forked from phoenix-oss/llama-stack-mirror

Ashwin Bharambe c9e5578151

[memory refactor][5/n] Migrate all vector_io providers (#835 )

See https://github.com/meta-llama/llama-stack/issues/827 for the broader
design.

This PR finishes off all the stragglers and migrates everything to the
new naming.

2025-01-22 10:17:59 -08:00

2.4 KiB

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NVIDIA Distribution

The llamastack/distribution-nvidia 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::nvidia`
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`, `remote::model-context-protocol`
vector_io	`inline::faiss`

Environment Variables

The following environment variables can be configured:

LLAMASTACK_PORT: Port for the Llama Stack distribution server (default: 5001)
NVIDIA_API_KEY: NVIDIA API Key (default: ``)

Models

The following models are available by default:

meta-llama/Llama-3-8B-Instruct (meta/llama3-8b-instruct)
meta-llama/Llama-3-70B-Instruct (meta/llama3-70b-instruct)
meta-llama/Llama-3.1-8B-Instruct (meta/llama-3.1-8b-instruct)
meta-llama/Llama-3.1-70B-Instruct (meta/llama-3.1-70b-instruct)
meta-llama/Llama-3.1-405B-Instruct-FP8 (meta/llama-3.1-405b-instruct)
meta-llama/Llama-3.2-1B-Instruct (meta/llama-3.2-1b-instruct)
meta-llama/Llama-3.2-3B-Instruct (meta/llama-3.2-3b-instruct)
meta-llama/Llama-3.2-11B-Vision-Instruct (meta/llama-3.2-11b-vision-instruct)
meta-llama/Llama-3.2-90B-Vision-Instruct (meta/llama-3.2-90b-vision-instruct)

Prerequisite: API Keys

Make sure you have access to a NVIDIA API Key. You can get one by visiting https://build.nvidia.com/.

Running Llama Stack with NVIDIA

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-nvidia \
  --yaml-config /root/my-run.yaml \
  --port $LLAMA_STACK_PORT \
  --env NVIDIA_API_KEY=$NVIDIA_API_KEY

Via Conda

llama stack build --template nvidia --image-type conda
llama stack run ./run.yaml \
  --port 5001 \
  --env NVIDIA_API_KEY=$NVIDIA_API_KEY
  --env INFERENCE_MODEL=$INFERENCE_MODEL

2.4 KiB Raw Blame History