padding='longest',return_tensors='pt').to(device)input_ids=inputs.input_idsn=input_ids.shape[0]withtorch.no_grad():foriinrange(max_tokens):# 模型输出model_input=model.prepare_inputs_for_generation(input_ids)outputs=model
model_kwargs={"temperature": 0, "max_length": args.max_length, "trust_remote_code": True},)然后，创建一个正常的对话链 LLMChain，并将已经创建的 llm 设置为输入参数。# The following code is complete the same as the use-casevoiceassistant_chain = LLMChain( llm=llm, ...
tokenizer.pad_token = tokenizer.unk_token input = tokenizer(prompts, padding='max_length', max_length=20, return_tensors="pt"); print(input) 在这个例子中,我要求tokenizer填充到max_length。我将max_length设置为20。如果你的示例包含10个标记,tokenizer将添加10个填充标记。 {'input_ids': tensor([...
model_inputs = tokenizer(inputs, max_length=max_length, padding="max_length", truncation=True, return_tensors="pt") labels = tokenizer(targets, max_length=3, padding="max_length", truncation=True, return_tensors="pt") labels = labels["input_ids"] labels[labels == tokenizer.pad_token_...
MAX_MAX_NEW_TOKENS = 2048 DEFAULT_MAX_NEW_TOKENS = 1024 MAX_INPUT_TOKEN_LENGTH = 4000 DESCRIPTION = """ LICENSE = """ logger.info("Starting") def clear_and_save_textbox(message: str) -> tuple[str, str]: return '', message def display_input(message: str, history: list[tuple[str...
Unlimiformer: Long-Range Transformers with Unlimited Length Input https://github.com/abertsch72/unlimiformer 适用于Encoder-Decoder模型,长文本摘要等场景 特意起了个隐式搜索的标题,是因为和上面的文本搜索实现有异曲同工之妙,本质的差异只是以上是离散文本块的搜索。而Unlimiformer是在解码阶段对超长输入,to...
# Apply softmax to obtain probabilitiesprobs = torch.nn.functional.softmax(logits, dim=-1) # Extract the generated tokens from the outputgenerated_tokens = outputs.sequences[:, input_length:] # Compute conditional probabilityconditional_probability =...
Unlimiformer: Long-Range Transformers with Unlimited Length Inputhttps://github.com/abertsch72/unlimiformer适用于Encoder-Decoder模型,长文本摘要等场景 特意起了个隐式搜索的标题,是因为和上面的文本搜索实现有异曲同工之妙,本质的差异只是以上是离散文本块的搜索。而Unlimiformer是在解码阶段对超长输入,token...
labels = tokenizer(targets, max_length=3, padding="max_length", truncation=True, return_tensors="pt") labels = labels["input_ids"] labels[labels == tokenizer.pad_token_id] = -100 model_inputs["labels"] = labels returnmodel_inputs ...
Unlimiformer: Long-Range Transformers with Unlimited Length Input https://github.com/abertsch72/unlimiformer 适用于Encoder-Decoder模型,长文本摘要等场景 特意起了个隐式搜索的标题,是因为和上面的文本搜索实现有异曲同工之妙,本质的差异只是以上是离散文本块的搜索。而Unlimiformer是在解码阶段对超长输入,to...