1. 07 Jul, 2025 1 commit
  2. 29 May, 2025 1 commit
    • Devon Rifkin's avatar
      add thinking support to the api and cli (#10584) · 5f57b0ef
      Devon Rifkin authored
      - Both `/api/generate` and `/api/chat` now accept a `"think"`
        option that allows specifying whether thinking mode should be on or
        not
      - Templates get passed this new option so, e.g., qwen3's template can
        put `/think` or `/no_think` in the system prompt depending on the
        value of the setting
      - Models' thinking support is inferred by inspecting model templates.
        The prefix and suffix the parser uses to identify thinking support is
        also automatically inferred from templates
      - Thinking control & parsing is opt-in via the API to prevent breaking
        existing API consumers. If the `"think"` option is not specified, the
        behavior is unchanged from previous versions of ollama
      - Add parsing for thinking blocks in both streaming/non-streaming mode
        in both `/generate` and `/chat`
      - Update the CLI to make use of these changes. Users can pass `--think`
        or `--think=false` to control thinking, or during an interactive
        session they can use the commands `/set think` or `/set nothink`
      - A `--hidethinking` option has also been added to the CLI. This makes
        it easy to use thinking in scripting scenarios like
        `ollama run qwen3 --think --hidethinking "my question here"` where you
        just want to see the answer but still want the benefits of thinking
        models
      5f57b0ef
  3. 20 Mar, 2025 1 commit
  4. 14 Feb, 2025 1 commit
    • 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
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