forked from phoenix-oss/llama-stack-mirror
102 lines
3.7 KiB
Markdown
102 lines
3.7 KiB
Markdown
---
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orphan: true
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---
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<!-- This file was auto-generated by distro_codegen.py, please edit source -->
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# Meta Reference Quantized 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-quantized-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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| datasetio | `remote::huggingface`, `inline::localfs` |
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| eval | `inline::meta-reference` |
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| inference | `inline::meta-reference-quantized` |
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| safety | `inline::llama-guard` |
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| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
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| telemetry | `inline::meta-reference` |
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| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol` |
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| vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
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The only difference vs. the `meta-reference-gpu` distribution is that it has support for more efficient inference -- with fp8, int4 quantization, etc.
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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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- `LLAMA_STACK_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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## 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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-v ~/.llama:/root/.llama \
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llamastack/distribution-meta-reference-quantized-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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-v ~/.llama:/root/.llama \
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llamastack/distribution-meta-reference-quantized-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 `uv 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-quantized-gpu --image-type conda
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llama stack run distributions/meta-reference-quantized-gpu/run.yaml \
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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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llama stack run distributions/meta-reference-quantized-gpu/run-with-safety.yaml \
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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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