71 lines
2.3 KiB
Python
71 lines
2.3 KiB
Python
from typing import Iterator, Optional
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from huggingface_hub import InferenceClient
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from .llm_engine import LlmEngine
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class HfLlmEngine(LlmEngine):
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"""
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LLM Engine implementation using HuggingFace's InferenceClient.
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"""
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def __init__(
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self,
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model_id: str = "mistralai/Mistral-7B-Instruct-v0.2",
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api_token: Optional[str] = None,
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temperature: float = 0.7,
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max_new_tokens: int = 1024,
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):
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"""
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Initialize the HuggingFace Inference API LLM Engine.
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Args:
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model_id: HuggingFace model ID to use (default: Mistral-7B-Instruct)
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api_token: HuggingFace API token. If None, will try to read from HF_TOKEN env var
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temperature: Sampling temperature (default: 0.7)
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max_new_tokens: Maximum number of tokens to generate (default: 1024)
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"""
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self.model_id = model_id
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self.client = InferenceClient(token=api_token)
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# Generation parameters
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self.temperature = temperature
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self.max_new_tokens = max_new_tokens
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def set_model_path(self, model_id: str):
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"""
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Update the model being used.
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Args:
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model_id: New HuggingFace model ID to use
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"""
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self.model_id = model_id
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def infer(self, system_prompt: str, main_context: str) -> Iterator[str]:
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"""
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Run inference using the system prompt and main context.
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Args:
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system_prompt: The system prompt string
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main_context: The main context string after templating
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Returns:
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Iterator[str]: An iterator that yields the generated text.
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"""
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": main_context}
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]
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def stream_wrapper():
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stream = self.client.chat_completion(
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model=self.model_id,
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messages=messages,
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temperature=self.temperature,
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max_tokens=self.max_new_tokens,
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stream=True
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)
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for response in stream:
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if content := response.choices[0].delta.content:
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yield content
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return stream_wrapper() |