60 lines
1.9 KiB
Python
60 lines
1.9 KiB
Python
from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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class EndpointHandler:
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def __init__(self, path=""):
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self.tokenizer = AutoTokenizer.from_pretrained(path)
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self.model = AutoModelForCausalLM.from_pretrained(
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path,
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torch_dtype=torch.float16,
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device_map="auto"
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)
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self.model.eval()
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def __call__(self, data: dict) -> dict:
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inputs = data.get("inputs", "")
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parameters = data.get("parameters", {})
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max_new_tokens = parameters.get("max_new_tokens", 1024)
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temperature = parameters.get("temperature", 0.3)
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# Format with ChatML if not already formatted
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if "<|im_start|>" not in inputs:
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inputs = (
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f"<|im_start|>system\n"
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f"You are an expert network architect "
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f"with CCDE-level expertise.\n<|im_end|>\n"
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f"<|im_start|>user\n{inputs}<|im_end|>\n"
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f"<|im_start|>assistant\n"
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)
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tokenized = self.tokenizer(
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inputs,
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return_tensors="pt"
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).to(self.model.device)
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with torch.no_grad():
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output = self.model.generate( # type: ignore[union-attr]
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**tokenized,
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max_new_tokens=max_new_tokens,
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temperature=temperature,
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do_sample=temperature > 0,
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pad_token_id=self.tokenizer.eos_token_id,
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eos_token_id=self.tokenizer.convert_tokens_to_ids(
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"<|im_end|>"
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)
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)
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# Decode only new tokens
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new_tokens = output[0][tokenized["input_ids"].shape[1]:]
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response = self.tokenizer.decode(
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new_tokens,
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skip_special_tokens=False
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)
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# Clean up stop tokens
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response = response.replace("<|im_end|>", "").strip()
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return {"generated_text": response}
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