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import json |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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from typing import Dict, List, Any |
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class EndpointHandler: |
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def __init__(self, path: str = ""): |
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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.bfloat16, |
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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[str, Any]) -> Dict[str, Any]: |
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inputs = data.get("inputs", "") |
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parameters = data.get("parameters", {}) |
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input_ids = self.tokenizer(inputs, return_tensors="pt").input_ids.to(self.model.device) |
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max_length = parameters.get("max_length", 100) |
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temperature = parameters.get("temperature", 1.0) |
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top_p = parameters.get("top_p", 1.0) |
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do_sample = parameters.get("do_sample", True) |
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with torch.no_grad(): |
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outputs = self.model.generate( |
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input_ids, |
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max_length=max_length, |
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temperature=temperature, |
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top_p=top_p, |
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do_sample=do_sample, |
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pad_token_id=self.tokenizer.pad_token_id, |
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eos_token_id=self.tokenizer.eos_token_id |
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) |
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generated_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True) |
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return {"generated_text": generated_text} |
