 laptop and similar mobile RTX 30-series GPUs to be skipped during runtime GPU detection on Windows with CUDA 13. The Linux preset (llama_cuda_v13_linux) included these architectures as "86-virtual" and "80-virtual", but the Windows preset only had "75-virtual;89-virtual;100-virtual;120-virtual", excluding Ampere mobile GPUs.
Signed-off-by: anish <anishesg@users.noreply.github.com>
Co-authored-by: anish <anishesg@users.noreply.github.com>
ollama launch codex-app sets root-level model_provider = "ollama-launch-codex-app"
in ~/.codex/config.toml to route requests through the local Ollama server.
In Codex, model_provider is a global config key, there is no per-model provider
in the catalog schema (ModelInfo has no model_provider field), so it applies to
every model, not just Ollama ones.
When a user switches to a built-in OpenAI model (e.g. gpt-5.5) in the Codex App
UI, the UI writes model = "gpt-5.5" to config.toml but does NOT update
model_provider. The root model_provider stays "ollama-launch-codex-app", so the
OpenAI model request goes to http://localhost:11434/v1/responses instead of
OpenAI API, resulting in a 404 ("model gpt-5.5 not found"). The user is
stuck: OpenAI models silently route to localhost until they know to run
"ollama launch codex-app --restore".
Fix: CurrentModel() now verifies the configured model appears as a slug in the
Ollama-managed catalog before reporting the integration as active. When the
model has drifted (user selected a non-Ollama model in the UI), CurrentModel()
returns empty, so the launcher accurately shows the integration as inactive and
the user is directed to restore or re-launch.
The heuristic schedule grew the draft toward a fixed cap on acceptance alone,
maximizing accepted-tokens-per-step rather than throughput, and on a
steep-forward target it regressed below no speculation. Replace it with an
engine-level controller that drafts the depth maximizing
committed-tokens-per-wallclock from live per-position acceptance and persisted
per-width forward cost, with no draft-length cap; the heuristic schedule and
the OLLAMA_MLX_MTP_* env vars go with it.
Acceptance took two blocking evals per round: one to read the accepted mask,
then a second for the bonus or residual token whose graph needed the
host-known rejection point. Sample the residual at every rejection point in
one batched draw alongside the bonus row, so a single eval covers acceptance
and the next token.
Each speculative round ran the target stack twice — once for the current
token's hidden and base logits, once to validate the drafts — capping
throughput below plain decode. Fuse them into one forward over [current,
draft_0..draft_{N-1}], whose hidden rows already line up with the acceptance
math, so the separate base-logits unembed disappears from the drafted path.
Sampler.Distribution built row i as if draftTokens[:i] were appended, leaving
a single-row proposal call with no draft history, so a drafter skipped the
repeat/presence penalties the target's validation applies and re-proposed
penalized tokens. Align rows with the end of the draft chain instead: the
final row sees every draft token, each earlier row one fewer.
Generalize the draft path so a head that maintains a KV cache (EAGLE-style)
and Gemma's read-only single-position assistant both fit one drafter
interface with no per-model branches, and make the committed stream the
drafter's maintenance mechanism — every committed run is reported, the
drafter pairs each draft slot with its look-ahead token and flushes completed
pairs to the draft caches. The draft KV thus stays prefix-cached alongside
the target in every session, drafting or not.
The pipeline and the MTP decoder each owned a decode loop with duplicated
prefill, budget, and emission handling. Split the pipeline into prefill and
decode phases behind a decoder interface, with the decode loop the sole
emitter enforcing the NumPredict budget, and split speculation into a generic
engine that returns the accepted run and a drafter interface that owns only
how proposals are made.
Greedy is a special case of sampled decoding — at temperature 0 the sampler
yields a point mass, so rejection-sampling acceptance reduces to argmax-match
— so collapse the separate greedy, sampled, and serial paths into one. MTP
now honors any temperature, penalty, and top-k/p/min-p setting; logprobs
remain the only gated feature.
On Windows hybrid-graphics systems (Intel iGPU + NVIDIA dGPU), discovery
could classify the integrated GPU as discrete and the discrete GPU as
integrated, dropping the dGPU's Vulkan device and scheduling models onto
the iGPU's shared system RAM (#16667). Two index-keyed correlations
between independently-ordered device enumerations caused this:
1. The native probe's stderr was concatenated into the output passed to
parseVulkanUMA. The probe enumerates Vulkan devices in its own order,
so its ggml_vulkan uma lines overwrote llama-server's index-keyed UMA
map with inverted values. Parse UMA metadata only from llama-server's
own output.
2. applyWindowsVulkanRefinement required the raw vkEnumeratePhysicalDevices
count to equal llama-server's Vulkan device count. The raw enumeration
is a superset on real systems (D3D12 mapping-layer devices, Microsoft
Basic Render Driver), so the refinement that reads the authoritative
VkPhysicalDeviceType was always skipped. Match devices by name against
the probed superset instead, bailing only when a device has no match or
matches conflicting device types.
Verified on the hardware from #16667 (Intel RaptorLake-S + RTX 4080
Laptop): the raw probe returns 5 devices vs llama-server's 2; with this
change the iGPU is dropped as integrated, the dGPU's Vulkan device
dedupes against CUDA0, and the model loads on the dGPU with no
environment overrides.
Fixes#16667
This PR separates prompt caching from the public shift request option for native llama-server requests.
Previously, shift controlled two different mechanisms:
context shifting / overflow behavior
per-request llama-server cache_prompt
That meant callers could not request shift: false without also disabling prompt caching.
Fixes#16635
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.
The batched MTP accept paths advance the cache by the whole accepted run
before streaming it to the client. If the stream was cancelled partway
(e.g. the caller disconnects), the loop returned before recording the
remaining accepted tokens, leaving the cache offset ahead of
session.outputs. close() then indexed the token log past its end and
panicked with a slice-bounds error.
Record the whole run to session.outputs before streaming any of it, so a
cancelled stream can no longer desync the cache from the token log.
The same bug is present on main, with identical mechanics: the accept
paths there commit the cache to before+accepted and then stream in a loop
that returns on cancellation before recording the rest.
Prefill no longer splits its batch at each requested snapshot offset. The
session schedules the pending offsets on every cache before prefill, runs the
forward in full-size chunks, and attaches the captured snapshots to the trie
afterward. Offsets the prefill never crosses (it leaves one token for decode
seeding) are dropped instead of materializing a node for tokens never written,
and snapshots from an abandoned prefill are released on session close.
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.
cache.go had grown to hold every cache kind. Move KVCache (and its
speculative wrappers) to kvcache.go and RotatingKVCache (and its
sliding-window mask applier) to rotating.go, leaving cache.go with the
shared interfaces and the Speculation transaction. Pure relocation;
no behavior change.
Work that panics on the locked MLX worker goroutine was recovered and
re-raised on the caller, so the printed trace pointed at the re-panic
site in this package rather than the code that actually panicked.
Capture the worker stack at recovery and carry it through a value that
implements error, so the runtime prints the original location in the
fatal trace.
Add support to launch the hermes-desktop app alongside the hermes agent from ollama launch. It will go through the install on first run if hermes-desktop is not already installed.
Bump llama.cpp to b9509, which includes the upstream Gemma 4 12B multimodal projector fixes for the n_head=0 divide-by-zero crash seen on x86/CUDA/Linux/Windows.
Fixes#16479Fixes#16489Fixes#16491Fixes#16492Fixes#16495
Windows installer and app cleanup could leave llama-server.exe running when ollama.exe was killed directly, so cleanup now includes llama-server.exe and taskkill /T.
llama.cpp b9478 added a default 30s SSE ping that emits colon-only comment frames (":\n\n") while streamed requests are idle; Ollama treated non-data SSE lines as JSON, so skip SSE comments in completion and chat streams.
* llama: add laguna (poolside) arch via a llama.cpp patch under llama/compat/models
The pinned llama.cpp does not include poolside Laguna yet. Add it as an Ollama-owned source file plus a small registration patch under llama/compat/models/. apply-patch.cmake now applies every *.patch under llama/compat/ (the hooks patch plus each arch patch), so adding an architecture only adds files under llama/compat/models/ and needs no new cmake.
* cleanup patch to keep windows happy
---------
Co-authored-by: Jeffrey Morgan <jmorganca@gmail.com>
llama-server model loads could time out after the fixed load duration even while tensor-loading progress dots were still being emitted, so track raw runner output activity and use OLLAMA_LOAD_TIMEOUT as a stall deadline.
Fixes#16416Fixes#16412
Default integrated GPU filtering dropped the supported ROCm gfx1151 Radeon 8060S unless OLLAMA_IGPU_ENABLE was set, so add a ROCm gfx-target allowlist with gfx1151 as the first admitted target. This iGPU is a known-good iGPU.
Fixes#16423
Local model metadata from /api/tags can include a context length without a max output limit, so omit OpenCode limit stanzas unless an output limit is known.
This preserves the pre-0.30 OpenCode behavior: local models did not receive a limit stanza because /api/tags did not expose context length, while cloud models still emit complete context/output limits.
Fixes#16424
This cleans up the capabilities logic so we can log more information about the various options we consider as well as the final template version we use.
* llama-server followups
Misc fixes for #16031
- Add back dropped ROCm build flag for multi-GPU support on windows
- Fix amdhip64_*.dll version detection for "latest" selection
- Fix embeddings API for consistent normalize behavior with prior versions
* ci: set up for automated llama.cpp update testing
* reduce batch for fa-disabled, and constrained vram
* mlx: fix v3 load bug on m5
Imagegen was incorrectly loading v3 first. This DRYs out the loading code so imagegen gets the same new v4/v3 selection logic.
* fix reload bug on embedding models
* bump version
* steer user how to enable iGPU when disabled
This change addresses some problems with GGUF conversion including:
* correctly naming the MoE tensors
* correctly quantizing the nextn.eh_proj.weight MTP tensor
* 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>
This change updates the show API for MLX models to:
* display the correct quantization in mixed precision models
* not display the global_scale scalar value
* not duplicate the `tools` capability
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.
Previously the draft architecture was hardcoded to
Gemma4AssistantForCausalLM. Read it from the draft model's config so
any draft architecture can be packaged.
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.
* Reduce startup model hydration
Add a lightweight model list cache for tags and launch inventory, while keeping show cache population lazy. This avoids loading every local model at startup on large model stores.
* harden flaky scheduler unit test
* remove extra launch model metadata text
* review comments
* review comments
* ci: speed up release builds
This should help speed things up for release. It also will help
speed up local developer builds a little.
* ci: dedup linux build steps and optimize
* review comments
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
* mlx: rework the MLX sampler
Replace the MLX sampler transform chain with an explicit distribution pipeline that applies:
1. penalties
2. top-k
3. temperature/softmax
4. top-p
5. min-p
6. normalize
7. categorical
The common top_k path now keeps sparse [B,K] token ids/probabilities on GPU instead of carrying full-vocab
scores, and sampled MTP reuses those draft/target distributions for acceptance, bonus, and residual sampling.
This change also fixes the seed parameter so that temperature sampling and sampled MTP are reproducible.
MLX compiles the AIR objects with the requested -mmacosx-version-min, but its final metallib step invokes metal instead of metallib. With the macOS 26 SDK, that can stamp the Metal v3 library with a macOS 26 deployment target.
Relink the generated AIR files with metallib before install until this is fixed upstream.
The MLX runner now routes model work through a locked worker thread. Status also used that worker only to sample memory, so a scheduler health ping could sit behind long prefill or generation until its 10s context expired, causing /v1/status to return 500 and the server to treat the runner as unhealthy.
While Metal doesn't change VRAM reporting, CUDA does. Cache the last memory sample and make status perform only a short best-effort refresh. If the worker is busy, status returns the cached value while a single background refresh continues and updates the cache when the worker becomes available. The in-flight guard and lifecycle context keep this from spawning unbounded refreshes while preserving live VRAM refresh behavior for CUDA.
Fixes#16081
* app: harden update flows
This hardens the windows update flows and adds a new opt-in and CI triggered unit test to verify Mac/Windows updates with verification.
* test: harden unit tests for OLLAMA_MODELS being set
* app: harden updater
* test: integration test hardening
Improve reliability on slower systems, and some flakes. Fix
a few logic flaws on the newer tests, general hardening.
* tighten up vision logging
* add new models
* remove some older models - still covered by library scenarios
* mlx: refined model push behavior
Refine the algorithm for parallel push of safetensors based models to get
better reliability and throughput.
* review comments, hardening, and performance tuning for slow links
* review comments
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>
* Update MLX and MLX-C
* Run MLX CGO work on a locked OS thread
MLX now relies on OS-thread-local execution state for streams, encoders, and caches. Add an mlxthread executor backed by runtime.LockOSThread and route runner initialization, model load, inference, status memory reads, and cleanup through the worker so Go goroutine migration cannot split MLX state across native threads.
Also stop caching default MLX streams before the runner owns the thread and add worker/threaded MLX regression tests.
* mlx: use common status writer
* mlx: bundle missing libjaccl on arm64
Inspired by #15793
* review comments
Replace the hardcoded FEATURED_MODELS list with the
/api/experimental/model-recommendations endpoint so the picker stays in
sync with server-driven recommendations. Inline the merge into useModels
(recommendations first, then the rest of /api/tags) and drop the
standalone mergeModels util.
* metal: harden for ggml initialization failures
ggml_metal_device_init performs a probe to verify the tensor API compiles. On
some systems this passes, even though kernel coverage isn't complete, which
results in a later crash when compiling the real kernels. This change adds a
single retry if any of the error strings match this failure mode to disable the
tensor API. It also hardens an error case in the Go initDevices to detect
device initialization failures and panic instead of crashing later on a nil
array entry.
Fixes#15734
* review comments
* review comments
* 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
Use the current fragment offset when emitting unmatched spans during multi-regex BPE splitting. This avoids duplicating earlier prompt text and inflating token counts for multi-stage BPE tokenizers.
Register sequences with Add/Remove; each Sample call takes any subset of
registered slots and samples one token per row, appending to each slot's
ring-buffer history. When all slots share Options and penalty rings are
full, one fused transform pass runs over the whole batch via a persistent
pooled history tensor; otherwise calls fall back to per-slot serial
processing indexed against the same pool.
