mirror of
https://github.com/meta-llama/llama-stack.git
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0e4307de0f
adding the --gpu all flag to Docker run commands for meta-reference-gpu distributions ensures models are loaded into GPU instead of CPU. Remove docs for meta-reference-quantized-gpu The distribution was removed in #1887 but these files were left behind. Fixes: #1798 # What does this PR do? Fixes doc to add --gpu all command to docker run [//]: # (If resolving an issue, uncomment and update the line below) Closes #1798 ## 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.*] verified in docker documentation but untested --------- Signed-off-by: Derek Higgins <derekh@redhat.com>
113 lines
5.8 KiB
Markdown
113 lines
5.8 KiB
Markdown
---
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orphan: true
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---
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# Meta Reference 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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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.
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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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## Prerequisite: Downloading Models
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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.
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```
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$ llama model list --downloaded
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓
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┃ Model ┃ Size ┃ Modified Time ┃
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┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩
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│ Llama3.2-1B-Instruct:int4-qlora-eo8 │ 1.53 GB │ 2025-02-26 11:22:28 │
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├─────────────────────────────────────────┼──────────┼─────────────────────┤
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│ Llama3.2-1B │ 2.31 GB │ 2025-02-18 21:48:52 │
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├─────────────────────────────────────────┼──────────┼─────────────────────┤
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│ Prompt-Guard-86M │ 0.02 GB │ 2025-02-26 11:29:28 │
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├─────────────────────────────────────────┼──────────┼─────────────────────┤
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│ Llama3.2-3B-Instruct:int4-spinquant-eo8 │ 3.69 GB │ 2025-02-26 11:37:41 │
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├─────────────────────────────────────────┼──────────┼─────────────────────┤
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│ Llama3.2-3B │ 5.99 GB │ 2025-02-18 21:51:26 │
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├─────────────────────────────────────────┼──────────┼─────────────────────┤
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│ Llama3.1-8B │ 14.97 GB │ 2025-02-16 10:36:37 │
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├─────────────────────────────────────────┼──────────┼─────────────────────┤
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│ Llama3.2-1B-Instruct:int4-spinquant-eo8 │ 1.51 GB │ 2025-02-26 11:35:02 │
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├─────────────────────────────────────────┼──────────┼─────────────────────┤
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│ Llama-Guard-3-1B │ 2.80 GB │ 2025-02-26 11:20:46 │
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├─────────────────────────────────────────┼──────────┼─────────────────────┤
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│ Llama-Guard-3-1B:int4 │ 0.43 GB │ 2025-02-26 11:33:33 │
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└─────────────────────────────────────────┴──────────┴─────────────────────┘
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```
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## Running the Distribution
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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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--gpu all \
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-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
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-v ~/.llama:/root/.llama \
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llamastack/distribution-{{ name }} \
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--port $LLAMA_STACK_PORT \
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--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
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```
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If you are using Llama Stack Safety / Shield APIs, use:
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```bash
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docker run \
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-it \
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--pull always \
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--gpu all \
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-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
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-v ~/.llama:/root/.llama \
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llamastack/distribution-{{ name }} \
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--port $LLAMA_STACK_PORT \
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--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \
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--env SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
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```
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### Via Conda
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Make sure you have done `uv pip install llama-stack` and have the Llama Stack CLI available.
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```bash
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llama stack build --template {{ name }} --image-type conda
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llama stack run distributions/{{ name }}/run.yaml \
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--port 8321 \
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--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
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```
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If you are using Llama Stack Safety / Shield APIs, use:
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```bash
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llama stack run distributions/{{ name }}/run-with-safety.yaml \
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--port 8321 \
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--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \
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--env SAFETY_MODEL=meta-llama/Llama-Guard-3-1B
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```
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