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This change removes the `llama model` and `llama download` subcommands from the CLI, replacing them with recommendations to use the Hugging Face CLI instead. Rationale for this change: - The model management functionality was largely duplicating what Hugging Face CLI already provides, leading to unnecessary maintenance overhead (except the download source from Meta?) - Maintaining our own implementation required fixing bugs and keeping up with changes in model repositories and download mechanisms - The Hugging Face CLI is more mature, widely adopted, and better maintained - This allows us to focus on the core Llama Stack functionality rather than reimplementing model management tools Changes made: - Removed all model-related CLI commands and their implementations - Updated documentation to recommend using `huggingface-cli` for model downloads - Removed Meta-specific download logic and statements - Simplified the CLI to focus solely on stack management operations Users should now use: - `huggingface-cli download` for downloading models - `huggingface-cli scan-cache` for listing downloaded models This is a breaking change as it removes previously available CLI commands. Signed-off-by: Sébastien Han <seb@redhat.com>
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Meta Reference GPU Distribution
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self
The llamastack/distribution-{{ name }} distribution consists of the following provider configurations:
{{ providers_table }}
Note that you need access to nvidia GPUs to run this distribution. This distribution is not compatible with CPU-only machines or machines with AMD GPUs.
{% if run_config_env_vars %}
Environment Variables
The following environment variables can be configured:
{% for var, (default_value, description) in run_config_env_vars.items() %}
{{ var }}: {{ description }} (default:{{ default_value }}) {% endfor %} {% endif %}
Prerequisite: Downloading Models
Please check that you have llama model checkpoints downloaded in ~/.llama before proceeding. See installation guide here to download the models using the Hugging Face CLI.
## Running the Distribution
You can do this via venv or Docker which has a pre-built image.
### Via Docker
This method allows you to get started quickly without having to build the distribution code.
```bash
LLAMA_STACK_PORT=8321
docker run \
-it \
--pull always \
--gpu all \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ~/.llama:/root/.llama \
-e INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \
llamastack/distribution-{{ name }} \
--port $LLAMA_STACK_PORT
If you are using Llama Stack Safety / Shield APIs, use:
docker run \
-it \
--pull always \
--gpu all \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ~/.llama:/root/.llama \
-e INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \
-e SAFETY_MODEL=meta-llama/Llama-Guard-3-1B \
llamastack/distribution-{{ name }} \
--port $LLAMA_STACK_PORT
Via venv
Make sure you have done uv pip install llama-stack and have the Llama Stack CLI available.
llama stack build --distro {{ name }} --image-type venv
INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \
llama stack run distributions/{{ name }}/run.yaml \
--port 8321
If you are using Llama Stack Safety / Shield APIs, use:
INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \
SAFETY_MODEL=meta-llama/Llama-Guard-3-1B \
llama stack run distributions/{{ name }}/run-with-safety.yaml \
--port 8321