forked from phoenix-oss/llama-stack-mirror
314ee09ae3
llama-models should have extremely minimal cruft. Its sole purpose should be didactic -- show the simplest implementation of the llama models and document the prompt formats, etc. This PR is the complement to https://github.com/meta-llama/llama-models/pull/279 ## Test Plan Ensure all `llama` CLI `model` sub-commands work: ```bash llama model list llama model download --model-id ... llama model prompt-format -m ... ``` Ran tests: ```bash cd tests/client-sdk LLAMA_STACK_CONFIG=fireworks pytest -s -v inference/ LLAMA_STACK_CONFIG=fireworks pytest -s -v vector_io/ LLAMA_STACK_CONFIG=fireworks pytest -s -v agents/ ``` Create a fresh venv `uv venv && source .venv/bin/activate` and run `llama stack build --template fireworks --image-type venv` followed by `llama stack run together --image-type venv` <-- the server runs Also checked that the OpenAPI generator can run and there is no change in the generated files as a result. ```bash cd docs/openapi_generator sh run_openapi_generator.sh ```
57 lines
2 KiB
Python
57 lines
2 KiB
Python
# 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 pydantic import BaseModel, Field, field_validator
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from llama_stack.providers.utils.inference import supported_inference_models
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from llama_stack.schema_utils import json_schema_type
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@json_schema_type
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class VLLMConfig(BaseModel):
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"""Configuration for the vLLM inference provider."""
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model: str = Field(
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default="Llama3.2-3B-Instruct",
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description="Model descriptor from `llama model list`",
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)
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tensor_parallel_size: int = Field(
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default=1,
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description="Number of tensor parallel replicas (number of GPUs to use).",
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)
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max_tokens: int = Field(
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default=4096,
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description="Maximum number of tokens to generate.",
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)
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enforce_eager: bool = Field(
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default=False,
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description="Whether to use eager mode for inference (otherwise cuda graphs are used).",
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)
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gpu_memory_utilization: float = Field(
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default=0.3,
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)
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@classmethod
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def sample_run_config(cls):
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return {
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"model": "${env.INFERENCE_MODEL:Llama3.2-3B-Instruct}",
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"tensor_parallel_size": "${env.TENSOR_PARALLEL_SIZE:1}",
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"max_tokens": "${env.MAX_TOKENS:4096}",
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"enforce_eager": "${env.ENFORCE_EAGER:False}",
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"gpu_memory_utilization": "${env.GPU_MEMORY_UTILIZATION:0.7}",
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}
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@field_validator("model")
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@classmethod
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def validate_model(cls, model: str) -> str:
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permitted_models = supported_inference_models()
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descriptors = [m.descriptor() for m in permitted_models]
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repos = [m.huggingface_repo for m in permitted_models]
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if model not in (descriptors + repos):
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model_list = "\n\t".join(repos)
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raise ValueError(f"Unknown model: `{model}`. Choose from [\n\t{model_list}\n]")
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return model
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