| from typing import Any, Dict |
| import torch |
| import transformers |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
|
|
| |
| dtype = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.get_device_capability()[0] == 8 else torch.float16 |
|
|
| class EndpointHandler: |
| def __init__(self, path="vkamra/llama_finetune_clockit"): |
| |
| tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True) |
| tokenizer.padding_side = "left" |
|
|
| |
| if torch.cuda.is_available(): |
| model = AutoModelForCausalLM.from_pretrained( |
| path, |
| return_dict=True, |
| device_map="auto", |
| load_in_8bit=True, |
| torch_dtype=dtype, |
| trust_remote_code=True, |
| ) |
| else: |
| model = AutoModelForCausalLM.from_pretrained( |
| path, |
| return_dict=True, |
| torch_dtype=torch.float32, |
| trust_remote_code=True, |
| ) |
|
|
| |
| generation_config = model.generation_config |
| generation_config.max_new_tokens = 60 |
| generation_config.temperature = 0.7 |
| 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 |
|
|
