ramalama-stack

An external provider for Llama Stack allowing for the use of RamaLama for inference.

Installing

You can install ramalama-stack from PyPI via pip install ramalama-stack

This will install Llama Stack and RamaLama as well if they are not installed already.

Usage

Warning

The following workaround is currently needed to run this provider - see #53 for more details

curl --create-dirs --output ~/.llama/providers.d/remote/inference/ramalama.yaml https://raw.githubusercontent.com/containers/ramalama-stack/refs/tags/v0.1.3/src/ramalama_stack/providers.d/remote/inference/ramalama.yaml
curl --create-dirs --output ~/.llama/distributions/ramalama/ramalama-run.yaml https://raw.githubusercontent.com/containers/ramalama-stack/refs/tags/v0.1.3/src/ramalama_stack/ramalama-run.yaml

1. First you will need a RamaLama server running - see the RamaLama project docs for more information.

2. Ensure you set your INFERENCE_MODEL environment variable to the name of the model you have running via RamaLama.

3. You can then run the RamaLama external provider via llama stack run ~/.llama/distributions/ramalama/ramalama-run.yaml

Note

You can also run the RamaLama external provider inside of a container via Podman

podman run \
 --net=host \
 --env RAMALAMA_URL=http://0.0.0.0:8080 \
 --env INFERENCE_MODEL=$INFERENCE_MODEL \
 ramalama/llama-stack

This will start a Llama Stack server which will use port 8321 by default. You can test this works by configuring the Llama Stack Client to run against this server and sending a test request.

• If your client is running on the same machine as the server, you can run llama-stack-client configure --endpoint http://0.0.0.0:8321 --api-key none
• If your client is running on a different machine, you can run llama-stack-client configure --endpoint http://<hostname>:8321 --api-key none
• The client should give you a message similar to Done! You can now use the Llama Stack Client CLI with endpoint <endpoint>
• You can then test the server by running llama-stack-client inference chat-completion --message "tell me a joke" which should return something like

ChatCompletionResponse(
    completion_message=CompletionMessage(
        content='A man walked into a library and asked the librarian, "Do you have any books on Pavlov\'s dogs
and Schrödinger\'s cat?" The librarian replied, "It rings a bell, but I\'m not sure if it\'s here or not."',
        role='assistant',
        stop_reason='end_of_turn',
        tool_calls=[]
    ),
    logprobs=None,
    metrics=[
        Metric(metric='prompt_tokens', value=14.0, unit=None),
        Metric(metric='completion_tokens', value=63.0, unit=None),
        Metric(metric='total_tokens', value=77.0, unit=None)
    ]
)