forked from phoenix-oss/llama-stack-mirror

rename LLAMASTACK_PORT to LLAMA_STACK_PORT for consistency with other env vars (#744 )

# What does this PR do?

Rename environment var for consistency

## Test Plan

No regressions

## Sources

## Before submitting

- [X] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [X] Ran pre-commit to handle lint / formatting issues.
- [X] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [X] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.

---------

Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
Co-authored-by: Yuan Tang <terrytangyuan@gmail.com>

2025-01-10 11:09:49 -08:00

3.6 KiB

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Meta Reference Quantized Distribution

:maxdepth: 2
:hidden:

self

The llamastack/distribution-meta-reference-quantized-gpu distribution consists of the following provider configurations:

API	Provider(s)
agents	`inline::meta-reference`
datasetio	`remote::huggingface`, `inline::localfs`
eval	`inline::meta-reference`
inference	`inline::meta-reference-quantized`
memory	`inline::faiss`, `remote::chromadb`, `remote::pgvector`
safety	`inline::llama-guard`
scoring	`inline::basic`, `inline::llm-as-judge`, `inline::braintrust`
telemetry	`inline::meta-reference`
tool_runtime	`remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::memory-runtime`

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.

Environment Variables

The following environment variables can be configured:

LLAMA_STACK_PORT: Port for the Llama Stack distribution server (default: 5001)
INFERENCE_MODEL: Inference model loaded into the Meta Reference server (default: meta-llama/Llama-3.2-3B-Instruct)
INFERENCE_CHECKPOINT_DIR: Directory containing the Meta Reference model checkpoint (default: null)

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-meta-reference-quantized-gpu \
  --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-meta-reference-quantized-gpu \
  --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 meta-reference-quantized-gpu --image-type conda
llama stack run distributions/meta-reference-quantized-gpu/run.yaml \
  --port $LLAMA_STACK_PORT \
  --env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct