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# What does this PR do? [Provide a short summary of what this PR does and why. Link to relevant issues if applicable.] [//]: # (If resolving an issue, uncomment and update the line below) [//]: # (Closes #[issue-number]) ## Test Plan [Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.*] [//]: # (## Documentation) --------- Co-authored-by: Francisco Arceo <farceo@redhat.com>
88 lines
3.2 KiB
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88 lines
3.2 KiB
Markdown
<!-- This file was auto-generated by distro_codegen.py, please edit source -->
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# NVIDIA Distribution
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The `llamastack/distribution-nvidia` 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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| datasetio | `inline::localfs` |
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| eval | `inline::meta-reference` |
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| inference | `remote::nvidia` |
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| post_training | `remote::nvidia` |
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| safety | `remote::nvidia` |
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| scoring | `inline::basic` |
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| telemetry | `inline::meta-reference` |
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| tool_runtime | `inline::rag-runtime` |
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| vector_io | `inline::faiss` |
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### Environment Variables
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The following environment variables can be configured:
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- `NVIDIA_API_KEY`: NVIDIA API Key (default: ``)
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- `NVIDIA_USER_ID`: NVIDIA User ID (default: `llama-stack-user`)
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- `NVIDIA_DATASET_NAMESPACE`: NVIDIA Dataset Namespace (default: `default`)
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- `NVIDIA_ACCESS_POLICIES`: NVIDIA Access Policies (default: `{}`)
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- `NVIDIA_PROJECT_ID`: NVIDIA Project ID (default: `test-project`)
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- `NVIDIA_CUSTOMIZER_URL`: NVIDIA Customizer URL (default: `https://customizer.api.nvidia.com`)
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- `NVIDIA_OUTPUT_MODEL_DIR`: NVIDIA Output Model Directory (default: `test-example-model@v1`)
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- `GUARDRAILS_SERVICE_URL`: URL for the NeMo Guardrails Service (default: `http://0.0.0.0:7331`)
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- `INFERENCE_MODEL`: Inference model (default: `Llama3.1-8B-Instruct`)
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- `SAFETY_MODEL`: Name of the model to use for safety (default: `meta/llama-3.1-8b-instruct`)
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### Models
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The following models are available by default:
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- `meta/llama3-8b-instruct (aliases: meta-llama/Llama-3-8B-Instruct)`
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- `meta/llama3-70b-instruct (aliases: meta-llama/Llama-3-70B-Instruct)`
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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-3.2-11b-vision-instruct (aliases: meta-llama/Llama-3.2-11B-Vision-Instruct)`
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- `meta/llama-3.2-90b-vision-instruct (aliases: meta-llama/Llama-3.2-90B-Vision-Instruct)`
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- `nvidia/llama-3.2-nv-embedqa-1b-v2 `
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- `nvidia/nv-embedqa-e5-v5 `
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- `nvidia/nv-embedqa-mistral-7b-v2 `
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- `snowflake/arctic-embed-l `
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### Prerequisite: API Keys
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Make sure you have access to a NVIDIA API Key. You can get one by visiting [https://build.nvidia.com/](https://build.nvidia.com/).
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## Running Llama Stack with NVIDIA
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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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-v ./run.yaml:/root/my-run.yaml \
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llamastack/distribution-nvidia \
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--yaml-config /root/my-run.yaml \
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--port $LLAMA_STACK_PORT \
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--env NVIDIA_API_KEY=$NVIDIA_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 nvidia --image-type conda
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llama stack run ./run.yaml \
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--port 8321 \
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--env NVIDIA_API_KEY=$NVIDIA_API_KEY
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--env INFERENCE_MODEL=$INFERENCE_MODEL
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```
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