| from modelscope import AutoTokenizer, AutoModel | |
| import torch | |
| import pdb | |
| class chatglm2(object): | |
| def __init__(self, model_path='~/.cache/modelscope/hub/ZhipuAI/chatglm2-6b', torch_dtype=torch.float32, device='cuda', max_new_tokens=5): | |
| print("Loading model from", model_path) | |
| self.model = AutoModel.from_pretrained(model_path, torch_dtype=torch_dtype, device_map=device, trust_remote_code=True) | |
| self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) | |
| 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").input_ids.to(self.model.device) | |
| outputs = self.model.generate(inputs, max_length=len(inputs[0])+max_new_tokens) | |
| return self.tokenizer.batch_decode(outputs)[0][len(input_text):] | |
| if __name__=='__main__': | |
| model = chatglm2() | |
| 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)) |