Performance is unchanged for a single sequence, which is all that is
exposed for now.
AppendToken used to concatenate the new token onto the history tensor
and slice it back to RepeatLastN every decode step, churning the graph
shape and reallocating a fresh tensor each call. The stateful penalties
don't care about order within the window, so a fixed-capacity ring with
one SliceUpdate per append keeps the tensor shape constant across
steps.
Move tokenization out of the single GPU processing goroutine and
into each request's HTTP handler goroutine. This allows the next
request's prompt to be tokenized on the CPU while the current
request is executing on the GPU.
Use atomic.Int32 for Array.pinned and a sync.Mutex for the global
arrays slice so MLX arrays can be created and pinned from multiple
goroutines without racing on those structures. Convert Array value
receivers to pointer receivers and struct fields from Array to
*Array to avoid copying the atomic.
This does not fully achieve thread safety even when building
completely independent graphs. The tracing flag and traceScratch
slice in compile.go are unprotected, so concurrent Compile calls
will race. MLX itself is not fully thread-safe either although
it is working to improve.
* app/ui: fix model picker showing stale model after switching chats
Optimistic messages created during streaming were storing the full
Model object instead of the model name string. When switching back
to a chat with cached streaming data, the restore effect read an
object where it expected a string, causing the model picker to fail
matching and remain stuck on the previous chat's model.
* app/ui: fix two more instances of Model object passed as model name
Fix the same bug at lines 523 and 536 in the assistant_with_tools
event handler, where selectedModel (object) was used instead of
selectedModel.model (string).
launchInteractiveModel was introduced in PR #14609 without the
client.Show() capability-detection block that RunHandler uses.
This left opts.MultiModal always false in the TUI path, causing
image/audio file paths to always be treated as unknown commands
instead of being loaded as multimodal attachments.
Mirror the Show() call, pull-on-404 fallback, cloud auth handling,
and MultiModal/Think population from RunHandler into
launchInteractiveModel.
Fixes#15711
When both filters are active, avoid paying for a full sort in top-P
and a partial sort in top-K. Single-filter paths are unchanged.
Improves generation throughput on gemma4:e4b by 1.5%.
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%.
If you have a long running create, and start another ollama server with the
same model dir, the GC algorithm deletes the pending blobs and breaks the
create. This adds a 1h grace period to avoid deleting in-flight creation
operations.
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`.
After the rotating buffer has wrapped (c.offset > c.maxSize) a subsequent
L>1 Update() went through a slice-to-[0, c.idx) path that discarded all
slots in [c.idx, Dim), losing the older-but-still-in-window tokens the
first Q of the new batch needs for its sliding-window attention.
Linearize the circular buffer to logical order in that wrapped case so
the existing trim + concat preserves the last (maxSize - 1) old tokens.
When the buffer has not yet wrapped (c.offset <= c.maxSize), slots
[c.idx, Dim) are grow padding or stale post-rewind data, so keep
dropping them.
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.
Wraps MLX's mlx_compile API so Go functions can be traced into fused
kernels. Contiguous elementwise chains collapse into a single
Metal/CUDA kernel instead of launching one per op.
Exposes Compile plus arity helpers (Compile1/2/3) that mirror Python's
@mx.compile decorator shape, lazily building the closure on first call
so package-level declarations work before the MLX dylib loads.
* 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
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
For some versions of Xcode, cmake builds are failing due to header problems in
cross-compiling during the generate phase. Since generate is producing arch
independent generated output, we can skip this during cross-compiling.
* mlx: add op wrappers for Conv2d, Pad, activations, trig, and masked SDPA
Add Conv2d, flexible Pad (with axes/mode), PadConstant, Maximum,
Minimum, Softplus, ReLU, GLU, Clamp, Sin, Cos, Clip,
ScaledDotProductAttentionMasked, and RoPEWithFreqs. Refactor
RoPEWithBase to delegate to RoPEWithFreqs.
* review comments
* mlx: fix ScaledDotProductAttentionMasked to consult the mask argument
Improve the MLX model creation pipeline with several model-agnostic changes:
- Rewrite supportsVision to use vision_config instead of architecture name
- Add supportsAudio for audio encoder detection
- Add alignment checking (isAligned) for quantization group sizes
- Support per-projection mixed quantization in MoE expert packing
- Record per-tensor quant metadata in safetensors blobs
- Parse per-tensor quant metadata at model load time
- Validate quantize output is non-empty before storing
- Fix pin/unpin cleanup in expert group quantization
- Promote v_proj/k_proj/down_proj to INT8 for INT4 base quant
- Add MetalIsAvailable() utility
- Skip audio encoder tensors from quantization
* 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
Update cloud and local model recommendations to match current
models.go: add qwen3.5:cloud and glm-5.1:cloud, replace glm-4.7-flash
with gemma4 and qwen3.5 as local options.
Add documentation for Hermes Agent by Nous Research, covering
installation, Ollama setup via custom endpoint, messaging configuration,
and recommended models.
This change fixes two issues with Modelfiles:
1. If a user uses `ollama show --modelfile` to show a safetensors based
model, the Model would leave the "FROM" field blank which won't allow
a user to recreate the model. This change adds the model's current
canonical short name to the FROM field.
2. If a user uses the `/save` command in the CLI any messages which were
saved in a previous model wouldn't get saved (only the set of messages
from the current session).
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>
* pull: refine safetensors pull
- Body drain in resolve() — drain response body before close so Go's HTTP
client can reuse TCP connections instead of opening a new one per blob
(1,075 extra TCP+TLS handshakes eliminated)
- Skip speed recording for tiny blobs (<100KB) — prevents
HTTP-overhead-dominated transfer times from poisoning the median, which the
stall detector uses to cancel "too slow" downloads
- Resume support for large blobs (>=64MB) — on failure, preserves partial .tmp
files; on retry, re-hashes existing datak and sends Range header to download
only remaining bytes; gracefully falls back to full download if server returns
200 instead of 206; SHA256 verification catches corrupt partials
* harden push
- Prevents killing TCP connections after every request.
- Stronger backoff to handle server back-pressure and rate limiting
- Larger buffered reads for improve safetensor upload performance
- Better error message handling from server
- Handle 201 if server says blob exists
- Fix progress reporting on already uploaded blobs
- Trace logging to help troubleshoot and tune going forward
* review comments
* review comments
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
* create: Clean up experimental paths
This cleans up the experimental features, and adds both unit and integration test coverage to verify no regressions.
* create: preserve config and layer names when creating from safetensors models
When creating a model FROM an existing safetensors model, ModelFormat,
Capabilities, and layer Name fields were lost. ModelFormat stayed empty
because it's only set from GGML layers (which safetensors models lack),
and layer names weren't copied in parseFromModel. This caused derived
models to fail loading ("config.json not found in manifest").
* review comments
* mlx: Improve M5 performance with NAX
This modifies the Mac release to now have 2 builds of MLX for broader
compatibility while supporting the latest M5 hardware features. NAX requires
building with xcode 26.2 and targetting support only for OS v26 and up. Since
we want to support older MacOS versions as well, we now need 2 different MLX
builds and runtime detection logic to select the optimal version. The newer
build will detect NAX missing at runtime, so it is safe to run on pre M5 macs.
* mac: prevent generate on cross-compiles
For some versions of Xcode, cmake builds are failing due to header problems in
cross-compiling during the generate phase. Since generate is producing arch
independent generated output, we can skip this during cross-compiling.
The existing strict gemma4 tool parser is still the primary path, but if
this fails, we try to repair by fixing some of the most commonly seen
mistakes these models seem to make in practice.
We repair by building up a set of candidates, and use the first candidate
that parses.
Repairs cover:
- missing Gemma string delimiters
- single-quoted string values, including a dangling Gemma delimiter
- raw terminal string values (if the corresponding tool schema indicates
it should be a string)
- missing object close only after a concrete repair
Add regression coverage for malformed tool calls from issue #15315 and
focused unit tests for the individual repair helpers and candidate
pipeline.
We've observed Gemma 4 occasionally emitting extra <tool_call|> tags
after a valid tool call. We suppress leading close tags in this
immediate post-tool-call state so the extra close tags do not leak into
assistant content. The tradeoff is that if the model intentionally
begins its next content span with the literal string "<tool_call|>", we
will erroneously treat it as noise and drop it.
Replace the custom Gemma4 argument normalizer with a stricter
reference-style conversion: preserve Gemma-quoted strings, quote bare
keys, and then unmarshal the result as JSON.
This keeps quoted scalars as strings, preserves typed unquoted values,
and adds test coverage for malformed raw-quoted inputs that the
reference implementation rejects.
cublasGemmBatchedEx fails during graph capture when pool allocations
return fake pointers. This is triggered when NUM_PARALLEL is greater
than 1 for models like gemma4 that use batched matmuls. Skip it
during reservation since the memory tracking is already handled by
the pool allocations.
Fixes#15249
* model/parsers: fix gemma4 arg parsing when quoted strings contain "
Fixes: #15241
* add more tests, be careful about what we escape
We want Windows-style paths to not get misinterpreted
* fix backslash-quote case, it really should be a literal backslash
h/t to @chathaway-codes for pointing this out!
Co-Authored-By: Charles H <2773397+chathaway-codes@users.noreply.github.com>
---------
Co-authored-by: Charles H <2773397+chathaway-codes@users.noreply.github.com>
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.
* tokenizer: add byte fallback for SentencePiece BPE encoding
When BPE merging produces tokens not in the vocabulary, fall back to
encoding each UTF-8 byte as <0xHH> byte tokens instead of silently
dropping the character. Also teach Decode to convert <0xHH> tokens
back to raw bytes.
Fixes#15229, fixes#15231
* tokenizer fixes
* 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>
Previously we were accidentally using different clients/UAs depending on
whether it was an inference call or a different call. This change makes
them consistent, other than the timeout being different.
* tokenizer: add SentencePiece-style BPE support
Add WithSentencePieceNormalizer option to BytePairEncoding for models
that use BPE with SentencePiece-style space markers (space to/from
U+2581).
NewBytePairEncoding is unchanged; the new NewBytePairEncodingWithOptions
constructor accepts BPEOption functions. Decoding handles the reverse
mapping of U+2581 back to spaces.
* review comments
Replace hardcoded Encode(prompt, true) with
Encode(prompt, r.Tokenizer.AddBOS()) so the pipeline respects each
model's tokenizer configuration.
Models with add_bos_token=true (gemma3, llama): unchanged, tokenizer
still prepends BOS.
Models with bos_token=null (qwen3, qwen3.5): unchanged, the BOS
guard (vocab.BOS >= 0) already prevented prepending regardless of
the flag.
This aligns the pipeline with the /v1/tokenize endpoint which already
uses Tokenizer.AddBOS().
pullModelManifest unmarshals the registry response into a Go struct
then re-marshals with json.Marshal before writing to disk. When the
registry's JSON formatting or field ordering differs from Go's
output, the local SHA256 won't match the registry's
Ollama-Content-Digest header, causing false "out of date" warnings.
Preserve the raw bytes from the registry response and write them
directly to disk so the local manifest is byte-for-byte identical
to what the registry serves.
* anthropic: fix empty inputs in content blocks
When we switched to `api.ToolCallFunctionArguments`, `omitempty` stopped
doing what we were relying on it for before. This would cause non-tool
content blocks to have an `"input": {}` field, which doesn't match our
old behavior.
* use omitzero instead
The staleness check compared the local manifest digest (SHA256 of the
file on disk) against the registry's Ollama-Content-Digest header.
These never matched because PullModel re-serializes the manifest JSON
before writing, producing different bytes than the registry's original.
The fallback comparison (local modified_at vs upstream push time) was
also broken: the generated TypeScript Time class discards the actual
timestamp value, so Date parsing always produced NaN.
Fix by moving the staleness comparison server-side where we have
reliable access to both the local manifest file mtime and the upstream
push time. The /api/v1/model/upstream endpoint now returns a simple
`stale` boolean instead of raw digests for the frontend to compare.
Also adds User-Agent to the CORS allowed headers for dev mode.
A stop-gap for now to guide users better. We'll add more in-depth recommendations per integration as well.
---------
Co-authored-by: Parth Sareen <parth.sareen@ollama.com>
Add periodic snapshots every 8k tokens and near the end of the prompt
so that long prompts can be partially restored and thinking/generation
can be retried without full reprocessing.
Update LRU last used time just on the nodes that actually used
during processing rather than all snapshots along the path. This
allows eviction to remove nodes more accurately so we can avoid
other heuristics to auto-merge nodes.
mlx.Copy shares the backing buffer with its source (via
copy_shared_buffer) rather than allocating independent storage.
When used to snapshot a slice of the KV cache, the snapshot array
holds the entire original cache buffer alive through the shared
data pointer — even after eval detaches the computation graph.
Replace Copy with Contiguous in Snapshot and Split. Contiguous
allocates a compact buffer when the source buffer is significantly
larger than the logical slice (Contiguous::eval checks
buffer_size > nbytes + 16384), which is always the case for KV
cache slices.
Copilot Chat prefers to use `general.basename` in the built-in Ollama
integration, but this name isn't usually shown directly to users (and
there may be many models that share this name). Instead we pass back
`req.Model`, which for this extension is the value that we return from
`/api/tags`
* integration: improve ability to test individual models
Add OLLAMA_TEST_MODEL env var to run integration tests against a
single model.
Enhance vision tests: multi-turn chat with cached image tokens, object
counting, spatial reasoning, detail recognition, scene understanding, OCR, and
multi-image comparison.
Add tool calling stress tests with complex agent-style prompts, large
system messages, and multi-turn tool response handling.
* review comments
Previously, a partial match within a node's edge would truncate the path
to the parent snapshot - effectively making all cache types behave as
recurrent caches. Caches with only transformer layers can rewind to
arbitrary boundary so this restores this capability to improve cache
hits
* mlx: update to HEAD on 3/23
Also fixes a few misc vendoring bugs uncovered with this first update.
This also renames the version files to make them clearer.
* CUDA Fast Gated Delta kernel
* mlx: detect eval errors and panic
On model errors or missing kernels, don't mask the error, bubble it up.
Receiving from a buffered chan error consumes the value, so only the
first caller (WaitUntilRunning, HasExited, or Close) sees the signal.
