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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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MODEL_PATH = "." |
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class EndpointHandler: |
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def __init__(self, path=""): |
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print("Loading merged model...") |
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self.tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True) |
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self.model = AutoModelForCausalLM.from_pretrained( |
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MODEL_PATH, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True |
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) |
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self.model.eval() |
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print("Model loaded successfully.") |
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def __call__(self, data): |
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prompt = data.get("inputs", "") |
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inputs = self.tokenizer(prompt, 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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**inputs, |
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max_new_tokens=512, |
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temperature=0.7, |
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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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eos_token_id=self.tokenizer.eos_token_id, |
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) |
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return {"generated_text": self.tokenizer.decode(outputs[0], skip_special_tokens=True)} |
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