| from typing import Any, Dict |
| import torch |
| from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipeline |
|
|
| dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.float16 |
|
|
| class EndpointHandler: |
| def __init__(self, path=""): |
|
|
| quantization_config = BitsAndBytesConfig(load_in_8bit=True) |
| tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code = True) |
| model = AutoModelForCausalLM.from_pretrained( |
| path, |
| return_dict = True, |
| device_map = "auto", |
| torch_dtype = dtype, |
| trust_remote_code = True, |
| quantization_config=quantization_config |
| ) |
| |
| gen_config = model.generation_config |
| gen_config.max_new_tokens = 256 |
| gen_config.num_return_sequences = 1 |
| gen_config.pad_token_id = tokenizer.eos_token_id |
| gen_config.eos_token_id = tokenizer.eos_token_id |
| |
| self.generation_config = gen_config |
| |
| self.pipeline = pipeline( |
| 'text-generation', model=model, tokenizer=tokenizer |
| ) |
| |
| |
| |
| def __call__(self, data: Dict[dict, Any]) -> Dict[str, Any]: |
| prompt = data.pop("inputs", data) |
|
|
| instruction = "Create a list of chords,a corresponding scale to improve with, title, and style along with an example in ABC notation based on this input in JSON format." |
|
|
| full_prompt = f"""<s> |
| ### Instruction: |
| {instruction} |
| ### Input: |
| {prompt} |
| ### Response: |
| """ |
| |
| result = self.pipeline(full_prompt, generation_config = self.generation_config)[0] |
| |
| return result |
