forked from phoenix-oss/llama-stack-mirror
7fe2592795
# 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.
75 lines
2.1 KiB
Markdown
75 lines
2.1 KiB
Markdown
---
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orphan: true
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---
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# SambaNova Distribution
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```{toctree}
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:maxdepth: 2
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:hidden:
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self
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```
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The `llamastack/distribution-sambanova` distribution consists of the following provider configurations.
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| API | Provider(s) |
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|-----|-------------|
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| agents | `inline::meta-reference` |
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| inference | `remote::sambanova` |
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| safety | `inline::llama-guard` |
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| telemetry | `inline::meta-reference` |
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| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime` |
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| vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
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### Environment Variables
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The following environment variables can be configured:
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- `LLAMASTACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
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- `SAMBANOVA_API_KEY`: SambaNova.AI API Key (default: ``)
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### Models
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The following models are available by default:
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- `meta-llama/Llama-3.1-8B-Instruct (Meta-Llama-3.1-8B-Instruct)`
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- `meta-llama/Llama-3.1-70B-Instruct (Meta-Llama-3.1-70B-Instruct)`
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- `meta-llama/Llama-3.1-405B-Instruct-FP8 (Meta-Llama-3.1-405B-Instruct)`
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- `meta-llama/Llama-3.2-1B-Instruct (Meta-Llama-3.2-1B-Instruct)`
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- `meta-llama/Llama-3.2-3B-Instruct (Meta-Llama-3.2-3B-Instruct)`
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- `meta-llama/Llama-3.2-11B-Vision-Instruct (Llama-3.2-11B-Vision-Instruct)`
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- `meta-llama/Llama-3.2-90B-Vision-Instruct (Llama-3.2-90B-Vision-Instruct)`
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### Prerequisite: API Keys
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Make sure you have access to a SambaNova API Key. You can get one by visiting [SambaNova.ai](https://cloud.sambanova.ai/).
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## Running Llama Stack with SambaNova
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You can do this via Conda (build code) or Docker which has a pre-built image.
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### Via Docker
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This method allows you to get started quickly without having to build the distribution code.
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```bash
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LLAMA_STACK_PORT=5001
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docker run \
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-it \
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-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
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llamastack/distribution-sambanova \
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--port $LLAMA_STACK_PORT \
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--env SAMBANOVA_API_KEY=$SAMBANOVA_API_KEY
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```
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### Via Conda
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```bash
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llama stack build --template sambanova --image-type conda
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llama stack run ./run.yaml \
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--port $LLAMA_STACK_PORT \
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--env SAMBANOVA_API_KEY=$SAMBANOVA_API_KEY
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```
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