| from typing import Dict, List, Any |
| from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig |
| import torch |
|
|
| class EndpointHandler(): |
| def __init__(self, path=""): |
| self.base_model = path |
|
|
| bitsandbytes= BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16) |
| self.model = AutoModelForCausalLM.from_pretrained(self.base_model, device_map={"":0},quantization_config= bitsandbytes, trust_remote_code= True) |
| self.tokenizer = AutoTokenizer.from_pretrained(self.base_model, trust_remote_code=True) |
| self.tokenizer.pad_token = self.tokenizer.eos_token |
|
|
| def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: |
| inputs = data.pop("inputs",data) |
| prompt = f"Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {inputs} ### Response:" |
| model_inputs = self.tokenizer([prompt], return_tensors="pt", padding=True).to("cuda") |
| generated_ids = self.model.generate(**model_inputs, max_length=200) |
| output = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True) |
| answer_without_prompt = output[0].split("### Response:")[1].strip() |
| prediction = answer_without_prompt.split("###")[0].strip() |
| return [{"generated_text": prediction}] |