Subsequent receivers block or take the wrong branch. Replace with a
closed chan struct{} which can be received from any number of times,
and store the error in a separate field.
The stderr reader used bufio.Scanner which has a 64KB max line size.
If the subprocess wrote a line exceeding this limit, the scanner would
stop reading, the OS pipe buffer would fill, and the subprocess would
deadlock.
Replace the scanner with a statusWriter that wraps io.Copy. The writer
forwards all stderr to os.Stderr while capturing the last short line
(≤256 bytes) for error reporting, avoiding both the deadlock and the
need to buffer arbitrarily long lines.
If `OLLAMA_DEBUG_LOG_REQUESTS` is set, then on server startup a temp
folder will be created. Upon any inference request, the body will be
logged to a file in this folder, as well as a small shell script to
"replay" the request using cURL.
This is just intended for debugging scenarios, not as something to turn
on normally.
Previous xml repair for glm was a good start, but we need to go further and repair any incorrect open or closing tags
Co-authored-by: Dongluo Chen <dongluo.chen@gmail.com>
Enable multiple conversations to reuse cached computations when they
share token prefixes (e.g. the same system prompt). A prefix trie
tracks shared regions so switching between conversations only
recomputes tokens that diverge. Inactive conversation state is paged
from active GPU memory to other memory and restored on demand, with LRU
eviction to keep memory usage bounded.
Slice used cmp.Or to resolve a zero stop value to the dimension size,
intended to support open-ended slices like a[i:]. This made Slice(0, 0)
indistinguishable from Slice(), so any slice with a zero stop would
silently include the entire dimension instead of being empty.
Replace cmp.Or with an explicit End sentinel and resolve negative
indices against the dimension size, matching Python/PyTorch semantics.
Defensively handle environments without a display server to ensure signin remains usable on headless VMs and SSH sessions.
- Skip calling xdg-open when neither DISPLAY nor WAYLAND_DISPLAY is set, preventing silent failures or unexpected browser handlers
- Render the signin URL as plain text instead of wrapping it in OSC 8 hyperlink escape sequences, which can be garbled or hidden by terminals that don't support them
Claude Code sends an x-anthropic-billing-header that changes on every
request. This is embedded in the system prompt and consequently
breaks the KV cache for every request. Given the size of the prompts
that Claude Code usees, this has significant performance impact.
The OpenClaw installer requires git in addition to npm. Update the
dependency check to detect both and provide specific install guidance
for whichever dependencies are missing.
`WebSearchAnthropicWriter` expects a single object per write. The new
transparent proxy will instead send it whatever bytes it sees. This
cloud-model + local-orchestration + cloud-search is a temporary code
path, so instead of making the web search code more robust to this, I
put an adapter in the middle that will flush line-by-line to preserve
the old behavior.
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.
This change adds a tensorImportTransform interface for model-specific
tensor transformations during safetensors import. This allows importing
and modifying the standard HF based weights as well as the mlx-community
derived pre-quantized safetensors repos to be directly
imported into `ollama create`. Right now this only works with Qwen3.5
importing which does tensor renaming, norm weight shifting (it
adds +1 to each value of the norm vectors), conv1d transposition,
and casts to BF16s for F32 based vectors.
MLX runners (image generation and LLM) previously bypassed the
scheduler's standard load path via a separate loadMLX method. This meant
they skipped VRAM fitting checks and couldn't participate in model
eviction.
Now all model types flow through the same load function. Model eviction
for MLX is based on weights as KV cache and compute graph are dynamic.
This means that eviction does not take into account the worst case
memory and models can still compete for memory but it is a significant
improvement.
In allocModel(), the first call to reserveWorstCaseGraph(true) had its
error silently discarded — `return nil` was used instead of `return err`.
This meant that if the prompt-sized graph reservation failed (e.g. due
to insufficient memory), the error was swallowed, allocModel reported
success, and the model appeared to load correctly. Subsequent inference
would then fail in unexpected ways because the worst-case graph was
never properly reserved.
Fix: return the actual error so the caller can handle the failure
(retry with reduced parallelism, report OOM, etc.).
Co-Authored-By: Claude (claude-opus-4-6) <noreply@anthropic.com>
In container environments without systemd, `openclaw onboard
--install-daemon` exits non-zero because it cannot create a systemd
user service. This causes `ollama launch openclaw` to abort even
though the gateway can be started as a foreground child process.
Only pass --install-daemon when systemd user services are reachable
(Linux with /run/systemd/system present and XDG_RUNTIME_DIR set).
On all other platforms the flag is still included by default.
New features:
- Warmup phase to eliminate cold-start outliers
- time-to-first-token measured in each epoch
- VRAM/memory tracking to identify CPU spillover
- Controlled prompt length
- Defaults to 6 epochs and 200 tokens max
Benchstat fixes:
- ns/request instead of ns/op — non-standard unit created a separate group instead of grouping with timing metrics
- Token count as the N field — benchstat interprets N as iteration count for statistical weighting, not as a token count
OpenClaw now accepts the Ollama onboarding flags directly upstream, so rely on its wizard state instead of the legacy integration onboarding flag.
Update first-run setup to pass the Ollama auth and model flags during onboarding, perform a best-effort update before onboarding when needed, and drop the stale test that asserted persistence of the old onboarding flag.
When a zstd-compressed request (e.g. from Codex CLI) hits /v1/responses
with a cloud model the request failed.
Fix by decompressing zstd bodies before
model extraction, so cloud models are detected and proxied directly
without the writer being wrapped.
writeError in both OpenAI and Anthropic middleware writers would return
a raw json.SyntaxError when the error payload wasn't valid JSON (e.g.
"invalid character 'e' looking for beginning of value"). Fall back to
using the raw bytes as the error message instead.
Also use the actual HTTP status code rather than hardcoding 500, so
error types map correctly
Root cause: StreamConverter.Process() only incremented contentIndex when
closing a thinking block if text content was present. When a model emitted
thinking followed directly by a tool_use block (no text in between),
thinkingDone was never set and contentIndex was not incremented, causing the
tool_use content_block_start to reuse index 0. Clients expecting sequential
indices would then fail to find the tool content block.
Fix: In the tool call loop, close any open thinking block (thinkingStarted &&
!thinkingDone) and increment contentIndex before opening the tool_use block,
mirroring the existing logic that closes an open text block.
Fixes#14816
Add reasoning_effort and reasoning to the supported features and
request fields for /v1/chat/completions. These fields control
thinking on thinking-capable models but were previously undocumented.
Closes#14820
Use 7z compression (better compression rate) if found in path. That
alone isn't sufficient to get us under 2G, so MLX is now split out as a
discrete download. Fix CI so it will fail if artifacts fail to upload.
The CLI now links to the lazy-load MLX code, but that still happens in
init functions. On internal MLX errors, the CLI exits before it has a
chance to start. This change re-wires the MLX error handling so it
doesn't exit by default. The MLX based runners currently expect exits
on failure, so they re-initialize the default error handling. We can
refine error handling for better go stack traces in the future.
* 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
- Collapse MLX sampling state into a single sample.Sampler struct (options + history).
- Replace interface-based sampler chain (TopP, TopK, penalty, etc.) with function-based transforms.
- Update request/pipeline wiring to use *sample.Sampler, seed history from prompt tokens, and append generated tokens each step.
- Implement top_p, min_p, repeat_penalty, and frequency_penalty
Previously we were printing out bad errors for expected cases like
clients disconnecting. Now we only debug log when that happens (which
still might help in cases where we're figuring out why an integration
isn't working). For other errors, we print out a proper warning now
Our Dockerfile leverages parallel stages for more efficient builds. However,
our old parallel settings were naive and lead to under/over utilization
depending on the capabilities of your build system.
This change switches to using Ninja for all our docker cmake builds to leverage
its smarter parallel logic. We tell Ninja to target a load of nproc so each of
the build stages will share the load on the system aiming for full CPU use
without oversaturation.
The GPU parallelism settings are also adjusted to 4 to avoid a long-tail for
the last few GPU targets as they work through the long list of GPU
architectures.
This also fixes the Dockerfile to move Vulkan install to just the stage that
needs it instead of blocking most other GPU installs. This should speed up CI
which always has a clean build cache.
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.
Remove the /v1 suffix from the OpenClaw provider baseUrl so it uses
the native Ollama API instead of the OpenAI-compatible endpoint. The
/v1 endpoint my break tool calling in OpenClaw.
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
* don't require pulling stubs for cloud models
This is a first in a series of PRs that will better integrate Ollama's
cloud into the API and CLI. Previously we used to have a layer of
indirection where you'd first have to pull a "stub" model that contains
a reference to a cloud model. With this change, you don't have to pull
first, you can just use a cloud model in various routes like `/api/chat`
and `/api/show`. This change respects
<https://github.com/ollama/ollama/pull/14221>, so if cloud is disabled,
these models won't be accessible.
There's also a new, simpler pass-through proxy that doesn't convert the
requests ahead of hitting the cloud models, which they themselves
already support various formats (e.g., `v1/chat/completions` or Open
Responses, etc.). This will help prevent issues caused by double
converting (e.g., `v1/chat/completions` converted to `api/chat` on the
client, then calling cloud and converting back to a
`v1/chat/completions` response instead of the cloud model handling the
original `v1/chat/completions` request first).
There's now a notion of "source tags", which can be mixed with existing
tags. So instead of having different formats like`gpt-oss:20b-cloud` vs.
`kimi-k2.5:cloud` (`-cloud` suffix vs. `:cloud`), you can now specify
cloud by simply appending `:cloud`. This PR doesn't change model
resolution yet, but sets us up to allow for things like omitting the
non-source tag, which would make something like `ollama run
gpt-oss:cloud` work the same way that `ollama run gpt-oss` already works
today.
More detailed changes:
- Added a shared model selector parser in `types/modelselector`:
- supports `:cloud` and `:local`
- accepts source tags in any position
- supports legacy `:<tag>-cloud`
- rejects conflicting source tags
- Integrated selector handling across server inference/show routes:
- `GenerateHandler`, `ChatHandler`, `EmbedHandler`,
`EmbeddingsHandler`, `ShowHandler`
- Added explicit-cloud passthrough proxy for ollama.com:
- same-endpoint forwarding for `/api/*`, `/v1/*`, and `/v1/messages`
- normalizes `model` (and `name` for `/api/show`) before forwarding
- forwards request headers except hop-by-hop/proxy-managed headers
- uses bounded response-header timeout
- handles auth failures in a friendly way
- Preserved cloud-disable behavior (`OLLAMA_NO_CLOUD`)
- Updated create flow to support `FROM ...:cloud` model sources (though
this flow uses the legacy proxy still, supporting Modelfile overrides
is more complicated with the direct proxy approach)
- Updated CLI/TUI/config cloud detection to use shared selector logic
- Updated CLI preflight behavior so explicit cloud requests do not
auto-pull local stubs
What's next?
- Cloud discovery/listing and cache-backed `ollama ls` / `/api/tags`
- Modelfile overlay support for virtual cloud models on OpenAI/Anthropic
request families
- Recommender/default-selection behavior for ambiguous model families
- Fully remove the legacy flow
Fixes: https://github.com/ollama/ollama/issues/13801
* consolidate pull logic into confirmAndPull helper
pullIfNeeded and ShowOrPull shared identical confirm-and-pull logic.
Extract confirmAndPull to eliminate the duplication.
* skip local existence checks for cloud models
ModelExists and the TUI's modelExists both check the local model list,
which causes cloud models to appear missing. Return true early for
explicit cloud models so the TUI displays them beside the integration
name and skips re-prompting the model picker on relaunch.
* support optionally pulling stubs for newly-style names
We now normalize names like `<family>:<size>:cloud` into legacy-style
names like `<family>:<size>-cloud` for pulling and deleting (this also
supports stripping `:local`). Support for pulling cloud models is
temporary, once we integrate properly into `/api/tags` we won't need
this anymore.
* Fix server alias syncing
* Update cmd/cmd.go
Co-authored-by: Parth Sareen <parth.sareen@ollama.com>
* address comments
* improve some naming
---------
Co-authored-by: ParthSareen <parth.sareen@ollama.com>
The "(local)" qualifier is unnecessary since there's only one Ollama
provider. Existing configs with the old name are migrated automatically;
custom names are left unchanged.
Only the last token's processing time is included in prompt processing,
giving an artificially high rate. In addition, the number of tokens
only included the tokens that miss the cache, instead of our historic
total tokens.
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
Errors that occur during pipeline processing are currently only
logged but not sent back to the client. Rather than using HTTP
status codes as we have historically done, this serializes errors
as messages to allow sending them at any time during the stream.
The MLX runner previously reported a static VRAM estimate that was
computed at load time and consisted only of the weights. This is
strictly less than the actual memory usage, as it does not include
the KV cache or compute graph.
When the entire prompt was already cached (e.g. repeated prompt),
findRemaining returned an empty slice, causing FromValues to panic
on an index-out-of-range accessing a zero-length byte slice.
Fix by always keeping at least one token to re-evaluate so the
pipeline can seed token generation. Also reject empty prompts
early rather than panicking.
Align Qwen parser behavior with Transformers serve by allowing <tool_call> parsing while still in thinking collection.
Changes:
- qwen3vl: detect <tool_call> before </think> in thinking state and transition to tool parsing
- qwen3: same thinking-state tool detection and partial-tag overlap handling
- tests: update qwen3vl thinking/tool interleaving expectations
- tests: add qwen3 cases for tool call before </think> and split <tool_call> streaming
Currently, a canceled request can result in computation continuing
in the background to completion. It can also trigger a deadlock
when there is nobody to read the output tokens and the pipeline
cannot continue to the next request.
Particularly in error cases, it can be difficult to ensure that
all pinned memory is unpinned, MLX buffers are released and cache
state is consistent. This encapsulates those pieces and sets up
proper deferrals so that this happens automatically on exit.
Pass subprocess stdout/stderr through to the parent's stderr directly
instead of re-wrapping each line with slog. The subprocess already
writes structured slog output, so the re-wrapping produced nested
timestamps, levels, and message fields that were hard to read.
Also downgrade verbose KV cache debug logs to trace level.
