1. 03 Apr, 2025 1 commit
    • Bruce MacDonald's avatar
      model: support for mistral-small in the ollama runner · 6bd0a983
      Bruce MacDonald authored
      Mistral is a popular research lab making open source models. This updates
      the forward pass of llama architecture models to support both llama models
      and mistral models by accounting for additional metadata present in mistral
      models, and finding the correct dimensions for the output projection.
      6bd0a983
  2. 11 Mar, 2025 2 commits
  3. 08 Mar, 2025 1 commit
  4. 07 Mar, 2025 1 commit
    • Jesse Gross's avatar
      model: Don't unconditionally add special tokens · b70fc4d5
      Jesse Gross authored
      We sometimes tokenize partial strings. For example, with
      multimodal inputs, we split the input string around the images
      and then tokenize each piece. In these cases, we should only add
      the special tokens on the first piece.
      b70fc4d5
  5. 27 Feb, 2025 1 commit
  6. 14 Feb, 2025 2 commits
    • Jesse Gross's avatar
      vocab: Use int32 for special tokens · 7916f550
      Jesse Gross authored
      Special tokens are currently read as uint32 from the model metadata.
      However, all other parts of the system (including the tokenizer) use
      int32 to represent tokens so it is impossible to represent the high
      portion of the unsigned range. For consistency and to avoid casts,
      we should just use int32 everywhere.
      7916f550
    • Michael Yang's avatar
      next ollama runner (#7913) · 58245413
      Michael Yang authored
      
      
      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: default avatarBruce MacDonald <brucewmacdonald@gmail.com>
      58245413