forked from phoenix-oss/llama-stack-mirror
121 lines
6.7 KiB
Markdown
121 lines
6.7 KiB
Markdown
---
|
|
orphan: true
|
|
---
|
|
<!-- This file was auto-generated by distro_codegen.py, please edit source -->
|
|
# Meta Reference Distribution
|
|
|
|
```{toctree}
|
|
:maxdepth: 2
|
|
:hidden:
|
|
|
|
self
|
|
```
|
|
|
|
The `llamastack/distribution-meta-reference-gpu` distribution consists of the following provider configurations:
|
|
|
|
| API | Provider(s) |
|
|
|-----|-------------|
|
|
| agents | `inline::meta-reference` |
|
|
| datasetio | `remote::huggingface`, `inline::localfs` |
|
|
| inference | `inline::meta-reference` |
|
|
| safety | `inline::llama-guard` |
|
|
| telemetry | `inline::meta-reference` |
|
|
| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::code-interpreter`, `inline::rag-runtime`, `remote::model-context-protocol` |
|
|
| vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
|
|
|
|
|
|
Note that you need access to nvidia GPUs to run this distribution. This distribution is not compatible with CPU-only machines or machines with AMD GPUs.
|
|
|
|
### Environment Variables
|
|
|
|
The following environment variables can be configured:
|
|
|
|
- `LLAMA_STACK_PORT`: Port for the Llama Stack distribution server (default: `8321`)
|
|
- `INFERENCE_MODEL`: Inference model loaded into the Meta Reference server (default: `meta-llama/Llama-3.2-3B-Instruct`)
|
|
- `INFERENCE_CHECKPOINT_DIR`: Directory containing the Meta Reference model checkpoint (default: `null`)
|
|
- `SAFETY_MODEL`: Name of the safety (Llama-Guard) model to use (default: `meta-llama/Llama-Guard-3-1B`)
|
|
- `SAFETY_CHECKPOINT_DIR`: Directory containing the Llama-Guard model checkpoint (default: `null`)
|
|
|
|
|
|
## Prerequisite: Downloading Models
|
|
|
|
Please use `llama model list --downloaded` to check that you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints.
|
|
|
|
```
|
|
$ llama model list --downloaded
|
|
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓
|
|
┃ Model ┃ Size ┃ Modified Time ┃
|
|
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩
|
|
│ Llama3.2-1B-Instruct:int4-qlora-eo8 │ 1.53 GB │ 2025-02-26 11:22:28 │
|
|
├─────────────────────────────────────────┼──────────┼─────────────────────┤
|
|
│ Llama3.2-1B │ 2.31 GB │ 2025-02-18 21:48:52 │
|
|
├─────────────────────────────────────────┼──────────┼─────────────────────┤
|
|
│ Prompt-Guard-86M │ 0.02 GB │ 2025-02-26 11:29:28 │
|
|
├─────────────────────────────────────────┼──────────┼─────────────────────┤
|
|
│ Llama3.2-3B-Instruct:int4-spinquant-eo8 │ 3.69 GB │ 2025-02-26 11:37:41 │
|
|
├─────────────────────────────────────────┼──────────┼─────────────────────┤
|
|
│ Llama3.2-3B │ 5.99 GB │ 2025-02-18 21:51:26 │
|
|
├─────────────────────────────────────────┼──────────┼─────────────────────┤
|
|
│ Llama3.1-8B │ 14.97 GB │ 2025-02-16 10:36:37 │
|
|
├─────────────────────────────────────────┼──────────┼─────────────────────┤
|
|
│ Llama3.2-1B-Instruct:int4-spinquant-eo8 │ 1.51 GB │ 2025-02-26 11:35:02 │
|
|
├─────────────────────────────────────────┼──────────┼─────────────────────┤
|
|
│ Llama-Guard-3-1B │ 2.80 GB │ 2025-02-26 11:20:46 │
|
|
├─────────────────────────────────────────┼──────────┼─────────────────────┤
|
|
│ Llama-Guard-3-1B:int4 │ 0.43 GB │ 2025-02-26 11:33:33 │
|
|
└─────────────────────────────────────────┴──────────┴─────────────────────┘
|
|
```
|
|
|
|
## Running the Distribution
|
|
|
|
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 \
|
|
-v ~/.llama:/root/.llama \
|
|
llamastack/distribution-meta-reference-gpu \
|
|
--port $LLAMA_STACK_PORT \
|
|
--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
|
|
```
|
|
|
|
If you are using Llama Stack Safety / Shield APIs, use:
|
|
|
|
```bash
|
|
docker run \
|
|
-it \
|
|
--pull always \
|
|
-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
|
|
-v ~/.llama:/root/.llama \
|
|
llamastack/distribution-meta-reference-gpu \
|
|
--port $LLAMA_STACK_PORT \
|
|
--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \
|
|
--env SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
|
|
```
|
|
|
|
### Via Conda
|
|
|
|
Make sure you have done `uv pip install llama-stack` and have the Llama Stack CLI available.
|
|
|
|
```bash
|
|
llama stack build --template meta-reference-gpu --image-type conda
|
|
llama stack run distributions/meta-reference-gpu/run.yaml \
|
|
--port 8321 \
|
|
--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
|
|
```
|
|
|
|
If you are using Llama Stack Safety / Shield APIs, use:
|
|
|
|
```bash
|
|
llama stack run distributions/meta-reference-gpu/run-with-safety.yaml \
|
|
--port 8321 \
|
|
--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \
|
|
--env SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
|
|
```
|