While _has_unfinished_sequences 判断当前序列长度和max len来决定是否继续decode model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs) 用来准备输入的内容,这里包括了input_ids,attention_mask,past_key_values等属性, 对应了LlamaForCausalLM类forward中的属性。每一步decode前都要运行。
previously unexplored valley, in the Andes Mountains. Even more surprising to the researchers was the fact that the unicorns spoke perfect English.nn"""input_ids = tokenizer(input_txt, return_tensors="pt")["input_ids"].to(device)output_greedy = model.generate(input_ids, max_...
use my own model, infer by output_texts = model.generate( input_ids=input_ids, attention_mask=attention_mask, pad_token_id= tokenizer.eos_token_id, eos_token_id= tokenizer.eos_token_id, max_new_tokens=500, do_sample=False, top_k=30, top_p=0.85, temperature=0.3, repetition_penalty=1...
format(input=final_prompt) print("将要喂给模型的完整prompt如下:\n%s" % final_prompt) # 进行模型推理 # inputs = tokenizer(final_prompt) # result = model.generate(**inputs) 配置样例 以chatglm2-6b 为例: 配置: 当前问题拼接规范:[Round {round}]\n\n问:{question}\n\n答:历史问答拼接...
为何model.generate和model.chat生成效果不一样?不理解为什么这样 因为得按找我们的说明来用呀,两个干的本来也不是同一个事情的。 具体解释:generate接口的功能只是续写;chat接口是对话,有特定的格式。而且chat接口也是可以调整generation相关参数的,修改model.generation_config就可以。
The text generation can be done by the model.generate function, where we can specify all important parameters like saved chat history, length of the response in tokens, and usage of both Top-K and Top-p sampling. chat_history_ids = model.generate(bot_input_ids, do_sample...
generate_text(prompt, max_length, top_k, top_p) # 返回结果 return { 'output': output } 在这里,我们首先将 top-k 和 top-p 参数初始化为默认值,然后从推理请求中获取这些参数并覆盖默认值。最后,我们使用更新后的参数来调用 my_model.generate_text() 函数生成文本,并将结果返回给客户端。 通过...
>>> generate_ids = model.generate(inputs.input_ids, max_length=30) >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you." ...
(inputs, max_length=model_config.seq_length, padding="max_length")["input_ids"] outputs = model.generate(inputs_ids, max_length=model_config.max_decode_length, do_sample=model_config.do_sample, top_k=model_config.top_k, top_p=model_config.top_p)foroutputinoutputs: print(token...
inputs.position_ids+=past_length attention_mask=inputs.attention_mask attention_mask=torch.cat((attention_mask.new_ones(1,past_length),attention_mask),dim=1)inputs['attention_mask']=attention_mask history.append({"role":role,"content":query})foroutputsinself.stream_generate(**inputs,past_ke...