|
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]) |
