模型生成了一段随机的文本, PreTrainedModel.generate() 的默认参数可以在Pipelines 中直接覆盖,比如下面的max_length。 from transformers import pipeline text_generator = pipeline("text-generation") print(text_generator("As far as I am concerned, I will", max_length=50, do_sample=False)) [{'genera...
What does do_sample parameter of the generate method of the Hugging face model do? Generates sequences for models with a language modeling head. The method currently supports greedy decoding, multinomial sampling, beam-search decoding, and beam-search multinomial sampling. do_sample (bool, optional...
要用generate()函数激活束搜索,我们只需要用num_beams参数指定波束的数量。我们选择的波束越多,可能得到的结果就越好;然而,生成过程会变得更慢,因为我们为每个波束生成平行序列: output_beam = model.generate(input_ids, max_length=max_length, num_beams=5, do_sample=False) logp = sequence_logprob(model, ...
GenerationMixin的generate方法可用于 贪婪搜索greedy_search,num_beams=1 and do_sample=False 对比搜索contrastive_search,penalty_alpha>0 and top_k>1 多项式采样sample,num_beams=1 and do_sample=True 束搜索beam_search,num_beams>1 and do_sample=False 多项式采样束搜索beam_sample,num_beams>1 and do_s...
为了看看我们如何利用温度来影响生成的文本,让我们通过在generate()函数中设置温度参数,以T=2为例进行采样(我们将在下一节解释top_k参数的含义): output_temp = model.generate(input_ids, max_length=max_length, do_sample=True, temperature=2.0, top_k=0) ...
output = model.generate(**inputs, do_sample =True, fine_temperature =0.4, coarse_temperature =0.8) # get the end time end_event.record() torch.cuda.synchronize() # measure memory footprint and elapsed time max_memory = torch.cuda.max_memory_allocated(device) ...
(PADDING_TEXT + prompt, add_special_tokens=False, return_tensors="pt")["input_ids"]prompt_length = len(tokenizer.decode(inputs[0]))outputs = model.generate(inputs, max_length=250, do_sample=True, top_p=0.95, top_k=60)generated = prompt + tokenizer.decode(outputs[0])[prompt_length...
{sample['response']} ### Response: """ input_ids = tokenizer(prompt, return_tensors="pt", truncation=True).input_ids.cuda() # with torch.inference_mode(): outputs = model.generate(input_ids=input_ids, max_new_tokens=100, do_sample=True, top_p=0.9,temperature=0.9) ...
input_ids=tokenizer(input_txt,return_tensors="pt")["input_ids"].to(device)output=model.generate(input_ids,max_new_tokens=n_steps,do_sample=False)print(tokenizer.decode(output[0])) 代码语言:javascript 复制 Setting`pad_token_id`to`eos_token_id`:50256foropen-end generation.Transformers are ...
generated_ids = model.generate(input_ids, max_length=1000, do_sample=True) # 将生成的文本解码为字符串 generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True) print(generated_text) 以上代码将生成一篇以"人工智能是未来的发展趋势"开头的文章。你可以根据需要进行调整,例如改变输...