| from typing import Dict, List, Any |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| import transformers |
| import torch |
|
|
| dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.float16 |
|
|
| 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 = 100 |
| generation_config.temperature = 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 |
| ) |
|
|
| def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]: |
| prompt = data.pop("inputs", data) |
| result = self.pipeline(prompt, generation_config=self.generation_config) |
| return result |
