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2a31163178
# What does this PR do? Automatically generates - build.yaml - run.yaml - run-with-safety.yaml - parts of markdown docs for the distributions. ## Test Plan At this point, this only updates the YAMLs and the docs. Some testing (especially with ollama and vllm) has been performed but needs to be much more tested.
49 lines
1.6 KiB
Python
49 lines
1.6 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 typing import Optional
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from llama_models.schema_utils import json_schema_type
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from pydantic import BaseModel, Field
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@json_schema_type
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class TGIImplConfig(BaseModel):
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url: str = Field(
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description="The URL for the TGI serving endpoint",
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)
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api_token: Optional[str] = Field(
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default=None,
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description="A bearer token if your TGI endpoint is protected.",
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)
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@classmethod
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def sample_run_config(cls, url: str = "${env.TGI_URL}", **kwargs):
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return {
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"url": url,
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}
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@json_schema_type
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class InferenceEndpointImplConfig(BaseModel):
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endpoint_name: str = Field(
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description="The name of the Hugging Face Inference Endpoint in the format of '{namespace}/{endpoint_name}' (e.g. 'my-cool-org/meta-llama-3-1-8b-instruct-rce'). Namespace is optional and will default to the user account if not provided.",
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)
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api_token: Optional[str] = Field(
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default=None,
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description="Your Hugging Face user access token (will default to locally saved token if not provided)",
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)
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@json_schema_type
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class InferenceAPIImplConfig(BaseModel):
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huggingface_repo: str = Field(
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description="The model ID of the model on the Hugging Face Hub (e.g. 'meta-llama/Meta-Llama-3.1-70B-Instruct')",
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)
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api_token: Optional[str] = Field(
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default=None,
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description="Your Hugging Face user access token (will default to locally saved token if not provided)",
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)
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