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

Ashwin Bharambe 04de2f84e9

fix: register provider model name and HF alias in run.yaml (#1304 )

Each model known to the system has two identifiers: 

- the `provider_resource_id` (what the provider calls it) -- e.g.,
`accounts/fireworks/models/llama-v3p1-8b-instruct`
- the `identifier` (`model_id`) under which it is registered and gets
routed to the appropriate provider.

We have so far used the HuggingFace repo alias as the standardized
identifier you can use to refer to the model. So in the above example,
we'd use `meta-llama/Llama-3.1-8B-Instruct` as the name under which it
gets registered. This makes it convenient for users to refer to these
models across providers.

However, we forgot to register the _actual_ provider model ID also. You
should be able to route via `provider_resource_id` also, of course.

This change fixes this (somewhat grave) omission.

*Note*: this change is additive -- more aliases work now compared to
before.

## Test Plan

Run the following for distro=(ollama fireworks together)
```
LLAMA_STACK_CONFIG=$distro \
   pytest -s -v tests/client-sdk/inference/test_text_inference.py \
   --inference-model=meta-llama/Llama-3.1-8B-Instruct --vision-inference-model=""
```

2025-02-27 16:39:23 -08:00

2.7 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::rag-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/llama3-8b-instruct (aliases: meta-llama/Llama-3-8B-Instruct)
meta/llama3-70b-instruct (aliases: meta-llama/Llama-3-70B-Instruct)
meta/llama-3.1-8b-instruct (aliases: meta-llama/Llama-3.1-8B-Instruct)
meta/llama-3.1-70b-instruct (aliases: meta-llama/Llama-3.1-70B-Instruct)
meta/llama-3.1-405b-instruct (aliases: meta-llama/Llama-3.1-405B-Instruct-FP8)
meta/llama-3.2-1b-instruct (aliases: meta-llama/Llama-3.2-1B-Instruct)
meta/llama-3.2-3b-instruct (aliases: meta-llama/Llama-3.2-3B-Instruct)
meta/llama-3.2-11b-vision-instruct (aliases: meta-llama/Llama-3.2-11B-Vision-Instruct)
meta/llama-3.2-90b-vision-instruct (aliases: meta-llama/Llama-3.2-90B-Vision-Instruct)
nvidia/llama-3.2-nv-embedqa-1b-v2
nvidia/nv-embedqa-e5-v5
nvidia/nv-embedqa-mistral-7b-v2
snowflake/arctic-embed-l

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

2.7 KiB Raw Blame History