# 使用分词器处理输入文本 inputs = tokenizer(prompt, return_tensors="pt") # 生成文本 generated_text = model.generate(**inputs, max_length=50, num_return_sequences=1) # 解码生成的文本 print(tokenizer.decode(generated_text[0], skip_special_tokens=True)) 这段代码首先导入了必要的类,然后加...
torch_tokens = tokenizer(prompt, return_tensors="pt", padding=True).input_ids-outputs = torch_model.generate(torch_tokens, do_sample=False, max_length=512)+outputs = torch_model.generate(torch_tokens, do_sample=False, max_length=10)print(tokenizer.decode(outputs[0], skip_special_tokens=Tru...
prompt config = AutoConfig.from_pretrained(model_path) tokenizer = AutoTokenizer.from_pretrained(model_path, config=config) model = AutoModelForCausalLM.from_pretrained(model_path, config=config).cuda() inputs = tokenizer(prompt, return_tensors="pt") for key in inputs: inputs[key] = input...
prompt = f'Question: {text.strip()}\n\nAnswer:' inputs = tokenizer(prompt, return_tensors="pt").to(0) output = model.generate(inputs["input_ids"], max_new_tokens=40) print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True)) 输出: Question: 你叫什么名字? Answer: ...
"prompt=f'Question: {text.strip()}\n\nAnswer:'inputs=tokenizer(prompt,return_tensors="pt").to(0)output=model.generate(inputs["input_ids"],max_new_tokens=40)print(tokenizer.decode(output[0].tolist(),skip_special_tokens=True))
'is_split_into_words': False, 'pad_to_multiple_of': None, 'return_tensors': 'pt', 'return_token_type_ids': None, 'return_attention_mask': None, 'return_overflowing_tokens': False, 'return_special_tokens_mask': False, 'return_offsets_mapping': False, 'return_length': False, 'ver...
text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt") print(tokenizer.batch_decode(model_inputs["input_ids"])) 0 comments on commit e61714e Please sign in to comment. Footer...
"], return_tensors="pt").input_ids.to("cuda") import time for i in range(10): ti=time.time() re=model(input_ids) print(time.time()-ti) time.sleep(1) tokenizer = RWKVWorldTokenizer(vocab_file=r"D:\rwkv_input\tokenizer\rwkv_vocab_v20230424.txt") input_ids, seq_idx = toke...
Feature Request Transformers recently added a new feature called encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt") which auto-applies the right formatting around the messages for models. Using this could greatly imp...
"; tokenizer_request.set_input_tensor(ov::Tensor{ov::element::string, {1}, &prompt}); tokenizer_request.infer(); ov::Tensor input_ids = tokenizer_request.get_tensor("input_ids"); ov::Tensor attention_mask = tokenizer_request.get_tensor("attention_mask"); ov::InferRequest infer_request...