mirror of https://github.com/meta-llama/llama-stack.git synced 2025-06-28 02:53:30 +00:00

cerebras template update for memory (#792 )

# What does this PR do?

- we no longer have meta-reference as memory provider, update cerebras
template


## Test Plan

```
python llama_stack/scripts/distro_codegen.py
```

## Sources

Please link relevant resources if necessary.


## Before submitting

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

2025-01-16 16:07:53 -08:00

1.9 KiB

Raw Blame History

Cerebras Distribution

The llamastack/distribution-cerebras distribution consists of the following provider configurations.

API	Provider(s)
agents	`inline::meta-reference`
datasetio	`remote::huggingface`, `inline::localfs`
eval	`inline::meta-reference`
inference	`remote::cerebras`
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`

Environment Variables

The following environment variables can be configured:

LLAMA_STACK_PORT: Port for the Llama Stack distribution server (default: 5001)
CEREBRAS_API_KEY: Cerebras API Key (default: ``)

Models

The following models are available by default:

meta-llama/Llama-3.1-8B-Instruct (llama3.1-8b)
meta-llama/Llama-3.3-70B-Instruct (llama-3.3-70b)

Prerequisite: API Keys

Make sure you have access to a Cerebras API Key. You can get one by visiting cloud.cerebras.ai.

Running Llama Stack with Cerebras

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 ./run.yaml:/root/my-run.yaml \
  llamastack/distribution-cerebras \
  --yaml-config /root/my-run.yaml \
  --port $LLAMA_STACK_PORT \
  --env CEREBRAS_API_KEY=$CEREBRAS_API_KEY

Via Conda

llama stack build --template cerebras --image-type conda
llama stack run ./run.yaml \
  --port 5001 \
  --env CEREBRAS_API_KEY=$CEREBRAS_API_KEY

1.9 KiB Raw Blame History