The KV cache previously used a tree structure which could
store multiple divergent sequences, which is good for cache
reuse. However, this is typically used in conjunction with
paged attention so each node in the tree can store just a
chunk of the KV cache and they can be stitched together later.
We don't currently do this, so the cache was storing copies of
the full cache for each past sequence.
This redundancy plus the lack of resource limits, caused significant
memory use as a conversation grew. Instead, this changes to store
a single entry for the cache, which can be prefix matched. Although
it is less ideal for multiple users, it largely matches Ollama's
current behavior. It can be improved as additional pieces are fleshed
out.
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.
The recent change in #14322 added tryLoadByName() which attempts to
load libmlxc.dylib via rpath before searching directories. This is an
optimization for Homebrew installations where rpath is correctly set.
However, when rpath isn't set (which is the common case for app bundle
installations), dlopen fails and the CHECK macro prints an error to
stderr:
ERROR - dynamic.c:21 - CHECK failed: handle->ctx != NULL
This error is misleading because it's an expected failure path - the
code correctly falls back to searching the executable directory and
loads the library successfully. The error message causes user confusion
and makes it appear that something is broken.
Replace the CHECK macro with a simple return code so the C code fails
silently. The Go code already handles error logging appropriately:
tryLoadByName() fails silently (intentional fallback), while
tryLoadFromDir() logs via slog.Error() when explicit path loading fails.
Parse the default_num_ctx from the server's "vram-based default context"
log line and expose it through the inference compute API. This eliminates
duplicate VRAM tier calculation logic in the frontend.
- Add InferenceInfo struct with Computes and DefaultContextLength
- Rename GetInferenceComputer to GetInferenceInfo
- Handle missing default context line gracefully (older servers)
- Add DefaultContextLength to InferenceComputeResponse
- Update Settings UI to use server's default, disable slider while loading
- Add disabled prop to Slider component (grays out + hides handle)
- Migrate existing users with context_length=4096 to 0 (auto mode)
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.
The existing code manually searches directories for libmlxc.* and passes
full paths to dlopen, bypassing the binary's rpath. This means MLX
libraries installed via package managers (e.g., Homebrew) aren't found
even when rpath is correctly set at link time.
This change adds a fallback that tries loading via rpath first (using
just the library name), before falling back to the existing directory
search. This follows standard Unix/macOS conventions and works with any
installation that sets rpath.
Fixes library loading on macOS with Homebrew-installed mlx-c without
requiring OLLAMA_LIBRARY_PATH environment variable.
Co-authored-by: Natl <nat@MacBook-Pro.local>
The Codex runner was not setting OPENAI_BASE_URL or OPENAI_API_KEY, this prevents Codex from sending requests to api.openai.com instead of the local Ollama server. This mirrors the approach used by the Claude runner.
Codex v0.98.0 sends zstd-compressed request bodies to the /v1/responses endpoint. Add decompression support in ResponsesMiddleware with an 8MB max decompressed size limit to prevent resource exhaustion.
This fixes a bug with current MLX based models which don't get loaded/unloaded correctly. The first model currently gets loaded and then subsequent model starts get shunted to the first runner which results in the wrong model being run.
* add ability to disable cloud
Users can now easily opt-out of cloud inference and web search by
setting
```
"disable_ollama_cloud": true
```
in their `~/.ollama/server.json` settings file. After a setting update,
the server must be restarted.
Alternatively, setting the environment variable `OLLAMA_NO_CLOUD=1` will
also disable cloud features. While users previously were able to avoid
cloud models by not pulling or `ollama run`ing them, this gives them an
easy way to enforce that decision. Any attempt to run a cloud model when
cloud is disabled will fail.
The app's old "airplane mode" setting, which did a similar thing for
hiding cloud models within the app is now unified with this new cloud
disabled mode. That setting has been replaced with a "Cloud" toggle,
which behind the scenes edits `server.json` and then restarts the
server.
* gate cloud models across TUI and launch flows when cloud is disabled
Block cloud models from being selected, launched, or written to
integration configs when cloud mode is turned off:
- TUI main menu: open model picker instead of launching with a
disabled cloud model
- cmd.go: add IsCloudModelDisabled checks for all Selection* paths
- LaunchCmd: filter cloud models from saved Editor configs before
launch, fall through to picker if none remain
- Editor Run() methods (droid, opencode, openclaw): filter cloud
models before calling Edit() and persist the cleaned list
- Export SaveIntegration, remove SaveIntegrationModel wrapper that
was accumulating models instead of replacing them
* rename saveIntegration to SaveIntegration in config.go and tests
* cmd/config: add --model guarding and empty model list fixes
* Update docs/faq.mdx
Co-authored-by: Jeffrey Morgan <jmorganca@gmail.com>
* Update internal/cloud/policy.go
Co-authored-by: Jeffrey Morgan <jmorganca@gmail.com>
* Update internal/cloud/policy.go
Co-authored-by: Jeffrey Morgan <jmorganca@gmail.com>
* Update server/routes.go
Co-authored-by: Jeffrey Morgan <jmorganca@gmail.com>
* Revert "Update internal/cloud/policy.go"
This reverts commit 8bff8615f9b5751fc5c0d1273b07c9de651e07f9.
Since this error shows up in other integrations, we want it to be
prefixed with Ollama
* rename cloud status
* more status renaming
* fix tests that weren't updated after rename
---------
Co-authored-by: ParthSareen <parth.sareen@ollama.com>
Co-authored-by: Jeffrey Morgan <jmorganca@gmail.com>
This change fixes an issue where GGML based models (for either the Ollama runner or
the legacy llama.cpp runner) would try to load the mlx library. That would panic
and the model fails to start.
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>
This change includes:
- changes to the safetensors metadata format
- changes to the create command to properly create the blobs with the new format
- changes to load the new format
- fixes ollama show to properly show each tensor
This adds a new powershell install script suitable for running via
irm https://ollama.com/install.ps1 | iex
If you download the script and run '-?' it reports basic usage
information, as well as usage examples for common customization
options. The script is signed as part of the release process
to ensure it can run on a typically configured Windows system.
This does not include doc updates - we can merge those after a release
ships to avoid user confusion.
Set ANTHROPIC_DEFAULT_OPUS_MODEL, ANTHROPIC_DEFAULT_SONNET_MODEL,
ANTHROPIC_DEFAULT_HAIKU_MODEL, and CLAUDE_CODE_SUBAGENT_MODEL when
launching Claude Code so all model tiers route through Ollama.
Allow installing Ollama on MacOS directly from the command line. This is in line with other CLI tools and results in a more streamlined experience when the user is looking to use the CLI specifically.
When numPredict is set, the user will receive one less token
than the requested limit. In addition, the stats will incorrectly
show the number of tokens returned as the limit. In cases where
numPredict is not set, the number of tokens is reported correctly.
This occurs because numPredict is checked when setting up the next
batch but hitting the limit will terminate the current batch as well.
Instead, is is better to check the limit as we actually predict them.
When trying to use cloud model with OLLAMA_HOST="ollama.com" while not signed in a helpful error message is displayed when the user is not signed in telling them they must sign in to use cloud models. This should be the same experience for models which specify a remote instance.
If a sequence is replaced in s.seqs while a batch is computing, the old logits can be decoded into the new sequence. This change rechecks the sequence pointer after compute and skips decoding for replaced entries, preventing stale results from being applied.
Change the truncation algorithm to start with all messages and remove
from the front until it fits, rather than adding messages one at a time
from the back. This reduces tokenization calls from O(n) to O(1) in the
common case where all messages fit in context.
When a browser is available open it to the connect URL automatically when running the `ollama signin` command. Browser is not opened in any other unauthorized scenario.
When context length is clamped to the model's trained context length,
ollama ps now shows the actual clamped value instead of the originally
configured value.
When launching OpenClaw without prior onboarding, run the onboarding
wizard instead of going straight to gateway. This ensures proper
gateway configuration (mode, token, etc.) before first use.
- Add onboarded() to check for wizard.lastRunAt marker in config
- Run onboard with --auth-choice skip --gateway-token ollama for fresh installs
- Existing installs (onboarding completed) run gateway directly
Fix typo in three error messages where 'baackend' was written instead
of 'backend' in the /health endpoint handler when initializing the
dummy model load.
* parsers/ministral: fix nested tool call parsing by counting brace nesting
* fix lint error
* parsers: refactor ministral parser
The old one was very tied to expecting to see only one token at a time,
which I don't like to assume (who knows what the future might hold wrt
speculative decoding, etc). This new one follows a similar structure to
qwen3-coder's parser, which incidentally makes it easier to test as well
(since we can test the individual events that come out when given
particular inputs).
---------
Co-authored-by: Devon Rifkin <drifkin@drifkin.net>
Use the original key dimension (qkNopeHeadDim + qkRopeHeadDim = 256) for
the attention scale instead of the MLA absorbed dimension (kvLoraRank +
qkRopeHeadDim = 576).
MLA absorption is a mathematically equivalent reorganization of the
attention computation - it should not change the effective attention
scale. The scale should match training, which uses 1/sqrt(256).
This improves tool calling and model looping issues.
CGO_CFLAGS and CGO_CXXFLAGS were being set without optimization flags,
which overrides Go's default -O2 and results in unoptimized C++ code.
This caused significant performance degradation in release builds
compared to local `go build` which uses the default optimization.
- build_darwin.sh: add -O3 to CGO_CFLAGS and CGO_CXXFLAGS exports
- Dockerfile: preserve CGO_CFLAGS/CGO_CXXFLAGS from build args instead
of overwriting them
- app/README.md: update documentation to include -O3
* update README ruby link
the ollama-ai ruby gem is vastly less popular and seems unmaintained
https://rubygems.org/gems/ollama-ai
the defacto standard with the most downloads in the ruby ecosystem is ruby_llm
https://rubygems.org/gems/ruby_llm
I would link to that to avoid complication and guarantee feature compatibility with ollama.
* Update gem link ruby_llm from website to GitHub
ollama links mostly to github, not project websites, hence link to ruby_llm github.
Use nthreads=128 for ncols=4 configurations in flash attention tile
kernel to reduce shared memory usage below 48KB limit on Maxwell
architectures (sm_50/52).
With nthreads=256 and ncols=4, np=2 which caused shared memory to
exceed 48KB. With nthreads=128 and ncols=4, np=1 keeps shared memory
under the limit.
The nvidia_fp32 config for (576, 512) head sizes had nbatch_fa=32,
which caused zero-sized arrays when computing array dimensions:
nbatch_fa / (np * warp_size) = 32 / (2 * 32) = 0
This resulted in CUDA compilation failures on CUDA 12 (Windows and
Linux arm64):
- "static assertion failed with nbatch_fa % (np*warp_size) != 0"
- "the size of an array must be greater than zero"
Fix by changing nbatch_fa from 32 to 64 for all (576, 512) configs
in the nvidia_fp32 function, matching the nvidia_fp16 and AMD configs.
- Fix panic in ollama show for image gen models (safe type assertion)
- Add vision capability for Flux2KleinPipeline models at create time
- Flatten transparent PNG images onto white background for better results
* model: add MLA absorption for glm4moelite
Split the combined KV_B tensor into separate K_B and V_B tensors
during conversion, enabling MLA (Multi-head Latent Attention)
absorption which compresses the KV cache for improved efficiency.
* ggml: enable MLA flash attention for GLM-4.7-flash
Add support for gqa_ratio 4 in MLA flash attention kernels. GLM-4.7-flash
uses head size 576 with gqa_ratio 4, which was previously only supported
for gqa_ratio 16 (DeepSeek).
Metal changes:
- Enable head size 576 for flash attention
- Increase simdgroups to 8 for large heads (>=512)
- Add case 8 kernel dispatch for 8 simdgroups
CUDA changes:
- Add gqa_ratio 4 support for head 576/512
- Add tile configs for (576, 512, 4) and (576, 512, 8)
- Add MMA config cases for ncols 4
- Add template instances for ncols2=4
* model: add compatibility validation for glm4moelite architecture
Remove static VRAM estimation (EstimateVRAM, CheckMemoryRequirements)
which wasn't helpful. Instead, report the actual tensor weight size
from the manifest for ollama ps.
- Remove memory estimation check from runner startup
- Remove EstimateVRAM, CheckMemoryRequirements, modelVRAMEstimates
- Add TotalTensorSize() to get actual weight size from manifest
- Use weight size for Server.vramSize instead of estimates
Note: This is better than showing 0 or inaccurate estimates, but the
weight size is a drastic underestimation of actual memory usage since
it doesn't account for activations, intermediate tensors, or MLX
overhead. Future work should query real-time memory from MLX
(e.g., MetalGetActiveMemory) for accurate reporting.
Remove the Qwen image generation and image editing model packages
to clean up the codebase. These models will be reintroduced later.
- Delete x/imagegen/models/qwen_image/ (10 files)
- Delete x/imagegen/models/qwen_image_edit/ (5 files)
- Remove related CLI flags and imports from cmd/engine/main.go
- Update comments in cache/step.go to remove Qwen-specific references
For `/api/show`, a fully missing `model_info` field trips up various
integrators (including a recent Android Studio integration).
The primary source of missing info tends to come from models with a
remote that are also missing other data. It seems better to me to return
an empty `model_info` than making up some other fields within
`model_info` (like saying the architecture is `remote` or something like
that). So this does slightly change `/api/show`'s behavior that possibly
someone is relying on, but it seems more important to ensure the field
is always there (from a quick sampling integrations seem to be robust to
missing fields _within_ it).
Fixes: https://github.com/ollama/ollama/issues/13783
Move the unload check (empty prompt + KeepAlive=0) before the image
generation model dispatch in GenerateHandler. This prevents models like
flux from being loaded into memory just to be immediately unloaded when
running `ollama rm`.
Also fix a bug in DeleteHandler where `args[0]` was used instead of
`arg` in the delete loop, causing only the first model to be unloaded
when deleting multiple models.
Add --quantize fp4 support to ollama create for image generation models
(flux2, z-image-turbo), using MLX's affine 4-bit quantization.
