forked from phoenix-oss/llama-stack-mirror
b2b00a216b
# What does this PR do? switch sambanova inference adaptor to LiteLLM usage to simplify integration and solve issues with current adaptor when streaming and tool calling, models and templates updated ## Test Plan pytest -s -v tests/integration/inference/test_text_inference.py --stack-config=sambanova --text-model=sambanova/Meta-Llama-3.3-70B-Instruct pytest -s -v tests/integration/inference/test_vision_inference.py --stack-config=sambanova --vision-model=sambanova/Llama-3.2-11B-Vision-Instruct
80 lines
2.6 KiB
Markdown
80 lines
2.6 KiB
Markdown
---
|
|
orphan: true
|
|
---
|
|
<!-- This file was auto-generated by distro_codegen.py, please edit source -->
|
|
# SambaNova Distribution
|
|
|
|
```{toctree}
|
|
: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`, `inline::sentence-transformers` |
|
|
| safety | `inline::llama-guard` |
|
|
| telemetry | `inline::meta-reference` |
|
|
| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` |
|
|
| 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: `8321`)
|
|
- `SAMBANOVA_API_KEY`: SambaNova API Key (default: ``)
|
|
|
|
### Models
|
|
|
|
The following models are available by default:
|
|
|
|
- `sambanova/Meta-Llama-3.1-8B-Instruct (aliases: meta-llama/Llama-3.1-8B-Instruct)`
|
|
- `sambanova/Meta-Llama-3.1-405B-Instruct (aliases: meta-llama/Llama-3.1-405B-Instruct-FP8)`
|
|
- `sambanova/Meta-Llama-3.2-1B-Instruct (aliases: meta-llama/Llama-3.2-1B-Instruct)`
|
|
- `sambanova/Meta-Llama-3.2-3B-Instruct (aliases: meta-llama/Llama-3.2-3B-Instruct)`
|
|
- `sambanova/Meta-Llama-3.3-70B-Instruct (aliases: meta-llama/Llama-3.3-70B-Instruct)`
|
|
- `sambanova/Llama-3.2-11B-Vision-Instruct (aliases: meta-llama/Llama-3.2-11B-Vision-Instruct)`
|
|
- `sambanova/Llama-3.2-90B-Vision-Instruct (aliases: meta-llama/Llama-3.2-90B-Vision-Instruct)`
|
|
- `sambanova/Llama-4-Scout-17B-16E-Instruct (aliases: meta-llama/Llama-4-Scout-17B-16E-Instruct)`
|
|
- `sambanova/Llama-4-Maverick-17B-128E-Instruct (aliases: meta-llama/Llama-4-Maverick-17B-128E-Instruct)`
|
|
- `sambanova/Meta-Llama-Guard-3-8B (aliases: meta-llama/Llama-Guard-3-8B)`
|
|
|
|
|
|
### Prerequisite: API Keys
|
|
|
|
Make sure you have access to a SambaNova API Key. You can get one by visiting [SambaNova.ai](https://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.
|
|
|
|
```bash
|
|
LLAMA_STACK_PORT=8321
|
|
docker run \
|
|
-it \
|
|
--pull always \
|
|
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
|
|
llamastack/distribution-sambanova \
|
|
--port $LLAMA_STACK_PORT \
|
|
--env SAMBANOVA_API_KEY=$SAMBANOVA_API_KEY
|
|
```
|
|
|
|
### Via Conda
|
|
|
|
```bash
|
|
llama stack build --template sambanova --image-type conda
|
|
llama stack run ./run.yaml \
|
|
--port $LLAMA_STACK_PORT \
|
|
--env SAMBANOVA_API_KEY=$SAMBANOVA_API_KEY
|
|
```
|