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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)) |