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Docs for meta-reference-gpu
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llama_stack/templates/meta-reference-gpu/__init__.py
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llama_stack/templates/meta-reference-gpu/__init__.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from .meta_reference import get_distribution_template # noqa: F401
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version: '2'
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name: meta-reference-gpu
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distribution_spec:
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docker_image: pytorch/pytorch:2.5.0-cuda12.4-cudnn9-runtime
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description: Use code from `llama_stack` itself to serve all llama stack APIs
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description: Use Meta Reference for running LLM inference
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docker_image: null
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providers:
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inference: meta-reference
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inference:
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- inline::meta-reference
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memory:
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- inline::faiss
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- remote::chromadb
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- remote::pgvector
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safety: inline::llama-guard
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agents: inline::meta-reference
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telemetry: inline::meta-reference
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safety:
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- inline::llama-guard
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agents:
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- inline::meta-reference
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telemetry:
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- inline::meta-reference
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image_type: conda
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82
llama_stack/templates/meta-reference-gpu/doc_template.md
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llama_stack/templates/meta-reference-gpu/doc_template.md
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# Meta Reference Distribution
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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 make sure you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/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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$ ls ~/.llama/checkpoints
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Llama3.1-8B Llama3.2-11B-Vision-Instruct Llama3.2-1B-Instruct Llama3.2-90B-Vision-Instruct Llama-Guard-3-8B
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Llama3.1-8B-Instruct Llama3.2-1B Llama3.2-3B-Instruct Llama-Guard-3-1B Prompt-Guard-86M
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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=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-{{ name }} \
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/root/my-run.yaml \
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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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-p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
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-v ./run-with-safety.yaml:/root/my-run.yaml \
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llamastack/distribution-{{ name }} \
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/root/my-run.yaml \
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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 `pip install llama-stack` and have the Llama Stack CLI available.
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```bash
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llama stack build --template meta-reference-gpu --image-type conda
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llama stack run ./run.yaml \
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--port 5001 \
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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 ./run-with-safety.yaml \
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--port 5001 \
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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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100
llama_stack/templates/meta-reference-gpu/meta_reference.py
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llama_stack/templates/meta-reference-gpu/meta_reference.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from pathlib import Path
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from llama_stack.distribution.datatypes import ModelInput, Provider, ShieldInput
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from llama_stack.providers.inline.inference.meta_reference import (
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MetaReferenceInferenceConfig,
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)
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from llama_stack.templates.template import DistributionTemplate, RunConfigSettings
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def get_distribution_template() -> DistributionTemplate:
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providers = {
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"inference": ["inline::meta-reference"],
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"memory": ["inline::faiss", "remote::chromadb", "remote::pgvector"],
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"safety": ["inline::llama-guard"],
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"agents": ["inline::meta-reference"],
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"telemetry": ["inline::meta-reference"],
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}
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inference_provider = Provider(
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provider_id="meta-reference-inference",
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provider_type="inline::meta-reference",
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config=MetaReferenceInferenceConfig.sample_run_config(
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model="${env.INFERENCE_MODEL}",
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checkpoint_dir="${env.INFERENCE_CHECKPOINT_DIR:null}",
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),
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)
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inference_model = ModelInput(
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model_id="${env.INFERENCE_MODEL}",
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provider_id="meta-reference-inference",
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)
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safety_model = ModelInput(
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model_id="${env.SAFETY_MODEL}",
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provider_id="meta-reference-safety",
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)
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return DistributionTemplate(
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name="meta-reference-gpu",
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distro_type="self_hosted",
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description="Use Meta Reference for running LLM inference",
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template_path=Path(__file__).parent / "doc_template.md",
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providers=providers,
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default_models=[inference_model, safety_model],
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run_configs={
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"run.yaml": RunConfigSettings(
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provider_overrides={
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"inference": [inference_provider],
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},
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default_models=[inference_model],
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),
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"run-with-safety.yaml": RunConfigSettings(
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provider_overrides={
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"inference": [
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inference_provider,
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Provider(
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provider_id="meta-reference-safety",
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provider_type="inline::meta-reference",
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config=MetaReferenceInferenceConfig.sample_run_config(
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model="${env.SAFETY_MODEL}",
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checkpoint_dir="${env.SAFETY_CHECKPOINT_DIR:null}",
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),
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),
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],
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},
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default_models=[
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inference_model,
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safety_model,
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],
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default_shields=[ShieldInput(shield_id="${env.SAFETY_MODEL}")],
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),
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},
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docker_compose_env_vars={
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"LLAMASTACK_PORT": (
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"5001",
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"Port for the Llama Stack distribution server",
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),
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"INFERENCE_MODEL": (
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"meta-llama/Llama-3.2-3B-Instruct",
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"Inference model loaded into the Meta Reference server",
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),
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"INFERENCE_CHECKPOINT_DIR": (
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"null",
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"Directory containing the Meta Reference model checkpoint",
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),
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"SAFETY_MODEL": (
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"meta-llama/Llama-Guard-3-1B",
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"Name of the safety (Llama-Guard) model to use",
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),
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"SAFETY_CHECKPOINT_DIR": (
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"null",
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"Directory containing the Llama-Guard model checkpoint",
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),
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},
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)
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