forked from phoenix-oss/llama-stack-mirror
7b4eb0967e
# What does this PR do? ## Test Plan export MODEL=accounts/fireworks/models/llama4-scout-instruct-basic; LLAMA_STACK_CONFIG=verification pytest -s -v tests/integration/inference --vision-model $MODEL --text-model $MODEL
80 lines
2.5 KiB
Markdown
80 lines
2.5 KiB
Markdown
---
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orphan: true
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---
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<!-- This file was auto-generated by distro_codegen.py, please edit source -->
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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: `8321`)
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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-3.1-8B-Instruct (aliases: meta-llama/Llama-3.1-8B-Instruct)`
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- `Meta-Llama-3.1-70B-Instruct (aliases: meta-llama/Llama-3.1-70B-Instruct)`
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- `Meta-Llama-3.1-405B-Instruct (aliases: meta-llama/Llama-3.1-405B-Instruct-FP8)`
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- `Meta-Llama-3.2-1B-Instruct (aliases: meta-llama/Llama-3.2-1B-Instruct)`
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- `Meta-Llama-3.2-3B-Instruct (aliases: meta-llama/Llama-3.2-3B-Instruct)`
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- `Meta-Llama-3.3-70B-Instruct (aliases: meta-llama/Llama-3.3-70B-Instruct)`
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- `Llama-3.2-11B-Vision-Instruct (aliases: meta-llama/Llama-3.2-11B-Vision-Instruct)`
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- `Llama-3.2-90B-Vision-Instruct (aliases: meta-llama/Llama-3.2-90B-Vision-Instruct)`
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- `Meta-Llama-Guard-3-8B (aliases: meta-llama/Llama-Guard-3-8B)`
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- `Llama-4-Scout-17B-16E-Instruct (aliases: meta-llama/Llama-4-Scout-17B-16E-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://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=8321
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docker run \
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-it \
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--pull always \
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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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