| from modelscope import AutoModelForCausalLM, AutoTokenizer | |
| import torch | |
| import pdb | |
| from fastchat.model import load_model, add_model_args | |
| class vicuna7b(object): | |
| def __init__(self, model_path='~/.cache/modelscope/hub/AI-ModelScope/vicuna-7b-v1___5', torch_dtype=torch.float32, device='cuda', max_new_tokens=5): | |
| print("Loading model from", model_path) | |
| self.model, self.tokenizer = load_model(model_path, device=device, load_8bit=False, dtype=torch_dtype) | |
| self.model_path = model_path | |
| self.max_new_tokens = max_new_tokens | |
| def generate(self, input_text, max_new_tokens=None): | |
| if max_new_tokens is None: | |
| max_new_tokens = self.max_new_tokens | |
| inputs = self.tokenizer(input_text, return_tensors="pt").to(self.model.device) | |
| outputs = self.model.generate(**inputs, max_new_tokens=max_new_tokens) | |
| if self.model.config.is_encoder_decoder: | |
| outputs = outputs[0] | |
| else: | |
| outputs = outputs[0][len(inputs["input_ids"][0]) :] | |
| return self.tokenizer.decode(outputs, skip_special_tokens=True, spaces_between_special_tokens=False) | |
| if __name__=='__main__': | |
| model = vicuna7b() | |
| print(model.generate("Yesterday was Thursday, today is Friday, so tomorrow is ", 10)) | |
| print(model.generate("Yesterday was 2022-01-01, today is 2022-01-02, so tomorrow is ", 10)) |