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Make TGI adapter compatible with HF Inference API (#97)
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851c30597a
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7 changed files with 122 additions and 96 deletions
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@ -12,18 +12,32 @@ from pydantic import BaseModel, Field
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@json_schema_type
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class TGIImplConfig(BaseModel):
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url: Optional[str] = Field(
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default=None,
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description="The URL for the local TGI endpoint (e.g., http://localhost:8080)",
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url: str = Field(
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description="The URL for the TGI endpoint (e.g. 'http://localhost:8080')",
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)
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api_token: Optional[str] = Field(
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default=None,
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description="The HF token for Hugging Face Inference Endpoints (will default to locally saved token if not provided)",
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)
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hf_endpoint_name: Optional[str] = Field(
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default=None,
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description="The name of the Hugging Face Inference Endpoint : can be either in the format of '{namespace}/{endpoint_name}' (namespace can be the username or organization name) or just '{endpoint_name}' if logged into the same account as the namespace",
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description="A bearer token if your TGI endpoint is protected.",
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)
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def is_inference_endpoint(self) -> bool:
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return self.hf_endpoint_name is not None
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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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model_id: 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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