| from typing import Dict, List, Any |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| import torch |
| import os |
|
|
|
|
| MAX_INPUT_SIZE = 10_000 |
| MAX_NEW_TOKENS = 4_000 |
|
|
| def clean_json_text(text): |
| """ |
| Cleans JSON text by removing leading/trailing whitespace and escaping special characters. |
| """ |
| text = text.strip() |
| text = text.replace("\#", "#").replace("\&", "&") |
| return text |
|
|
| class EndpointHandler: |
| def __init__(self, path=""): |
| |
| self.model = AutoModelForCausalLM.from_pretrained(path, |
| trust_remote_code=True, |
| torch_dtype=torch.bfloat16, |
| device_map="auto") |
| self.model.eval() |
| self.tokenizer = AutoTokenizer.from_pretrained(path) |
|
|
| def __call__(self, data: Dict[str, Any]) -> str: |
| data = data.pop("inputs") |
| template = data.pop("template") |
| text = data.pop("text") |
| input_llm = f"<|input|>\n### Template:\n{template}\n### Text:\n{text}\n\n<|output|>" + "{" |
|
|
| input_ids = self.tokenizer(input_llm, return_tensors="pt", truncation=True, max_length=MAX_INPUT_SIZE).to("cuda") |
| output = self.tokenizer.decode(self.model.generate(**input_ids, max_new_tokens=MAX_NEW_TOKENS)[0], skip_special_tokens=True) |
|
|
| return clean_json_text(output.split("<|output|>")[1]) |
