forked from phoenix-oss/llama-stack-mirror
Addressed comment https://github.com/meta-llama/llama-stack/pull/723#issuecomment-2581902075. cc @yanxi0830 I am not 100% sure if the diff is correct though but this is the result of running `python llama_stack/scripts/distro_codegen.py`. --------- Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
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Cerebras Distribution
The llamastack/distribution-cerebras
distribution consists of the following provider configurations.
API | Provider(s) |
---|---|
agents | inline::meta-reference |
inference | remote::cerebras |
memory | inline::meta-reference |
safety | inline::llama-guard |
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