| from typing import Dict, List, Any |
| from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline |
| import torch |
| from peft import PeftModel |
| import json |
| import os |
|
|
|
|
| class EndpointHandler(): |
| def __init__(self, path=""): |
| base_model_path = json.load(open(os.path.join(path, "training_params.json")))["model"] |
| model = AutoModelForCausalLM.from_pretrained( |
| base_model_path, |
| torch_dtype=torch.float16, |
| low_cpu_mem_usage=True, |
| trust_remote_code=True, |
| device_map="auto", |
| ) |
| tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True) |
| model.resize_token_embeddings(len(tokenizer)) |
| model = PeftModel.from_pretrained(model, path) |
| model = model.merge_and_unload() |
| 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 |