Changes:
- Add fp4 to validation in CreateImageGenModel
- Add FP4 case to quantizeTensor (group_size=32, bits=4, affine mode)
- Add GetQuantization() to WeightSource interface for dynamic params
- Update LoadLinearLayer to use quantization params from model metadata
The loadImageGen function was not setting Options on the runnerRef,
causing needsReload() to always return true (since it checks if
runner.Options == nil). This resulted in the image generation
subprocess being killed and restarted for every request.
Simplify Nemotron3NanoParser by delegating tool call parsing to
Qwen3CoderParser instead of duplicating the parsing logic. The
Nemotron parser now only handles the thinking state machine and
transitions to Qwen3CoderParser for content and tool call parsing.
This also fixes an issue where tool calls without </think> would
cause the parser to get stuck in thinking mode.
* MLX - dynamic loading of mlx-c
Create a wrapper layer to indirect the dependency on mlx-c so
the main ollama binary does not have a load-time dependency on mlx-c, mlx, and on linux, cuda. Lazy load the library via dlopen
so we can adjust the path to ensure the dependencies are found
and fail gracefully if not present.
* review comments
* fix broken tests
* x: make `ollama create --experimental` import from safetensors
This change allows pulling in safetensors models into the new experimental model format, and also
fixes the `ollama show` command to be able to correctly display the model information.
* gofumpt the linter
* gofumpt the linter again
* validate the model name
Added validation to ensure auth redirects stay on the same host as the original request. The fix is a single check in getAuthorizationToken comparing the realm URL's host against the request host. Added tests for the auth flow.
Co-Authored-By: Gecko Security <188164982+geckosecurity@users.noreply.github.com>
* gofmt
---------
Co-authored-by: Gecko Security <188164982+geckosecurity@users.noreply.github.com>
Add --norsrc flag to ditto commands when creating Ollama-darwin.zip
to exclude AppleDouble resource fork files (._* files) from the archive.
The mlx.metallib file has extended attributes, which causes ditto to
include a ._mlx.metallib AppleDouble file in the zip. Since this file
is not part of the code signature seal, macOS rejects the bundle during
auto-update verification with:
"a sealed resource is missing or invalid"
"file added: .../._mlx.metallib"
The --norsrc flag prevents ditto from preserving resource forks and
extended attributes, ensuring only signed files are included in the
release archive.
The CMake condition for installing mlx.metallib checks
CMAKE_OSX_ARCHITECTURES, but this variable is only set when explicitly
passed - not auto-detected. The arm64 build was missing this flag,
causing the metallib to not be installed, which then caused codesign
to fail on the unexpanded glob pattern.
- Install mlx.metallib for arm64 builds (required for Metal GPU acceleration)
- Apply rpath settings to all macOS builds, not just x86_64
- Add CMAKE_BUILD_WITH_INSTALL_RPATH to avoid install_name_tool errors
- Update build_darwin.sh to copy, sign, and package the metallib
TeaCache:
- Timestep embedding similarity caching for diffusion models
- Polynomial rescaling with configurable thresholds
- Reduces transformer forward passes by ~30-50%
FP8 quantization:
- Support for FP8 quantized models (8-bit weights with scales)
- QuantizedMatmul on Metal, Dequantize on CUDA
- Client-side quantization via ollama create --quantize fp8
Other bug fixes:
- Fix `/api/show` API for image generation models
- Server properly returns model info (architecture, parameters, quantization)
- Memory allocation optimizations
- CLI improvements for image generation
RemoveLayers was calling Manifests() for each layer to check if it was
shared with other models. For models with many blobs (e.g., tensor
models), this caused O(N*M) manifest reads.
Now loads manifests once and builds a set of in-use digests.
Removes 5-minute HTTP client timeout that caused "context deadline
exceeded" errors on large file downloads. Stall detection (10s)
already handles unresponsive connections.
Fixes progress bar total going down on resume by calculating total
from all blobs upfront and reporting already-downloaded bytes
as completed immediately.
* api: add Anthropic Messages API compatibility layer
Add middleware to support the Anthropic Messages API format at /v1/messages.
This enables tools like Claude Code to work with Ollama local and cloud models through the
Anthropic API interface.
* WIP - MLX backend with gemma3
* MLX: add cmake and go tag build toggles
To build the new MLX backend code:
cmake --preset MLX
cmake --build --preset MLX --parallel
cmake --install build --component MLX
go build -tags mlx .
Note: the main.go entrypoint for the MLX engine will change in a follow up commit.
* add experimental image generation runtime
* add experimental image generation runtime
* MLX: wire up cuda build for linux
* MLX: get dependencies correct and dedup
This is still too large for a unified github artifact, but is now "correct" for the mlx_cuda_v13
directory.
* fix relative link bug in dedup
* Add darwin build and readme
* add go build tag for mlx dependent code and wire up build_darwin.sh
* lint cleanup
* macos: build mlx for x86
This will be CPU only.
* cuda build instructions and fix drift from mlx bump
* stale comment
* Delete agent helper doc
* Clean up readme.md
* Revise README for tokenizer clarity and details
Updated README to clarify tokenizer functionality and removed correctness section.
---------
Co-authored-by: jmorganca <jmorganca@gmail.com>
With the upcoming addition of MLX, the linux bundle will exceed the
maximum github artifact size of 2G. This change will bring the size
back down.
The install.sh changes support backwards compatibility for prior versions
thus should be safe to merge concurrently with this change.
In #13525, I accidentally broke templates' ability to automatically
render tool call function arguments as JSON.
We do need these to be proper maps because we need templates to be able
to call range, which can't be done on custom types.
* 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
The normalize function now checks for NaN and Inf values in the
embedding vector before processing. This prevents JSON encoding
failures when models produce invalid floating-point values.
Fixes#13572
Signed-off-by: majiayu000 <1835304752@qq.com>
The tool calling example used "get_temperature" for tool_calls but
defined the tool as "get_weather". Also removed trailing commas that
made the JSON invalid.
Fixes#13031
On the llama engine, when we compute the memory layout, we reserve
a buffer to allow for some flexibility for incorrect estimates.
This is subtracted from GPU free memory and on GPUs with limited
memory, it may underflow.
Fixes#13494
* Revert "add support for NVIDIA Nemotron 3 Nano"
This reverts commit e7d2ae9d69421012e9a8765c06a3fdf0e45b12f3.
* GGML update to 380b4c984
Remove MaskBatchPadding as GGML_KQ_MASK_PAD is no longer present (no
padding required)
* update to c45f89d55
* ec98e2002
solar pro needed more adjusting - needs verification
* review comments
Refactored the ConfigV2 and RootFS types from server/images.go to a new types/model/config.go file under the model package. Updated all references to use model.ConfigV2 and model.RootFS. This allows for use in other projects without worrying about compiling the c code in the llama package.
The ggml/src/CMakeLists.txt uses GGML_VERSION_MAJOR for the shared
library SOVERSION property, but these variables were not defined when
building from ollama's CMakeLists.txt.
This caused libggml-base.so to be named with a literal "SOVERSION"
suffix (libggml-base.so.SOVERSION) instead of the actual version
number (libggml-base.so.0).
The fix adds the required GGML_VERSION_* variables before including
the ggml subdirectory.
Fixes#13436
* flash attn: add auto mode for llama engine
If the user does not specify fa in the environment, use auto-mode.
* review comments
* ensure kv cache quantized types have FA explicitly enabled
additional review comments
This changes the default behavior to use the Ollama engine for supported
models, while retaining the ability to disable the Ollama engine and
fall back to the Llama engine. Models in the OllamaEngineRequired list
will always run on the Ollama engine.
* docs: add docs for v1/responses and rework openai compat section
I reworked the examples to be separated by topic and to be fully
runnable (i.e., they now log output instead of just suggesting how a
call might be made).
We now use `<CodeGroup>`s so that each example has a dropdown on the
docs site for users to choose, which makes the examples a lot more
digestible (since you only see approx 1/3 of the code you used to).
I also added a new tool to extract code examples into files so that it's
easier to actually run them and check that they work.
## Example
```shell
go run docs/tools/extract-examples/main.go docs/api/openai-compatibility.mdx
```
Output:
```
Extracting code examples to: /var/folders/vq/wfm2g6k917d3ldzpjdxc8ph00000gn/T/mdx-examples-3271754368
- 01_basic.py
- 01_basic.js
- 01_basic.sh
- 02_responses.py
- 02_responses.js
- 02_responses.sh
- 03_vision.py
- 03_vision.js
- 03_vision.sh
Extracted 9 file(s) to /var/folders/vq/wfm2g6k917d3ldzpjdxc8ph00000gn/T/mdx-examples-3271754368
To run examples:
cd /var/folders/vq/wfm2g6k917d3ldzpjdxc8ph00000gn/T/mdx-examples-3271754368
npm install # for JS examples
then run individual files with `node file.js`, `python file.py`, `bash file.sh`
```
In the future we should consider actually running the examples in CI and
having some sort of acceptance test so we can automatically detect when
our examples break. So this is just a start in that direction.
* Update docs/api/openai-compatibility.mdx
Co-authored-by: Parth Sareen <parth.sareen@ollama.com>
* Update docs/api/openai-compatibility.mdx
Co-authored-by: Parth Sareen <parth.sareen@ollama.com>
---------
Co-authored-by: Parth Sareen <parth.sareen@ollama.com>
This PR detects embedding models and sets batch_size = context_size so the full input fits in a single batch.
Previously, if batch size was smaller than the input, tokens could be split across batches and cause a SIGTRAP crash.
This change ensures all tokens stay in one batch and prevents crashes.
Fixes: #12938#13054
Co-authored-by: Jesse Gross <jesse@ollama.com>
* feat: Bump llama.cpp to the latest master (17f7f4b)
This brings in significant improvements to prefill performance for all
models using the SSM_CONV and SSM_SCAN ops (granite4, jamba, falcon-h,
nemotron-h, Qwen3 Next) on Apple Metal.
See https://github.com/ggml-org/llama.cpp/pull/17876
Branch: LlamaCPPMetalSSMImprovements
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Update patches 1-4
Branch: LlamaCPPMetalSSMImprovements
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Update patches 5-12
Branch: LlamaCPPMetalSSMImprovements
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Update patches 13-18
Branch: LlamaCPPMetalSSMImprovements
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Update patch 20
Branch: LlamaCPPMetalSSMImprovements
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Update patches 21-31
Branch: LlamaCPPMetalSSMImprovements
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Sync vendored code
The two files I'm not sure about here are the swap from gemma3-iswa.cpp to
gemma3.cpp (I chose to include this because I think it's required), and the
inclusion of `ggml-zendnn.h` which I chose to omit.
Branch: LlamaCPPMetalSSMImprovements
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
---------
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Although the vision component of multimodal models typically already
call the optimized nn.Attention, it is converted into non-fused
operations. That is because the backend-specific fused kernels may
have requirements, such as padding, and they is performed by the
cache, which vision encoders don't use.
This implements a fallback path in the backend, softening the
requirements into optimizations. In turn, this allows flash attention
to be used for vision encoders, saving a significant amount of VRAM
and improving performance.
We currently use cache padding of 32 when not using flash attention
and 256 with flash attention, which is based on the historic alignment
requirements of these kernels. The restrictions have since been
loosened but there are still performance benefits, such as better
CUDA graph reuse.
Since the requirement is no longer kernel-specific, set the padding
uniformly to 256, as llama.cpp has.
* cmd/bench: support writing benchmark output to file
This changes Ollama to allow the bench command to write benchmark
results to a user-specified output file instead of stdout when the
--output flag is provided.
---------
Co-authored-by: Patrick Devine <patrick@infrahq.com>
* Revert "vulkan: temporary cary of vulkan fixes (#12971)"
This reverts commit 3a9e8e9fd4.
* ggml update to b7087
* fix argsort on metal
* update to b7108
* fix bakllava regression
This model lacks the metadata for the projector type.
* update to b7209
* fix TopK perf
* only build arm code on arm
This fixes a bug where disabling thinking on deepseek-v3.1 did not stop the model from thinking.
When thinking is not defined it should not be sent to the server since this will cause error responses in some cases where the model does not support thinking. However if it is defined as false it should still be sent.
We now do a deeper probe of CUDA devices to verify the library version has
the correct compute capability coverage for the device. Due to ROCm also
interpreting the CUDA env var to filter AMD devices, we try to avoid setting
it which leads to problems in mixed vendor systems. However without setting
it for this deeper probe, each CUDA library subprocess discovers all CUDA GPUs
and on systems with lots of GPUs, this can lead to hitting timeouts. The fix is
to turn on the CUDA visibility env var just for this deeper probe use-case.
Model eviction happens when we have at least one other model
loaded and are unable to load all layers into VRAM. However, on
CPU-only systems we can never load layers into VRAM, so this
constantly triggered eviction.
Fixes#13227
This change:
* fixes rope scaling in the mistral converter
* updates ministral to include llama4 scaling
* includes a new ministral parser for parsing reasoning and tool calling
---------
Co-authored-by: jmorganca <jmorganca@gmail.com>
If the user has somehow installed another GGML based app which places a
ggml-base lib somewhere in their PATH, we can experience runtime problems
due to incompatibilities. This change adds a warning message if we detect
a ggml-base outside of our install location to aid in troubleshooting.
While processing the response stream during a chat or generation if an error is occurred it is parsed and returned to the user. The issue with the existing code is that this assumed the response would be valid JSON, which is not a safe assumption and caused cryptic error messages to be displayed due to parsing failures:
`invalid character 'i' looking for beginning of value`
This change updates the stream function to return the raw error string if it cant be parsed as JSON. This should help with debugging issues by making sure the actual error reaches the user.
The cuda_jetpack libs will enumerate discrete GPUs on SBSA systems
which leads to runtime failures of missing kernels. This fix
requires an exact match to enable jetpacks instead of relying on
enumeration to filter out supported libraries.
We currently copy data into the KV cache in contiguous buffers using
ggml_cpy(). ggml_set_rows() was introduced to allow scatter operation
so that contiguous buffers are no longer required. The direct primary
benefit of this is that we no longer need to perform defragmentation.
However, GGML recently removed an optimization for ggml_cpy() and
we picked it up in 544b673 "ggml update to b6840 (#12791)". This
caused a roughly 40% drop in token generation performance on CUDA
due to CUDA graphs no longer being used. By switching to
ggml_set_rows(), the original optimization is no longer necessary
and CUDA performance is restored.
