input_ids = tokenizer(prompt, return_tensors="pt").input_ids gen_tokens = model.generate( input_ids, do_sample=True, temperature=0.9, max_length=100, ) gen_text = tokenizer.batch_decode(gen_tokens)[0] print("gen_text: ", gen_text) (2)完整的 0-train_tokenizer 代码如下: import ...
"" --> 254 return self.encoder.get(token, self.encoder.get(self.unk_token)) 255 ipdb> self.encoder.get(token, self.encoder.get(self.unk_token)) 1639 ipdb> token 'You' 至此,就实现了,基于bpe,来tokenize输入的长的字符串了。 带上mask: ipdb> inputs {'input_ids': tensor([[ 1639, ...
torch_model = AutoModelForSeq2SeqLM.from_pretrained(t5_model) 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_lengt...
if input: return f"""Instruction: {instruction} text = "你叫什么名字?" prompt = f'Question: {text.strip()}\n\nAnswer:' Input: {input} inputs = tokenizer(prompt, return_tensors="pt").to(0) output = model.generate(inputs["input_ids"], max_new_tokens=40) print(tokenizer.d...
"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))
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...
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: ...
text == tokenizer.decode(tokenizer(text, add_special_tokens=False)["input_ids"]) However, it is not the case, unlike thetiktokenreference implementation, which is correctly invertible. For example, given the sentenceIs this restaurant family-friendly ? Yes No Unsure ? This is a follow-up se...
Tensor, torch.Tensor]: input_ids_lst = [] attention_mask_lst = [] max_input_ids_len = -1 max_attention_mask_len = -1 prompt_size = 0 if prompt is not None: assert prompt.size(0) == num_vq, "prompt dim 0 must equal to num_vq" prompt_size = prompt.size(1) # avoid ...