forked from phoenix-oss/llama-stack-mirror

feat(eval api): (2.2/n) delete eval / scoring / scoring_fn apis (#1700 )

# What does this PR do?
- To make it easier, delete existing `eval/scoring/scoring_function`
apis. There will be a bunch of broken impls here. The sequence is:
1. migrate benchmark graders
2. clean up existing scoring functions

- Add a skeleton evaluation impl to make tests pass. 

## Test Plan
tested in following PRs

[//]: # (## Documentation)

2025-03-19 11:04:23 -07:00

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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`
inference	`remote::cerebras`, `inline::sentence-transformers`
safety	`inline::llama-guard`
telemetry	`inline::meta-reference`
tool_runtime	`remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`
vector_io	`inline::faiss`, `remote::chromadb`, `remote::pgvector`

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:

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

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

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