* broad lint fixes to sidestep CI scope glitch
* runner: Remove CGO engines, use llama-server exclusively for GGML models
Remove the vendored GGML and llama.cpp backend, CGO runner, Go model
implementations, and sample. llama-server (built from upstream llama.cpp via
FetchContent) is now the sole inference engine for GGUF-based models.
(Safetensor based models continue to run on the new MLX engine.) This allows
us to more rapidly pick up new capabilities and fixes from llama.cpp as they
come out.
On windows this now requires recent AMD driver versions to support ROCm v7 as
llama.cpp currently does not support building against v6.
* llama/compat: load Ollama-format GGUFs in llama-server
Squashed from upstream/jmorganca/llama-compat on 2026-04-29.
Source tip: 0c33775d37.
Original source commits:
- 25223160d llama/compat: add in-memory shim so llama-server can load Ollama-format GGUFs
- 7449b539a llm,server: route Ollama-format gemma3 blobs through llama/compat
- 436f2e2b1 llama/compat: make patch-apply idempotent
- 8c2c9d4c8 llama/compat: extend gemma3 handler to cover 1B and 270M blobs
- 021389f7b llama/compat: shrink clip.cpp injection from 18 lines to 1
- 61b367ec2 llama/compat: shrink patch to pure call-site hooks (34 -> 20 lines)
- 36049361c llama/compat: simplify shim (gemma3-tested)
- 8fa664865 llama/compat: add qwen35moe text handler
- db0c74530 llama/compat: add qwen35moe vision (clip) support
- 2a388da77 llama/compat: split shared infra into a util TU
- 9a69a17dc llama/compat: document non-public API dependencies
- d0f38a915 llama/compat: add gpt-oss and lfm2 handlers
- 086071822 llama/compat: add mistral3 text handler (vision TODO)
- 63bde9ff7 llama/compat: add mistral3 vision (clip) support
- 3a57b89d5 llama/compat: apply LLaMA RoPE permute to mistral3 vision Q/K
- 99cb87439 llama/compat: add qwen35, gemma4, deepseek-ocr handlers
- 2c7850dba llama/compat: add nemotron_h_moe handler (latent FFN + MTP skip)
- 9e3b54225 llama/compat: add llama4 text + clip handlers
- 034fee349 llama/compat: add gemma4 clip handler (gemma4v projector)
- 9945c5a93 server: remove dhiltgen/* compat redirect table
- 5d4539101 llama/compat: rewrite gemma4 tokenizer model to BPE
- 7e0765327 llama/compat: add glm-ocr text handler + text-loader load-op hook
- f1bd1a25a llama/compat: add glm-ocr clip handler (glm4v projector)
- 4b5cf3420 llama/compat: collapse text-loader hook back to one new patch line
- eb4ecf4fc llama/compat: extend gemma4 clip handler to gemma4a (audio)
- a23a5e76f llama/compat: fix gemma4a per-block norm tensor mapping
- cd2dcaff4 llama/compat: add embeddinggemma handler
- 1ce8a6b26 llama/compat: add qwen3-vl + qwen2.5-vl handlers
- fd98ffa1e llama/compat: add gemma3n + glm4moelite handlers
- cc7bdf0bc llama/compat: handle null buft in maybe_load_tensor
- 0c33775d3 llama/compat: disable mmap when load_op transforms text-side tensors
* refine implementation
* ci: fix windows MLX build
* ci: fix windows llama-server build
* ci: fix windows rocm build
* ci: windows mlx tuning
Shorten long-tail on build, and get OllamaSetup.exe back under 2g limit
* ci: fix windows dependencies
* win: fix dependency gathering
* disable openmp
* win: arm64 cross-compile build
also DRY out CI steps
* scheduler improvements
* ci: improvements from #15982
* win: favor ninja for faster developer builds
* win: fix build
* win: fix arm64 cross-compile
* win: avoid spaces in compiler path
* misc discovery fixes, and bos handling
* lint fixes
* win: fix arm cross-compile build/CI bugs
* llama.cpp update
* win: handle multiple CRT dirs
* vulkan: add windows iGPU detection
* fix creation bugs for patched models, other refactoring work
* tune batch size for better performance
* ci and lint fixes
* fix repeat_last_n bug
* build: revamp build for better developer UX
* amd, sampler, qwen3next fixes
* version bump
* fix mlx build
* revamp GPU discovery
Scanning the output of llama-server is turning out to be too error prone across
llama.cpp updates, so this switches to a thin dynamic library load against the
bundled GGML libraries so more details can be gathered from the API.
