47 lines
1.6 KiB
Python
47 lines
1.6 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, trust_remote_code=True)
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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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trust_remote_code=True
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)
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self.model.eval()
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def __call__(self, data):
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inputs = data.pop("inputs", "")
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parameters = data.pop("parameters", {})
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# Build chat format
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messages = [
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{"role": "system", "content": "You are a cybersecurity expert assistant."},
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{"role": "user", "content": inputs}
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]
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text = self.tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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model_inputs = self.tokenizer(text, return_tensors="pt").to(self.model.device)
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with torch.no_grad():
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outputs = self.model.generate(
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**model_inputs,
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max_new_tokens=parameters.get("max_new_tokens", 512),
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temperature=parameters.get("temperature", 0.7),
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top_p=parameters.get("top_p", 0.9),
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do_sample=True,
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pad_token_id=self.tokenizer.eos_token_id
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)
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response = self.tokenizer.decode(
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outputs[0][model_inputs['input_ids'].shape[1]:],
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skip_special_tokens=True
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)
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return {"generated_text": response}
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