Fixes#13112
GGML requires tensors to be contiguous for reshape and if
this is not the case, it will assert fail. Contiguous is an
expensive operation, so it's best to do it lazily when it is
actually required rather than ahead of time when it may not
be needed.
Calling abort on windows triggers the C++ runtime to attempt a debugger
attach, which causes the crashed runners to hang instead of exit, leading
to a timeout instead of a fast failure during discovery.
Void is an open source AI code editor and Cursor alternative that supports
Ollama. It's built on VS Code and allows users to connect directly to Ollama
for private LLM usage without going through a middleman backend.
Key features:
- Open source Cursor alternative
- Direct Ollama integration
- VS Code fork with full compatibility
- Agent mode and MCP support
- Works with any open source model
Fixes#12919
Signed-off-by: Samaresh Kumar Singh <ssam3003@gmail.com>
* build: optimize dockerfile context for iterating
This moves the copy of the source into the layer AFTER
doing software installs so we don't have to go through
the RPM install for cuda, etc. every time you touch a
source file.
* amd: implement linux sysfs based VRAM lookup
This adds a C++ implementation of sysfs DRM VRAM discovery
for more accurate free VRAM data on linux for AMD GPUs.
This change adds a basic benchmarking test framework for Ollama which can
be used to determine the prefill, eval, load duration, and total duration
for running a given model or models.
Many failed GPU discovery issues recently can be traced to incorrect override settings.
This extra logging should help quickly spot these and guide users to try unsetting them first.
description:Please copy and paste any relevant log output. See [Troubleshooting Guide](https://github.com/ollama/ollama/blob/main/docs/troubleshooting.md#how-to-troubleshoot-issues) for details.
description:Please copy and paste any relevant log output. See [Troubleshooting Guide](https://github.com/ollama/ollama/blob/main/docs/troubleshooting.mdx#how-to-troubleshoot-issues) for details.
echo GOFLAGS="'-ldflags=-w -s \"-X=github.com/ollama/ollama/version.Version=${GITHUB_REF_NAME#v}\" \"-X=github.com/ollama/ollama/server.mode=release\"'" | tee -a $GITHUB_OUTPUT
echo VERSION="${GITHUB_REF_NAME#v}" | tee -a $GITHUB_OUTPUT
echo vendorsha=$(cat LLAMA_CPP_VERSION)-$(cat MLX_VERSION)-$(cat MLX_C_VERSION) | tee -a $GITHUB_OUTPUT
"Regular expression describing AMDGPU_TARGETS not supported on Windows. Override to force building these targets. Default \"^gfx(908|90a|1200|1201):xnack[+-]$\"."
@@ -16,7 +16,7 @@ See the [development documentation](./docs/development.md) for instructions on h
* New features: new features (e.g. API fields, environment variables) add surface area to Ollama and make it harder to maintain in the long run as they cannot be removed without potentially breaking users in the future.
* Refactoring: large code improvements are important, but can be harder or take longer to review and merge.
* Documentation: small updates to fill in or correct missing documentation is helpful, however large documentation additions can be hard to maintain over time.
* Documentation: small updates to fill in or correct missing documentation are helpful, however large documentation additions can be hard to maintain over time.
### Issues that may not be accepted
@@ -43,7 +43,7 @@ Tips for proposals:
* Explain how the change will be tested.
Additionally, for bonus points: Provide draft documentation you would expect to
see if the change were accepted.
see if the changes were accepted.
## Pull requests
@@ -66,7 +66,6 @@ Examples:
llm/backend/mlx: support the llama architecture
CONTRIBUTING: provide clarity on good commit messages, and bad
docs: simplify manual installation with shorter curl commands
> You should have at least 8 GB of RAM available to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.
## Customize a model
### Import from GGUF
Ollama supports importing GGUF models in the Modelfile:
1. Create a file named `Modelfile`, with a `FROM` instruction with the local filepath to the model you want to import.
```
FROM ./vicuna-33b.Q4_0.gguf
```
2. Create the model in Ollama
```shell
ollama create example -f Modelfile
```
3. Run the model
```shell
ollama run example
```
### Import from Safetensors
See the [guide](https://docs.ollama.com/import) on importing models for more information.
### Customize a prompt
Models from the Ollama library can be customized with a prompt. For example, to customize the `llama3.2` model:
```shell
ollama pull llama3.2
```
Create a `Modelfile`:
## Get started
```
FROM llama3.2
# set the temperature to 1 [higher is more creative, lower is more coherent]
PARAMETER temperature 1
# set the system message
SYSTEM """
You are Mario from Super Mario Bros. Answer as Mario, the assistant, only.
"""
ollama
```
Next, create and run the model:
You'll be prompted to run a model or connect Ollama to your existing agents or applications such as `Claude Code`, `OpenClaw`, `OpenCode` , `Codex`, `Copilot`, and more.
### Coding
To launch a specific integration:
```
ollama create mario -f ./Modelfile
ollama run mario
>>> hi
Hello! It's your friend Mario.
ollama launch claude
```
For more information on working with a Modelfile, see the [Modelfile](https://docs.ollama.com/modelfile) documentation.
Supported integrations include [Claude Code](https://docs.ollama.com/integrations/claude-code), [Codex](https://docs.ollama.com/integrations/codex), [Copilot CLI](https://docs.ollama.com/integrations/copilot-cli), [Droid](https://docs.ollama.com/integrations/droid), and [OpenCode](https://docs.ollama.com/integrations/opencode).
## CLI Reference
### AI assistant
### Create a model
`ollama create` is used to create a model from a Modelfile.
```shell
ollama create mymodel -f ./Modelfile
```
### Pull a model
```shell
ollama pull llama3.2
```
> This command can also be used to update a local model. Only the diff will be pulled.
### Remove a model
```shell
ollama rm llama3.2
```
### Copy a model
```shell
ollama cp llama3.2 my-model
```
### Multiline input
For multiline input, you can wrap text with `"""`:
Use [OpenClaw](https://docs.ollama.com/integrations/openclaw) to turn Ollama into a personal AI assistant across WhatsApp, Telegram, Slack, Discord, and more:
```
>>> """Hello,
... world!
... """
I'm a basic program that prints the famous "Hello, world!" message to the console.
ollama launch openclaw
```
### Multimodal models
### Chat with a model
Run and chat with [Gemma 4](https://ollama.com/library/gemma4):
```
ollama run llava "What's in this image? /Users/jmorgan/Desktop/smile.png"
ollama run gemma4
```
> **Output**: The image features a yellow smiley face, which is likely the central focus of the picture.
See [ollama.com/library](https://ollama.com/library) for the full list.
### Pass the prompt as an argument
```shell
ollama run llama3.2 "Summarize this file: $(cat README.md)"
```
> **Output**: Ollama is a lightweight, extensible framework for building and running language models on the local machine. It provides a simple API for creating, running, and managing models, as well as a library of pre-built models that can be easily used in a variety of applications.
### Show model information
```shell
ollama show llama3.2
```
### List models on your computer
```shell
ollama list
```
### List which models are currently loaded
```shell
ollama ps
```
### Stop a model which is currently running
```shell
ollama stop llama3.2
```
### Generate embeddings from the CLI
```shell
ollama run embeddinggemma "Your text to embed"
```
You can also pipe text for scripted workflows:
```shell
echo "Your text to embed" | ollama run embeddinggemma
```
### Start Ollama
`ollama serve` is used when you want to start ollama without running the desktop application.
## Building
See the [developer guide](https://github.com/ollama/ollama/blob/main/docs/development.md)
### Running local builds
Next, start the server:
```shell
./ollama serve
```
Finally, in a separate shell, run a model:
```shell
./ollama run llama3.2
```
See the [quickstart guide](https://docs.ollama.com/quickstart) for more details.
## REST API
Ollama has a REST API for running and managing models.
### Generate a response
```shell
curl http://localhost:11434/api/generate -d '{
"model": "llama3.2",
"prompt":"Why is the sky blue?"
}'
```
### Chat with a model
```shell
curl http://localhost:11434/api/chat -d '{
"model": "llama3.2",
"messages": [
{ "role": "user", "content": "why is the sky blue?" }
]
"model": "gemma4",
"messages": [{
"role": "user",
"content": "Why is the sky blue?"
}],
"stream": false
}'
```
See the [API documentation](./docs/api.md) for all endpoints.
See the [API documentation](https://docs.ollama.com/api) for all endpoints.
### Python
```
pip install ollama
```
```python
fromollamaimportchat
response=chat(model='gemma4',messages=[
{
'role':'user',
'content':'Why is the sky blue?',
},
])
print(response.message.content)
```
### JavaScript
```
npm i ollama
```
```javascript
importollamafrom"ollama";
constresponse=awaitollama.chat({
model:"gemma4",
messages:[{role:"user",content:"Why is the sky blue?"}],
});
console.log(response.message.content);
```
## Supported backends
- [llama.cpp](https://github.com/ggml-org/llama.cpp) project founded by Georgi Gerganov.
## Documentation
- [CLI reference](https://docs.ollama.com/cli)
- [REST API reference](https://docs.ollama.com/api)
- [TagSpaces](https://www.tagspaces.org) (A platform for file-based apps, [utilizing Ollama](https://docs.tagspaces.org/ai/) for the generation of tags and descriptions)
- [IntelliBar](https://intellibar.app/) (AI-powered assistant for macOS)
- [Jirapt](https://github.com/AliAhmedNada/jirapt) (Jira Integration to generate issues, tasks, epics)
- [ojira](https://github.com/AliAhmedNada/ojira) (Jira chrome plugin to easily generate descriptions for tasks)
- [QA-Pilot](https://github.com/reid41/QA-Pilot) (Interactive chat tool that can leverage Ollama models for rapid understanding and navigation of GitHub code repositories)
- [ChatOllama](https://github.com/sugarforever/chat-ollama) (Open Source Chatbot based on Ollama with Knowledge Bases)
- [CRAG Ollama Chat](https://github.com/Nagi-ovo/CRAG-Ollama-Chat) (Simple Web Search with Corrective RAG)
- [RAGFlow](https://github.com/infiniflow/ragflow) (Open-source Retrieval-Augmented Generation engine based on deep document understanding)
- [chat](https://github.com/swuecho/chat) (chat web app for teams)
- [Lobe Chat](https://github.com/lobehub/lobe-chat) with [Integrating Doc](https://lobehub.com/docs/self-hosting/examples/ollama)
- [Ollama RAG Chatbot](https://github.com/datvodinh/rag-chatbot.git) (Local Chat with multiple PDFs using Ollama and RAG)
- [BrainSoup](https://www.nurgo-software.com/products/brainsoup) (Flexible native client with RAG & multi-agent automation)
- [macai](https://github.com/Renset/macai) (macOS client for Ollama, ChatGPT, and other compatible API back-ends)
- [RWKV-Runner](https://github.com/josStorer/RWKV-Runner) (RWKV offline LLM deployment tool, also usable as a client for ChatGPT and Ollama)
- [Ollama Grid Search](https://github.com/dezoito/ollama-grid-search) (app to evaluate and compare models)
- [Olpaka](https://github.com/Otacon/olpaka) (User-friendly Flutter Web App for Ollama)
- [Casibase](https://casibase.org) (An open source AI knowledge base and dialogue system combining the latest RAG, SSO, ollama support, and multiple large language models.)
- [OllamaSpring](https://github.com/CrazyNeil/OllamaSpring) (Ollama Client for macOS)
- [LLocal.in](https://github.com/kartikm7/llocal) (Easy to use Electron Desktop Client for Ollama)
- [Shinkai Desktop](https://github.com/dcSpark/shinkai-apps) (Two click install Local AI using Ollama + Files + RAG)
- [AiLama](https://github.com/zeyoyt/ailama) (A Discord User App that allows you to interact with Ollama anywhere in Discord)
- [Ollama with Google Mesop](https://github.com/rapidarchitect/ollama_mesop/) (Mesop Chat Client implementation with Ollama)
- [Local Multimodal AI Chat](https://github.com/Leon-Sander/Local-Multimodal-AI-Chat) (Ollama-based LLM Chat with support for multiple features, including PDF RAG, voice chat, image-based interactions, and integration with OpenAI.)
- [ARGO](https://github.com/xark-argo/argo) (Locally download and run Ollama and Huggingface models with RAG and deep research on Mac/Windows/Linux)
- [OrionChat](https://github.com/EliasPereirah/OrionChat) - OrionChat is a web interface for chatting with different AI providers
- [G1](https://github.com/bklieger-groq/g1) (Prototype of using prompting strategies to improve the LLM's reasoning through o1-like reasoning chains.)
- [Perfect Memory AI](https://www.perfectmemory.ai/) (Productivity AI assists personalized by what you have seen on your screen, heard, and said in the meetings)
- [Hexabot](https://github.com/hexastack/hexabot) (A conversational AI builder)
- [Reddit Rate](https://github.com/rapidarchitect/reddit_analyzer) (Search and Rate Reddit topics with a weighted summation)
- [OpenTalkGpt](https://github.com/adarshM84/OpenTalkGpt) (Chrome Extension to manage open-source models supported by Ollama, create custom models, and chat with models from a user-friendly UI)
- [VT](https://github.com/vinhnx/vt.ai) (A minimal multimodal AI chat app, with dynamic conversation routing. Supports local models via Ollama)
- [Nosia](https://github.com/nosia-ai/nosia) (Easy to install and use RAG platform based on Ollama)
- [Witsy](https://github.com/nbonamy/witsy) (An AI Desktop application available for Mac/Windows/Linux)
- [Abbey](https://github.com/US-Artificial-Intelligence/abbey) (A configurable AI interface server with notebooks, document storage, and YouTube support)
- [Minima](https://github.com/dmayboroda/minima) (RAG with on-premises or fully local workflow)
- [aidful-ollama-model-delete](https://github.com/AidfulAI/aidful-ollama-model-delete) (User interface for simplified model cleanup)
- [Perplexica](https://github.com/ItzCrazyKns/Perplexica) (An AI-powered search engine & an open-source alternative to Perplexity AI)
- [Ollama Chat WebUI for Docker ](https://github.com/oslook/ollama-webui) (Support for local docker deployment, lightweight ollama webui)
- [AI Toolkit for Visual Studio Code](https://aka.ms/ai-tooklit/ollama-docs) (Microsoft-official VSCode extension to chat, test, evaluate models with Ollama support, and use them in your AI applications.)
