forked from phoenix-oss/llama-stack-mirror
5fc2ee6f77
# What does this PR do? Many of the URLs pointing to the Llama Stack's Read The Docs webpages were broken, presumably due to recent refactor of the documentation. This PR fixes all effected URLs throughout the repository.
95 lines
3.2 KiB
Markdown
95 lines
3.2 KiB
Markdown
---
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orphan: true
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---
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# Meta Reference Distribution
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```{toctree}
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:maxdepth: 2
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:hidden:
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self
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```
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The `llamastack/distribution-meta-reference-gpu` distribution consists of the following provider configurations:
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| API | Provider(s) |
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|-----|-------------|
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| agents | `inline::meta-reference` |
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| inference | `inline::meta-reference` |
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| memory | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
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| safety | `inline::llama-guard` |
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| telemetry | `inline::meta-reference` |
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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.
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### Environment Variables
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The following environment variables can be configured:
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- `LLAMASTACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
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- `INFERENCE_MODEL`: Inference model loaded into the Meta Reference server (default: `meta-llama/Llama-3.2-3B-Instruct`)
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- `INFERENCE_CHECKPOINT_DIR`: Directory containing the Meta Reference model checkpoint (default: `null`)
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- `SAFETY_MODEL`: Name of the safety (Llama-Guard) model to use (default: `meta-llama/Llama-Guard-3-1B`)
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- `SAFETY_CHECKPOINT_DIR`: Directory containing the Llama-Guard model checkpoint (default: `null`)
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## Prerequisite: Downloading Models
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Please make sure you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints.
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```
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$ ls ~/.llama/checkpoints
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Llama3.1-8B Llama3.2-11B-Vision-Instruct Llama3.2-1B-Instruct Llama3.2-90B-Vision-Instruct Llama-Guard-3-8B
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Llama3.1-8B-Instruct Llama3.2-1B Llama3.2-3B-Instruct Llama-Guard-3-1B Prompt-Guard-86M
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```
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## Running the Distribution
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You can do this via Conda (build code) or Docker which has a pre-built image.
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### Via Docker
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This method allows you to get started quickly without having to build the distribution code.
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```bash
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LLAMA_STACK_PORT=5001
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docker run \
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-it \
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-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
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llamastack/distribution-meta-reference-gpu \
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--port $LLAMA_STACK_PORT \
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--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
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```
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If you are using Llama Stack Safety / Shield APIs, use:
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```bash
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docker run \
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-it \
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-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
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llamastack/distribution-meta-reference-gpu \
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--port $LLAMA_STACK_PORT \
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--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \
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--env SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
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```
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### Via Conda
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Make sure you have done `pip install llama-stack` and have the Llama Stack CLI available.
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```bash
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llama stack build --template meta-reference-gpu --image-type conda
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llama stack run distributions/meta-reference-gpu/run.yaml \
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--port 5001 \
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--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
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```
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If you are using Llama Stack Safety / Shield APIs, use:
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```bash
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llama stack run distributions/meta-reference-gpu/run-with-safety.yaml \
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--port 5001 \
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--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \
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--env SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
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```
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