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For self-hosted providers like Ollama (or vLLM), the backing server is running a set of models. That server should be treated as the source of truth and the Stack registry should just be a cache for those models. Of course, in production environments, you may not want this (because you know what model you are running statically) hence there's a config boolean to control this behavior. _This is part of a series of PRs aimed at removing the requirement of needing to set `INFERENCE_MODEL` env variables for running Llama Stack server._ ## Test Plan Copy and modify the starter.yaml template / config and enable `refresh_models: true, refresh_models_interval: 10` for the ollama provider. Then, run: ``` LLAMA_STACK_LOGGING=all=debug \ ENABLE_OLLAMA=ollama uv run llama stack run --image-type venv /tmp/starter.yaml ``` See a gargantuan amount of logs, but verify that the provider is periodically refreshing models. Stop and prune a model from ollama server, restart the server. Verify that the model goes away when I call `uv run llama-stack-client models list` |
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| .. | ||
| agents | ||
| batch_inference | ||
| benchmarks | ||
| common | ||
| datasetio | ||
| datasets | ||
| eval | ||
| files | ||
| inference | ||
| inspect | ||
| models | ||
| post_training | ||
| providers | ||
| safety | ||
| scoring | ||
| scoring_functions | ||
| shields | ||
| synthetic_data_generation | ||
| telemetry | ||
| tools | ||
| vector_dbs | ||
| vector_io | ||
| __init__.py | ||
| datatypes.py | ||
| resource.py | ||
| version.py | ||