forked from phoenix-oss/llama-stack-mirror
04de2f84e9
Each model known to the system has two identifiers: - the `provider_resource_id` (what the provider calls it) -- e.g., `accounts/fireworks/models/llama-v3p1-8b-instruct` - the `identifier` (`model_id`) under which it is registered and gets routed to the appropriate provider. We have so far used the HuggingFace repo alias as the standardized identifier you can use to refer to the model. So in the above example, we'd use `meta-llama/Llama-3.1-8B-Instruct` as the name under which it gets registered. This makes it convenient for users to refer to these models across providers. However, we forgot to register the _actual_ provider model ID also. You should be able to route via `provider_resource_id` also, of course. This change fixes this (somewhat grave) omission. *Note*: this change is additive -- more aliases work now compared to before. ## Test Plan Run the following for distro=(ollama fireworks together) ``` LLAMA_STACK_CONFIG=$distro \ pytest -s -v tests/client-sdk/inference/test_text_inference.py \ --inference-model=meta-llama/Llama-3.1-8B-Instruct --vision-inference-model="" ```
78 lines
2.7 KiB
Markdown
78 lines
2.7 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 | `remote::huggingface`, `inline::localfs` |
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| eval | `inline::meta-reference` |
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| inference | `remote::nvidia` |
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| safety | `inline::llama-guard` |
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| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
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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`, `remote::model-context-protocol` |
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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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- `LLAMASTACK_PORT`: Port for the Llama Stack distribution server (default: `5001`)
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- `NVIDIA_API_KEY`: NVIDIA API Key (default: ``)
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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=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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-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 5001 \
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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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