| 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} |
|
|
