forked from phoenix-oss/llama-stack-mirror
992f865b2e
`EMBEDDING_DEPS` were wrongly associated with `vector_io` providers. They are needed by https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/utils/memory/vector_store.py#L142 and related code and is used by the RAG tool and as such should only be needed by the `inline::rag-runtime` provider.
66 lines
2 KiB
Markdown
66 lines
2 KiB
Markdown
<!-- This file was auto-generated by distro_codegen.py, please edit source -->
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# Cerebras Distribution
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The `llamastack/distribution-cerebras` 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 | `remote::cerebras`, `inline::sentence-transformers` |
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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` |
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| vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
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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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- `CEREBRAS_API_KEY`: Cerebras API Key (default: ``)
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### Models
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The following models are available by default:
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- `meta-llama/Llama-3.1-8B-Instruct (llama3.1-8b)`
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- `meta-llama/Llama-3.3-70B-Instruct (llama-3.3-70b)`
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### Prerequisite: API Keys
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Make sure you have access to a Cerebras API Key. You can get one by visiting [cloud.cerebras.ai](https://cloud.cerebras.ai/).
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## Running Llama Stack with Cerebras
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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 ./run.yaml:/root/my-run.yaml \
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llamastack/distribution-cerebras \
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--yaml-config /root/my-run.yaml \
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--port $LLAMA_STACK_PORT \
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--env CEREBRAS_API_KEY=$CEREBRAS_API_KEY
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```
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### Via Conda
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```bash
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llama stack build --template cerebras --image-type conda
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llama stack run ./run.yaml \
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--port 5001 \
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--env CEREBRAS_API_KEY=$CEREBRAS_API_KEY
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```
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