* 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.
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Co-authored-by: jmorganca <jmorganca@gmail.com>
* 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>
