forked from phoenix-oss/llama-stack-mirror
272d3359ee
# What does this PR do? The builtin implementation of code interpreter is not robust and has a really weak sandboxing shell (the `bubblewrap` container). Given the availability of better MCP code interpreter servers coming up, we should use them instead of baking an implementation into the Stack and expanding the vulnerability surface to the rest of the Stack. This PR only does the removal. We will add examples with how to integrate with MCPs in subsequent ones. ## Test Plan Existing tests.
125 lines
6.8 KiB
Markdown
125 lines
6.8 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 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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| datasetio | `remote::huggingface`, `inline::localfs` |
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| eval | `inline::meta-reference` |
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| inference | `inline::meta-reference` |
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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::rag-runtime`, `remote::model-context-protocol` |
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| vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
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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: `8321`)
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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 use `llama model list --downloaded` to check that 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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$ llama model list --downloaded
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓
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┃ Model ┃ Size ┃ Modified Time ┃
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┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩
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│ Llama3.2-1B-Instruct:int4-qlora-eo8 │ 1.53 GB │ 2025-02-26 11:22:28 │
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├─────────────────────────────────────────┼──────────┼─────────────────────┤
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│ Llama3.2-1B │ 2.31 GB │ 2025-02-18 21:48:52 │
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├─────────────────────────────────────────┼──────────┼─────────────────────┤
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│ Prompt-Guard-86M │ 0.02 GB │ 2025-02-26 11:29:28 │
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├─────────────────────────────────────────┼──────────┼─────────────────────┤
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│ Llama3.2-3B-Instruct:int4-spinquant-eo8 │ 3.69 GB │ 2025-02-26 11:37:41 │
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├─────────────────────────────────────────┼──────────┼─────────────────────┤
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│ Llama3.2-3B │ 5.99 GB │ 2025-02-18 21:51:26 │
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├─────────────────────────────────────────┼──────────┼─────────────────────┤
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│ Llama3.1-8B │ 14.97 GB │ 2025-02-16 10:36:37 │
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├─────────────────────────────────────────┼──────────┼─────────────────────┤
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│ Llama3.2-1B-Instruct:int4-spinquant-eo8 │ 1.51 GB │ 2025-02-26 11:35:02 │
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├─────────────────────────────────────────┼──────────┼─────────────────────┤
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│ Llama-Guard-3-1B │ 2.80 GB │ 2025-02-26 11:20:46 │
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├─────────────────────────────────────────┼──────────┼─────────────────────┤
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│ Llama-Guard-3-1B:int4 │ 0.43 GB │ 2025-02-26 11:33:33 │
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└─────────────────────────────────────────┴──────────┴─────────────────────┘
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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=8321
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docker run \
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-it \
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--pull always \
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--gpu all \
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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-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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--pull always \
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--gpu all \
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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-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-gpu --image-type conda
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llama stack run distributions/meta-reference-gpu/run.yaml \
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--port 8321 \
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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 8321 \
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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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