Delete handler.py

handler.py

@@ -1,70 +0,0 @@
-from typing import Dict, Any, List
-import logging
-
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from peft import PeftConfig, PeftModel
-import torch.cuda
-
-dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0]==8 else torch.float16
-
-# LOGGER = logging.getLogger(__name__)
-# logging.basicConfig(level=logging.INFO)
-# device = "cuda" if torch.cuda.is_available() else "cpu"
-
-
-class EndpointHandler():
-    def __init__(self, path=""):
-        tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
-        model = AutoModelForCausalLM.from_pretrained(
-            path,
-            return_dict=True,
-            device_map="auto",
-            load_in_8bit=True,
-            torch_dtype=dtype,
-            trust_remote_code=True,
-        )
-
-        generation_config = model.generation_config
-        generation_config.max_new_tokens=512
-        generation_config.temperation = 0
-        generation_config.num_return_sequences=1
-        generation_config.pad_token_id = tokenizer.eos_token_id
-        generation_config.eos_token_id = tokenizer.eos_token_id
-        self.generation_config = generation_config
-
-        self.pipeline = transformers.pipeline(
-            "text-generation",model=model,tokenizer=tokenizer
-        )
-
-
-        # config = PeftConfig.from_pretrained(path)
-        # model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, load_in_8bit=True, device_map='auto')
-        # self.tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
-        # # Load the Lora model
-        # self.model = PeftModel.from_pretrained(model, path)
-
-    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
-        # """
-        # Args:
-        #     data (Dict): The payload with the text prompt and generation parameters.
-        # """
-        # LOGGER.info(f"Received data: {data}")
-        # Get inputs
-        prompt = data.pop("inputs", None)
-        # parameters = data.pop("parameters", None)
-        # if prompt is None:
-        #     raise ValueError("Missing prompt.")
-        # # Preprocess
-        # input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(device)
-        # # Forward
-        # LOGGER.info(f"Start generation.")
-        # if parameters is not None:
-        #     output = self.model.generate(input_ids=input_ids, **parameters)
-        # else:
-        #     output = self.model.generate(input_ids=input_ids)
-        # # Postprocess
-        # prediction = self.tokenizer.decode(output[0])
-        # LOGGER.info(f"Generated text: {prediction}")
-        # return {"generated_text": prediction}
-        result = self.pipeline(prompt,generation_config=self.generation_config)
-        return result