| from typing import Dict, List, Any | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, GemmaTokenizer | |
| import torch | |
| from peft import PeftModel | |
| import json | |
| import os | |
| class EndpointHandler(): | |
| def __init__(self, path=""): | |
| bnb_config = BitsAndBytesConfig( | |
| load_in_8bit=True, | |
| bnb_8bit_quant_type="nf4", | |
| bnb_8bit_compute_dtype=torch.bfloat16 | |
| ) | |
| tokenizer = AutoTokenizer.from_pretrained('LexiconShiftInnovations/Gemma_Dental_it_07_merged') | |
| model = AutoModelForCausalLM.from_pretrained('LexiconShiftInnovations/Gemma_Dental_it_07_merged', quantization_config=bnb_config, device_map={"":0}) | |
| self.pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer) | |
| def __call__(self, data: Any) -> List[List[Dict[str, float]]]: | |
| inputs = data.pop("inputs", data) | |
| parameters = data.pop("parameters", None) | |
| if parameters is not None: | |
| prediction = self.pipeline(inputs, **parameters) | |
| else: | |
| prediction = self.pipeline(inputs) | |
| return prediction |