- [MinimalNextOllamaChat](https://github.com/anilkay/MinimalNextOllamaChat) (Minimal Web UI for Chat and Model Control)
- [Chipper](https://github.com/TilmanGriesel/chipper) AI interface for tinkerers (Ollama, Haystack RAG, Python)
- [ChibiChat](https://github.com/CosmicEventHorizon/ChibiChat) (Kotlin-based Android app to chat with Ollama and Koboldcpp API endpoints)
- [LocalLLM](https://github.com/qusaismael/localllm) (Minimal Web-App to run ollama models on it with a GUI)
- [Ollamazing](https://github.com/buiducnhat/ollamazing) (Web extension to run Ollama models)
- [OpenDeepResearcher-via-searxng](https://github.com/benhaotang/OpenDeepResearcher-via-searxng) (A Deep Research equivalent endpoint with Ollama support for running locally)
- [1Panel](https://github.com/1Panel-dev/1Panel/) (Web-based Linux Server Management Tool)
- [AstrBot](https://github.com/Soulter/AstrBot/) (User-friendly LLM-based multi-platform chatbot with a WebUI, supporting RAG, LLM agents, and plugins integration)
- [Reins](https://github.com/ibrahimcetin/reins) (Easily tweak parameters, customize system prompts per chat, and enhance your AI experiments with reasoning model support.)
- [Flufy](https://github.com/Aharon-Bensadoun/Flufy) (A beautiful chat interface for interacting with Ollama's API. Built with React, TypeScript, and Material-UI.)
- [Ellama](https://github.com/zeozeozeo/ellama) (Friendly native app to chat with an Ollama instance)
- [screenpipe](https://github.com/mediar-ai/screenpipe) Build agents powered by your screen history
- [Ollamb](https://github.com/hengkysteen/ollamb) (Simple yet rich in features, cross-platform built with Flutter and designed for Ollama. Try the [web demo](https://hengkysteen.github.io/demo/ollamb/).)
- [Writeopia](https://github.com/Writeopia/Writeopia) (Text editor with integration with Ollama)
- [AppFlowy](https://github.com/AppFlowy-IO/AppFlowy) (AI collaborative workspace with Ollama, cross-platform and self-hostable)
- [Lumina](https://github.com/cushydigit/lumina.git) (A lightweight, minimal React.js frontend for interacting with Ollama servers)
- [Tiny Notepad](https://pypi.org/project/tiny-notepad) (A lightweight, notepad-like interface to chat with ollama available on PyPI)
- [macLlama (macOS native)](https://github.com/hellotunamayo/macLlama) (A native macOS GUI application for interacting with Ollama models, featuring a chat interface.)
- [GPTranslate](https://github.com/philberndt/GPTranslate) (A fast and lightweight, AI powered desktop translation application written with Rust and Tauri. Features real-time translation with OpenAI/Azure/Ollama.)
- [ollama launcher](https://github.com/NGC13009/ollama-launcher) (A launcher for Ollama, aiming to provide users with convenient functions such as ollama server launching, management, or configuration.)
- [ai-hub](https://github.com/Aj-Seven/ai-hub) (AI Hub supports multiple models via API keys and Chat support via Ollama API.)
- [Mayan EDMS](https://gitlab.com/mayan-edms/mayan-edms) (Open source document management system to organize, tag, search, and automate your files with powerful Ollama driven workflows.)
- [Serene Pub](https://github.com/doolijb/serene-pub) (Beginner friendly, open source AI Roleplaying App for Windows, Mac OS and Linux. Search, download and use models with Ollama all inside the app.)
- [Andes](https://github.com/aqerd/andes) (A Visual Studio Code extension that provides a local UI interface for Ollama models)
- [Clueless](https://github.com/KashyapTan/clueless) (Open Source & Local Cluely: A desktop application LLM assistant to help you talk to anything on your screen using locally served Ollama models. Also undetectable to screenshare)
- [ollama-co2](https://github.com/carbonatedWaterOrg/ollama-co2) (FastAPI web interface for monitoring and managing local and remote Ollama servers with real-time model monitoring and concurrent downloads)
- [Hillnote](https://hillnote.com) (A Markdown-first workspace designed to supercharge your AI workflow. Create documents ready to integrate with Claude, ChatGPT, Gemini, Cursor, and more - all while keeping your work on your device.)
### Chat Interfaces
### Cloud
#### Web
- [Open WebUI](https://github.com/open-webui/open-webui) - Extensible, self-hosted AI interface
- [Onyx](https://github.com/onyx-dot-app/onyx) - Connected AI workspace
- [LibreChat](https://github.com/danny-avila/LibreChat) - Enhanced ChatGPT clone with multi-provider support
- [Lobe Chat](https://github.com/lobehub/lobe-chat) - Modern chat framework with plugin ecosystem ([docs](https://lobehub.com/docs/self-hosting/examples/ollama))
- [BoltAI for Mac](https://boltai.com) - AI chat client for Mac
- [IntelliBar](https://intellibar.app/) - AI-powered assistant for macOS
- [Kerlig AI](https://www.kerlig.com/) - AI writing assistant for macOS
- [Hillnote](https://hillnote.com) - Markdown-first AI workspace
- [Perfect Memory AI](https://www.perfectmemory.ai/) - Productivity AI personalized by screen and meeting history
#### Mobile
- [Ollama Android Chat](https://github.com/sunshine0523/OllamaServer) - One-click Ollama on Android
> SwiftChat, Enchanted, Maid, Ollama App, Reins, and ConfiChat listed above also support mobile platforms.
### Code Editors & Development
- [Cline](https://github.com/cline/cline) - VS Code extension for multi-file/whole-repo coding
- [Continue](https://github.com/continuedev/continue) - Open-source AI code assistant for any IDE
- [Void](https://github.com/voideditor/void) - Open source AI code editor, Cursor alternative
- [Copilot for Obsidian](https://github.com/logancyang/obsidian-copilot) - AI assistant for Obsidian
- [twinny](https://github.com/rjmacarthy/twinny) - Copilot and Copilot chat alternative
- [gptel Emacs client](https://github.com/karthink/gptel) - LLM client for Emacs
- [Ollama Copilot](https://github.com/bernardo-bruning/ollama-copilot) - Use Ollama as GitHub Copilot
- [Obsidian Local GPT](https://github.com/pfrankov/obsidian-local-gpt) - Local AI for Obsidian
- [Ellama Emacs client](https://github.com/s-kostyaev/ellama) - LLM tool for Emacs
- [orbiton](https://github.com/xyproto/orbiton) - Config-free text editor with Ollama tab completion
- [AI ST Completion](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) - Sublime Text 4 AI assistant
- [VT Code](https://github.com/vinhnx/vtcode) - Rust-based terminal coding agent with Tree-sitter
- [QodeAssist](https://github.com/Palm1r/QodeAssist) - AI coding assistant for Qt Creator
- [AI Toolkit for VS Code](https://aka.ms/ai-tooklit/ollama-docs) - Microsoft-official VS Code extension
- [Open Interpreter](https://docs.openinterpreter.com/language-model-setup/local-models/ollama) - Natural language interface for computers
### Libraries & SDKs
- [LiteLLM](https://github.com/BerriAI/litellm) - Unified API for 100+ LLM providers
- [Semantic Kernel](https://github.com/microsoft/semantic-kernel/tree/main/python/semantic_kernel/connectors/ai/ollama) - Microsoft AI orchestration SDK
- [LangChainGo](https://github.com/tmc/langchaingo/) - Go LangChain ([example](https://github.com/tmc/langchaingo/tree/main/examples/ollama-completion-example))
- [Spring AI](https://github.com/spring-projects/spring-ai) - Spring framework AI support ([docs](https://docs.spring.io/spring-ai/reference/api/chat/ollama-chat.html))
- [LangChain](https://python.langchain.com/docs/integrations/chat/ollama/) and [LangChain.js](https://js.langchain.com/docs/integrations/chat/ollama/) with [example](https://js.langchain.com/docs/tutorials/local_rag/)
- [Ollama for Ruby](https://github.com/crmne/ruby_llm) - Ruby LLM library
- [any-llm](https://github.com/mozilla-ai/any-llm) - Unified LLM interface by Mozilla
- [OllamaSharp for .NET](https://github.com/awaescher/OllamaSharp) - .NET SDK
- [Ollama-rs for Rust](https://github.com/pepperoni21/ollama-rs) - Rust SDK
- [LangChain for .NET](https://github.com/tryAGI/LangChain) - .NET LangChain ([example](https://github.com/tryAGI/LangChain/blob/main/examples/LangChain.Samples.OpenAI/Program.cs))
- [chromem-go](https://github.com/philippgille/chromem-go) - Go vector database with Ollama embeddings ([example](https://github.com/philippgille/chromem-go/tree/v0.5.0/examples/rag-wikipedia-ollama))
- [LlmTornado](https://github.com/lofcz/llmtornado) - Unified C# interface for multiple inference APIs
- [Ollama4j for Java](https://github.com/ollama4j/ollama4j) - Java SDK
- [Ollama for Laravel](https://github.com/cloudstudio/ollama-laravel) - Laravel integration
- [Ollama for Swift](https://github.com/mattt/ollama-swift) - Swift SDK
- [LlamaIndex](https://docs.llamaindex.ai/en/stable/examples/llm/ollama/) and [LlamaIndexTS](https://ts.llamaindex.ai/modules/llms/available_llms/ollama) - Data framework for LLM apps
- [Haystack](https://github.com/deepset-ai/haystack-integrations/blob/main/integrations/ollama.md) - AI pipeline framework
- [Firebase Genkit](https://firebase.google.com/docs/genkit/plugins/ollama) - Google AI framework
- [Ollama-hpp for C++](https://github.com/jmont-dev/ollama-hpp) - C++ SDK
- [PromptingTools.jl](https://github.com/svilupp/PromptingTools.jl) - Julia LLM toolkit ([example](https://svilupp.github.io/PromptingTools.jl/dev/examples/working_with_ollama))
- [Ollama for R - rollama](https://github.com/JBGruber/rollama) - R SDK
- [Portkey](https://portkey.ai/docs/welcome/integration-guides/ollama) - AI gateway
- [Raycast extension](https://github.com/MassimilianoPasquini97/raycast_ollama) - Ollama in Raycast
- [Painting Droid](https://github.com/mateuszmigas/painting-droid) - Painting app with AI integrations
- [Serene Pub](https://github.com/doolijb/serene-pub) - AI roleplaying app
- [Mayan EDMS](https://gitlab.com/mayan-edms/mayan-edms) - Document management with Ollama workflows
- [TagSpaces](https://www.tagspaces.org) - File management with [AI tagging](https://docs.tagspaces.org/ai/)
### Observability & Monitoring
- [Opik](https://www.comet.com/docs/opik/cookbook/ollama) - Debug, evaluate, and monitor LLM applications
- [OpenLIT](https://github.com/openlit/openlit) - OpenTelemetry-native monitoring for Ollama and GPUs
- [Lunary](https://lunary.ai/docs/integrations/ollama) - LLM observability with analytics and PII masking
- [Langfuse](https://langfuse.com/docs/integrations/ollama) - Open source LLM observability
- [HoneyHive](https://docs.honeyhive.ai/integrations/ollama) - AI observability and evaluation for agents
- [MLflow Tracing](https://mlflow.org/docs/latest/llms/tracing/index.html#automatic-tracing) - Open source LLM observability
### Database & Embeddings
- [pgai](https://github.com/timescale/pgai) - PostgreSQL as a vector database ([guide](https://github.com/timescale/pgai/blob/main/docs/vectorizer-quick-start.md))
- [MindsDB](https://github.com/mindsdb/mindsdb/blob/staging/mindsdb/integrations/handlers/ollama_handler/README.md) - Connect Ollama with 200+ data platforms
- [chromem-go](https://github.com/philippgille/chromem-go/blob/v0.5.0/embed_ollama.go) - Embeddable vector database for Go ([example](https://github.com/philippgille/chromem-go/tree/v0.5.0/examples/rag-wikipedia-ollama))
- [Harbor](https://github.com/av/harbor) - Containerized LLM toolkit with Ollama as default backend
### Tutorial
- [handy-ollama](https://github.com/datawhalechina/handy-ollama) (Chinese Tutorial for Ollama by [Datawhale ](https://github.com/datawhalechina) - China's Largest Open Source AI Learning Community)
- [Ollama Mixture of Experts (MOE) in 50 lines of code](https://github.com/rapidarchitect/ollama_moe)
- [vim-intelligence-bridge](https://github.com/pepo-ec/vim-intelligence-bridge) Simple interaction of "Ollama" with the Vim editor
- [x-cmd ollama](https://x-cmd.com/mod/ollama)
- [bb7](https://github.com/drunkwcodes/bb7)
- [SwollamaCLI](https://github.com/marcusziade/Swollama) bundled with the Swollama Swift package. [Demo](https://github.com/marcusziade/Swollama?tab=readme-ov-file#cli-usage)
- [aichat](https://github.com/sigoden/aichat) All-in-one LLM CLI tool featuring Shell Assistant, Chat-REPL, RAG, AI tools & agents, with access to OpenAI, Claude, Gemini, Ollama, Groq, and more.
- [PowershAI](https://github.com/rrg92/powershai) PowerShell module that brings AI to terminal on Windows, including support for Ollama
- [DeepShell](https://github.com/Abyss-c0re/deepshell) Your self-hosted AI assistant. Interactive Shell, Files and Folders analysis.
- [orbiton](https://github.com/xyproto/orbiton) Configuration-free text editor and IDE with support for tab completion with Ollama.
- [orca-cli](https://github.com/molbal/orca-cli) Ollama Registry CLI Application - Browse, pull, and download models from Ollama Registry in your terminal.
