e2054d53e4
# What does this PR do? - Addresses issue (#586 ) ## Test Plan ``` python llama_stack/scripts/distro_codegen.py ``` ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Ran pre-commit to handle lint / formatting issues. - [ ] Read the [contributor guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md), Pull Request section? - [ ] Updated relevant documentation. - [ ] Wrote necessary unit or integration tests.
2.8 KiB
| orphan |
|---|
| true |
Meta Reference Quantized Distribution
:maxdepth: 2
:hidden:
self
The llamastack/distribution-{{ name }} distribution consists of the following provider configurations:
{{ providers_table }}
The only difference vs. the meta-reference-gpu distribution is that it has support for more efficient inference -- with fp8, int4 quantization, etc.
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 make sure you have llama model checkpoints downloaded in ~/.llama before proceeding. See installation guide here to download the models. Run llama model list to see the available models to download, and llama model download to download the checkpoints.
$ ls ~/.llama/checkpoints
Llama3.1-8B Llama3.2-11B-Vision-Instruct Llama3.2-1B-Instruct Llama3.2-90B-Vision-Instruct Llama-Guard-3-8B
Llama3.1-8B-Instruct Llama3.2-1B Llama3.2-3B-Instruct Llama-Guard-3-1B Prompt-Guard-86M
Running the Distribution
You can do this via Conda (build code) 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.
LLAMA_STACK_PORT=5001
docker run \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ~/.llama:/root/.llama \
llamastack/distribution-{{ name }} \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
If you are using Llama Stack Safety / Shield APIs, use:
docker run \
-it \
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
-v ~/.llama:/root/.llama \
llamastack/distribution-{{ name }} \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \
--env SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
Via Conda
Make sure you have done pip install llama-stack and have the Llama Stack CLI available.
llama stack build --template {{ name }} --image-type conda
llama stack run distributions/{{ name }}/run.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
If you are using Llama Stack Safety / Shield APIs, use:
llama stack run distributions/{{ name }}/run-with-safety.yaml \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \
--env SAFETY_MODEL=meta-llama/Llama-Guard-3-1B