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from modelscope import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
import torch
import pdb
class qwen7b(object):
def __init__(self, model_path='~/.cache/modelscope/hub/qwen/Qwen-7B', torch_dtype=torch.float32, device='cuda', max_new_tokens=5):
print("Loading model from", model_path)
self.model = AutoModelForCausalLM.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.generation_config = GenerationConfig.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, max_new_tokens=None)
return self.tokenizer.batch_decode(outputs)[0][len(input_text):]
if __name__=='__main__':
model = qwen7b()
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)) |