forked from phoenix-oss/llama-stack-mirror

# What does this PR do?

- Fix typo
- Support Llama 3.3 70B

## Test Plan

Run the following scripts and obtain the test results

Script
```
pytest -s -v --providers inference=sambanova llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_streaming --env SAMBANOVA_API_KEY={API_KEY}
```

Result
```
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_streaming[-sambanova] PASSED

=========================================== 1 passed, 1 warning in 1.26s ============================================
```

Script
```
pytest -s -v --providers inference=sambanova llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_non_streaming --env SAMBANOVA_API_KEY={API_KEY}
```

Result
```
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_non_streaming[-sambanova] PASSED

=========================================== 1 passed, 1 warning in 0.52s ============================================
```

## Sources

Please link relevant resources if necessary.


## Before submitting

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

2025-01-30 09:24:46 -08:00

2.1 KiB

Raw Blame History

orphan
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SambaNova Distribution

:maxdepth: 2
:hidden:

self

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

API	Provider(s)
agents	`inline::meta-reference`
inference	`remote::sambanova`
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:

LLAMASTACK_PORT: Port for the Llama Stack distribution server (default: 5001)
SAMBANOVA_API_KEY: SambaNova.AI API Key (default: ``)

Models

The following models are available by default:

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 (Llama-3.2-11B-Vision-Instruct)
meta-llama/Llama-3.2-90B-Vision-Instruct (Llama-3.2-90B-Vision-Instruct)

Prerequisite: API Keys

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

Running Llama Stack with SambaNova

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 \
  llamastack/distribution-sambanova \
  --port $LLAMA_STACK_PORT \
  --env SAMBANOVA_API_KEY=$SAMBANOVA_API_KEY

Via Conda

llama stack build --template sambanova --image-type conda
llama stack run ./run.yaml \
  --port $LLAMA_STACK_PORT \
  --env SAMBANOVA_API_KEY=$SAMBANOVA_API_KEY

2.1 KiB Raw Blame History