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77 lines
1.7 KiB
Markdown
77 lines
1.7 KiB
Markdown
# Ssambanova 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-{{ name }}` distribution consists of the following provider configurations.
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{{ providers_table }}
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{% if run_config_env_vars %}
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### Environment Variables
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The following environment variables can be configured:
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{% for var, (default_value, description) in run_config_env_vars.items() %}
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- `{{ var }}`: {{ description }} (default: `{{ default_value }}`)
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{% endfor %}
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{% endif %}
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{% if default_models %}
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### Models
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The following models are available by default:
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{% for model in default_models %}
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- `{{ model.model_id }} ({{ model.provider_model_id }})`
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{% endfor %}
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{% endif %}
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### Prerequisite: API Keys
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Make sure you have access to a Ssambanova API Key. You can get one by visiting [Ssambanova](https://cloud.sambanova.ai/apis).
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## Running Llama Stack with Ssambanova
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You can do this via Conda (build code) or Docker which has a pre-built image.
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### Available INFERENCE_MODEL
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- Meta-Llama-3.1-8B-Instruct
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- Meta-Llama-3.1-70B-Instruct
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- Meta-Llama-3.1-405B-Instruct
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- Meta-Llama-3.2-1B-Instruct
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- Meta-Llama-3.2-3B-Instruct
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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-{{ name }} \
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--port $LLAMA_STACK_PORT \
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--env SSAMBANOVA_API_KEY=$SSAMBANOVA_API_KEY \
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--env INFERENCE_MODEL=$INFERENCE_MODEL
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```
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### Via Conda
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```bash
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llama stack build --template ssambanova --image-type conda
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llama stack run ./run.yaml \
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--port $LLAMA_STACK_PORT \
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--env SSAMBANOVA_API_KEY=$SSAMBANOVA_API_KEY \
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--env INFERENCE_MODEL=$INFERENCE_MODEL
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```
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