* version bump
* missing file
* ci: fix cache miss on rocm build
* refine vulkan dep handling
* fix ps reporting bug on full GPU load
* improve cmake wiring for customized local builds
* version bump
* docker build arg cleanup
* improve windows exit error logs
* fix community gemma4 support and ci flakes
* fix mlx unit test
* tighten up ps logic to avoid double counting fit log lines
* version bump
* fix ps view for full gpu layer offload
* add MTP wiring for llama-server and create with GGUFs
* pick best template by capabilities
* version bump
* ci: harden apt repos
* remove unused cpu core discovery
* adjust batch default logic to reduce OOMs
* support larger tool calls
* fix audio support, template show
* qwen35 mtp patch support
* flesh out dtypes
* rocm deps
* version bump
* lint fix
* block broken gfx1150 on windows
* fix qwen3.5 moe mtp tensors in patch
* mmproj oom fallback and vulkan on by default
* qwen MTP compat fix
* version bump
* ci: fix WoA cross-compile
* ci: workaround ui tool in cross-compile
* version bump
* win: enable OpenMP for CPU builds
* build: improve developer UX
* ci: windows path workaround for CPU build
* win: fix WoA dependencies
* win: fix large offset reads for mmproj patched loads
* version bump
* fix vulkan dup detection
* add OLLAMA_IGPU_ENABLE and largely disable iGPUs by default
* opt-in MTP, win large offset, integraton fixes
* fix unit test scheduler interaction hang
* fix multi-gpu filtering
* version bump
* review comments
* fix thinking level
* fix linux rocm ordering and granite 3.3 template
* version bump
* ci fix - non-shallow MLX checkout
* bypass linux sysfs unit test on windows
---------
Co-authored-by: jmorganca <jmorganca@gmail.com>
* mlx: add laguna model support
* convert: support fp8 safetensors import
Decode HF F8_E4M3 safetensors with block scale companions into GGUF-supported tensor types, and record which output tensors came from FP8 source weights.
Use that source-precision metadata during create quantization: default FP8-sourced GGUFs to Q8_0, keep non-FP8 tensors at their original precision for Q8_0, and promote non-FP8 quantizable tensors to Q8_0 for Q4_K requests.
* ggml: add laguna model support
* server: preserve generate logprobs with builtin parsers
Generate requests were dropping logprob-only chunks whenever a builtin parser buffered visible content. Chat already handled this case, but generate only forwarded chunks with visible response, thinking, or tool-call output.
Keep generate chunks that carry logprobs even when the builtin parser has not flushed visible content yet, and add a regression test that exercises the behavior with a generic thinking parser.
* review comments - perf improvements
* ggml: implement nemotron 3 nano omni
* add poolside integration
* update poolside doc
* adapt to new cache setup
* fix test
* fix test
---------
Co-authored-by: Eva Ho <hoyyeva@gmail.com>
Following up on #15560, this change now has e2b/e4b render differently
from 26b/31b.
For backwards compatibility, we take the existing renderer name `gemma4`
and make it do dynamic resolution based on the model name/size, but the
intended use is for the models to be republished with the renderer
variant specified explicitly: `gemma4-small` or `gemma4-large`.
* 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
* 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>
* 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>
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.
On main, the `RENDERER` and `PARSER` fields from the `Modelfile` don't
get propagated to a new model created with a `req.From` parameter. This
is easily triggered via `ollama run qwen3-coder`, then running some save
command like `/save qwen3-coder-custom`.
Added a regression test for this, and then open the config for the
"from" model in order to use its renderer/parser as a default for the
new model. This will fix the CLI and also API-based creates.
Fixes: https://github.com/ollama/ollama/issues/12792
* Move quantization logic to GGML via new backend
This moves the model aware logic to Go code and calls GGMLs quantization code for model creation.
* Remove "add model quantizations"
This is no longer needed now that quantization is implemented in Go+GGML code directly.
feat: add new Ollama engine using ggml through cgo
This change introduces a new way to run pretrained models. It introduces 3 high level interfaces and a bunch of smaller helper interfaces to facilitate this.
- `model.Model` defines the interface for a model architecture. Models such as `llama` and `mllama`, which are provided as examples, can implement the model's forward propagation in the `Forward` method. This method will be called to generate completions. This interface can be found in `model/model.go`
- `ml.Backend` defines the interface for a backend tensor library, in this case `ggml`. Among other things, a Backend is responsible for loading a pretrained model into hardware (GPU, CPU, etc) and providing an interface for Models to access loaded tensors. This interface can be found in `ml/backend.go`
- `ml.Tensor` defines the interface for a tensor and tensor operations
This is the first implementation of the new engine. Follow up PRs will implement more features:
- non-greedy sampling (#8410)
- integration with Ollama and KV caching (#8301)
- more model support (#9080) with more coming soon
Co-authored-by: Bruce MacDonald <brucewmacdonald@gmail.com>
multiple templates may appear in a model if a model is created from
another model that 1) has an autodetected template and 2) defines a
custom template