- [GGUF-to-Ollama](https://github.com/jonathanhecl/gguf-to-ollama) - Importing GGUF to Ollama made easy (multiplatform)
- [AWS-Strands-With-Ollama](https://github.com/rapidarchitect/ollama_strands) - AWS Strands Agents with Ollama Examples
- [ollama-multirun](https://github.com/attogram/ollama-multirun) - A bash shell script to run a single prompt against any or all of your locally installed ollama models, saving the output and performance statistics as easily navigable web pages. ([Demo](https://attogram.github.io/ai_test_zone/))
- [ollama-bash-toolshed](https://github.com/attogram/ollama-bash-toolshed) - Bash scripts to chat with tool using models. Add new tools to your shed with ease. Runs on Ollama.
- [hle-eval-ollama](https://github.com/mags0ft/hle-eval-ollama) - Runs benchmarks like "Humanity's Last Exam" (HLE) on your favorite local Ollama models and evaluates the quality of their responses
- [VT Code](https://github.com/vinhnx/vtcode) - VT Code is a Rust-based terminal coding agent with semantic code intelligence via Tree-sitter. Ollama integration for running local/cloud models with configurable endpoints.
### Apple Vision Pro
- [SwiftChat](https://github.com/aws-samples/swift-chat) (Cross-platform AI chat app supporting Apple Vision Pro via "Designed for iPad")
- [pgai](https://github.com/timescale/pgai) - PostgreSQL as a vector database (Create and search embeddings from Ollama models using pgvector)
- [Get started guide](https://github.com/timescale/pgai/blob/main/docs/vectorizer-quick-start.md)
- [MindsDB](https://github.com/mindsdb/mindsdb/blob/staging/mindsdb/integrations/handlers/ollama_handler/README.md) (Connects Ollama models with nearly 200 data platforms and apps)
- [chromem-go](https://github.com/philippgille/chromem-go/blob/v0.5.0/embed_ollama.go) with [example](https://github.com/philippgille/chromem-go/tree/v0.5.0/examples/rag-wikipedia-ollama)
- [Kangaroo](https://github.com/dbkangaroo/kangaroo) (AI-powered SQL client and admin tool for popular databases)
- [LangChain](https://python.langchain.com/docs/integrations/chat/ollama/) and [LangChain.js](https://js.langchain.com/docs/integrations/chat/ollama/) with [example](https://js.langchain.com/docs/tutorials/local_rag/)
- [Yacana](https://remembersoftwares.github.io/yacana/) (User-friendly multi-agent framework for brainstorming and executing predetermined flows with built-in tool integration)
- [Strands Agents](https://github.com/strands-agents/sdk-python) (A model-driven approach to building AI agents in just a few lines of code)
- [Spring AI](https://github.com/spring-projects/spring-ai) with [reference](https://docs.spring.io/spring-ai/reference/api/chat/ollama-chat.html) and [example](https://github.com/tzolov/ollama-tools)
- [LangChainGo](https://github.com/tmc/langchaingo/) with [example](https://github.com/tmc/langchaingo/tree/main/examples/ollama-completion-example)
- [LangChain4j](https://github.com/langchain4j/langchain4j) with [example](https://github.com/langchain4j/langchain4j-examples/tree/main/ollama-examples/src/main/java)
- [LangChainRust](https://github.com/Abraxas-365/langchain-rust) with [example](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/llm_ollama.rs)
- [LangChain for .NET](https://github.com/tryAGI/LangChain) with [example](https://github.com/tryAGI/LangChain/blob/main/examples/LangChain.Samples.OpenAI/Program.cs)
- [LlamaIndex](https://docs.llamaindex.ai/en/stable/examples/llm/ollama/) and [LlamaIndexTS](https://ts.llamaindex.ai/modules/llms/available_llms/ollama)
- [LiteLLM](https://github.com/BerriAI/litellm)
- [OllamaFarm for Go](https://github.com/presbrey/ollamafarm)
- [OllamaSharp for .NET](https://github.com/awaescher/OllamaSharp)
- [Ollama for Ruby](https://github.com/gbaptista/ollama-ai)
- [Ollama-rs for Rust](https://github.com/pepperoni21/ollama-rs)
- [Ollama-hpp for C++](https://github.com/jmont-dev/ollama-hpp)
- [Ollama4j for Java](https://github.com/ollama4j/ollama4j)
- [PromptingTools.jl](https://github.com/svilupp/PromptingTools.jl) with an [example](https://svilupp.github.io/PromptingTools.jl/dev/examples/working_with_ollama)
- [Agents-Flex for Java](https://github.com/agents-flex/agents-flex) with [example](https://github.com/agents-flex/agents-flex/tree/main/agents-flex-llm/agents-flex-llm-ollama/src/test/java/com/agentsflex/llm/ollama)
- [Parakeet](https://github.com/parakeet-nest/parakeet) is a GoLang library, made to simplify the development of small generative AI applications with Ollama.
- [Haverscript](https://github.com/andygill/haverscript) with [examples](https://github.com/andygill/haverscript/tree/main/examples)
- [Ollama for Swift](https://github.com/mattt/ollama-swift)
- [Swollama for Swift](https://github.com/marcusziade/Swollama) with [DocC](https://marcusziade.github.io/Swollama/documentation/swollama/)
- [Ollama for Haskell](https://github.com/tusharad/ollama-haskell)
- [multi-llm-ts](https://github.com/nbonamy/multi-llm-ts) (A Typescript/JavaScript library allowing access to different LLM in a unified API)
- [LlmTornado](https://github.com/lofcz/llmtornado) (C# library providing a unified interface for major FOSS & Commercial inference APIs)
- [Ollama for Zig](https://github.com/dravenk/ollama-zig)
- [Abso](https://github.com/lunary-ai/abso) (OpenAI-compatible TypeScript SDK for any LLM provider)
- [Nichey](https://github.com/goodreasonai/nichey) is a Python package for generating custom wikis for your research topic
- [Ollama for D](https://github.com/kassane/ollama-d)
- [OllamaPlusPlus](https://github.com/HardCodeDev777/OllamaPlusPlus) (Very simple C++ library for Ollama)
- [any-llm](https://github.com/mozilla-ai/any-llm) (A single interface to use different llm providers by [mozilla.ai](https://www.mozilla.ai/))
- [any-agent](https://github.com/mozilla-ai/any-agent) (A single interface to use and evaluate different agent frameworks by [mozilla.ai](https://www.mozilla.ai/))
- [Neuro SAN](https://github.com/cognizant-ai-lab/neuro-san-studio) (Data-driven multi-agent orchestration framework) with [example](https://github.com/cognizant-ai-lab/neuro-san-studio/blob/main/docs/user_guide.md#ollama)
- [achatbot-go](https://github.com/ai-bot-pro/achatbot-go) a multimodal(text/audio/image) chatbot.
- [Ollama Bash Lib](https://github.com/attogram/ollama-bash-lib) - A Bash Library for Ollama. Run LLM prompts straight from your shell, and more
### Mobile
- [SwiftChat](https://github.com/aws-samples/swift-chat) (Lightning-fast Cross-platform AI chat app with native UI for Android, iOS, and iPad)
- [Ollama App](https://github.com/JHubi1/ollama-app) (Modern and easy-to-use multi-platform client for Ollama)
- [ConfiChat](https://github.com/1runeberg/confichat) (Lightweight, standalone, multi-platform, and privacy-focused LLM chat interface with optional encryption)
- [Ollama Android Chat](https://github.com/sunshine0523/OllamaServer) (No need for Termux, start the Ollama service with one click on an Android device)
- [Reins](https://github.com/ibrahimcetin/reins) (Easily tweak parameters, customize system prompts per chat, and enhance your AI experiments with reasoning model support.)
- [Plasmoid Ollama Control](https://github.com/imoize/plasmoid-ollamacontrol) (KDE Plasma extension that allows you to quickly manage/control Ollama model)
- [AI Telegram Bot](https://github.com/tusharhero/aitelegrambot) (Telegram bot using Ollama in backend)
- [AI ST Completion](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (Sublime Text 4 AI assistant plugin with Ollama support)
- [ChatGPTBox: All in one browser extension](https://github.com/josStorer/chatGPTBox) with [Integrating Tutorial](https://github.com/josStorer/chatGPTBox/issues/616#issuecomment-1975186467)
- [Discord AI chat/moderation bot](https://github.com/rapmd73/Companion) Chat/moderation bot written in python. Uses Ollama to create personalities.
- [Headless Ollama](https://github.com/nischalj10/headless-ollama) (Scripts to automatically install ollama client & models on any OS for apps that depend on ollama server)
- [Terraform AWS Ollama & Open WebUI](https://github.com/xuyangbocn/terraform-aws-self-host-llm) (A Terraform module to deploy on AWS a ready-to-use Ollama service, together with its front-end Open WebUI service.)
- [Local AI Helper](https://github.com/ivostoykov/localAI) (Chrome and Firefox extensions that enable interactions with the active tab and customisable API endpoints. Includes secure storage for user prompts.)
- [LSP-AI](https://github.com/SilasMarvin/lsp-ai) (Open-source language server for AI-powered functionality)
- [QodeAssist](https://github.com/Palm1r/QodeAssist) (AI-powered coding assistant plugin for Qt Creator)
- [LLM Telegram Bot](https://github.com/innightwolfsleep/llm_telegram_bot) (telegram bot, primary for RP. Oobabooga-like buttons, [A1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui) API integration e.t.c)
- [mcp-llm](https://github.com/sammcj/mcp-llm) (MCP Server to allow LLMs to call other LLMs)
- [SimpleOllamaUnity](https://github.com/HardCodeDev777/SimpleOllamaUnity) (Unity Engine extension for communicating with Ollama in a few lines of code. Also works at runtime)
- [UnityCodeLama](https://github.com/HardCodeDev777/UnityCodeLama) (Unity Edtior tool to analyze scripts via Ollama)
- [NativeMind](https://github.com/NativeMindBrowser/NativeMindExtension) (Private, on-device AI Assistant, no cloud dependencies)
- [GMAI - Gradle Managed AI](https://gmai.premex.se/) (Gradle plugin for automated Ollama lifecycle management during build phases)
- [NOMYO Router](https://github.com/nomyo-ai/nomyo-router) (A transparent Ollama proxy with model deployment aware routing which auto-manages multiple Ollama instances in a given network)
### Supported backends
- [llama.cpp](https://github.com/ggml-org/llama.cpp) project founded by Georgi Gerganov.
### Observability
- [Opik](https://www.comet.com/docs/opik/cookbook/ollama) is an open-source platform to debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards. Opik supports native intergration to Ollama.
- [Lunary](https://lunary.ai/docs/integrations/ollama) is the leading open-source LLM observability platform. It provides a variety of enterprise-grade features such as real-time analytics, prompt templates management, PII masking, and comprehensive agent tracing.
- [OpenLIT](https://github.com/openlit/openlit) is an OpenTelemetry-native tool for monitoring Ollama Applications & GPUs using traces and metrics.
- [HoneyHive](https://docs.honeyhive.ai/integrations/ollama) is an AI observability and evaluation platform for AI agents. Use HoneyHive to evaluate agent performance, interrogate failures, and monitor quality in production.
- [Langfuse](https://langfuse.com/docs/integrations/ollama) is an open source LLM observability platform that enables teams to collaboratively monitor, evaluate and debug AI applications.
- [MLflow Tracing](https://mlflow.org/docs/latest/llms/tracing/index.html#automatic-tracing) is an open source LLM observability tool with a convenient API to log and visualize traces, making it easy to debug and evaluate GenAI applications.
compactionSystemPrompt="Summarize the archived part of an Ollama agent conversation. Preserve user goals, decisions, files, commands, tool results, and unresolved tasks needed to continue. Omit private reasoning and return only the summary."
returnfmt.Errorf("prompt is too large for the current context (~%d/%d tokens). Reduce the system prompt or message history, compact the conversation, or use a model with a larger context",estimated,contextWindow)
returnfmt.Errorf("history is still too large after compaction (~%d/%d tokens). Start a fresh request, reduce the system prompt or history, or use a model with a larger context",estimated,contextWindow)
alreadyTruncated:=strings.Repeat("x",7000)+"\n\n[tool output truncated: showing first ~100 tokens and last ~100 tokens; omitted ~99999 tokens. Use a narrower command, line range, or search query if more detail is needed.]\n\n"+strings.Repeat("y",7000)
returnfmt.Sprintf("%s omitted ~%d tokens. Use a narrower command, line range, or search query if more detail is needed.]",toolOutputFullOmissionPrefix,approximateTokensFromRunes(len([]rune(content))))
}
iflen(content)<=maxRunes{
returncontent
}
runes:=[]rune(content)
iflen(runes)<=maxRunes{
returncontent
}
head:=maxRunes*3/4
tail:=maxRunes-head
omitted:=len(runes)-head-tail
marker:=fmt.Sprintf(
"\n\n[tool output truncated: showing first ~%d tokens and last ~%d tokens; omitted ~%d tokens. Use a narrower command, line range, or search query if more detail is needed.]\n\n",
"\n\n[tool output truncated: showing first ~1500 tokens; omitted ~999 tokens. Use a narrower command, line range, or search query if more detail is needed.]\n\n"+
t.Fatalf("first message should be compaction summary tool call: %#v",nextRequestMessages[0])
}
ifnextRequestMessages[1].Role!="tool"||nextRequestMessages[1].ToolName!=CompactionToolName||!strings.Contains(nextRequestMessages[1].Content,"older history summarized"){
t.Fatalf("second message should be compaction summary result: %#v",nextRequestMessages[1])
returnagent.ToolResult{Content:bashContentWithError(sb.String(),"Error: command timed out after "+bashTimeout.String()),WorkingDir:finalWorkingDir},nil
}
ifctx.Err()==context.Canceled{
returnagent.ToolResult{Content:bashContentWithError(sb.String(),"Error: command was canceled"),WorkingDir:finalWorkingDir},nil
}
iferrors.Is(err,exec.ErrWaitDelay){
_=killBashCommand(cmd)
returnagent.ToolResult{Content:bashContentWithError(sb.String(),"Error: command output pipes did not close after "+bashWaitDelay.String()),WorkingDir:finalWorkingDir},nil
CI builds with Xcode 14.1 for OS compatibility prior to v13. If you want to manually build v11+ support, you can download the older Xcode [here](https://developer.apple.com/services-account/download?path=/Developer_Tools/Xcode_14.1/Xcode_14.1.xip), extract, then `mv ./Xcode.app /Applications/Xcode_14.1.0.app` then activate with:
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