input_ids = tokenizer(input_text, return_tensors="pt") outputs = model.generate(**input_ids) print(outputs) outputs = model.generate(**input_ids) print(outputs) generate是专门为了生成任务生成token设计的,直接调用model是对应的forward实现函数,返回的是Causal LMOutputWithPast huggingface.co/trans...
output = model(input_ids=torch.tensor(input_ids), attention_mask=torch.tensor(input_mask), output_attentions=True) logits = output.logits attentions = output.attentions prob = torch.softmax(logits, dim=-1) token_prob = torch.tensor([prob[i, input_ids[i]] for i in range(len(input_ids...
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=1.2) ...
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。 chat是怎么实现对话的, 我看chat里也是调用的generate函数, 理论上调整generate也可以实现chat的功能? jklj077 commented on Nov 9, 2023 jklj077 on Nov 9, 2023 ...
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...
the generated AE should also lie in the same problem-space of IDS. We adapt existing feature-space AE generation algorithms to problem-space algorithms in order to generate AEs that can map back to valid real network events. The key insight for detecting AEs is to identify the discrepancy bet...
a programming code that can solve the Task.### Task:{instruction}### Input:{input}### Response:"""# Tokenize the inputinput_ids = tokenizer(prompt, return_tensors="pt", truncation=True).input_ids.cuda()# Run the model to infere an outputoutputs = model.generate(input_ids=input_ids...
主要内容：3dGS，MVO，GUI，Stream还有Modeling Kernel Bridge。 3dGS是HOOPS最为核心的模块，其负责HOOPS...
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...
GPT3ForTextGeneration类可以将故事续写等任务模型加载起来用于训练和推理，可以配置模型generate参数，包括：...