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