| from typing import Dict, Any | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| class EndpointHandler: | |
| def __init__(self, path: str = ""): | |
| """Initialize model and tokenizer.""" | |
| self.tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True) | |
| if self.tokenizer.pad_token is None: | |
| self.tokenizer.pad_token = self.tokenizer.eos_token | |
| self.model = AutoModelForCausalLM.from_pretrained( | |
| path, | |
| torch_dtype=torch.bfloat16, | |
| device_map="auto", | |
| trust_remote_code=True | |
| ) | |
| self.model.eval() | |
| self.device = next(self.model.parameters()).device | |
| print(f"Model loaded on {self.device}") | |
| def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]: | |
| """Handle inference request.""" | |
| inputs = data.get("inputs", data.get("input", "")) | |
| params = data.get("parameters", {}) | |
| encoded = self.tokenizer( | |
| inputs, | |
| return_tensors="pt", | |
| truncation=True, | |
| max_length=2048 | |
| ).to(self.device) | |
| with torch.no_grad(): | |
| outputs = self.model.generate( | |
| **encoded, | |
| max_new_tokens=params.get("max_new_tokens", 256), | |
| temperature=params.get("temperature", 0.7), | |
| top_p=params.get("top_p", 0.9), | |
| do_sample=params.get("do_sample", True), | |
| repetition_penalty=params.get("repetition_penalty", 1.1), | |
| pad_token_id=self.tokenizer.pad_token_id, | |
| eos_token_id=self.tokenizer.eos_token_id, | |
| ) | |
| generated = outputs[0][encoded["input_ids"].shape[1]:] | |
| text = self.tokenizer.decode(generated, skip_special_tokens=True) | |
| return {"generated_text": text} | |