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([...
首先需要在脚本中导入以下必要模块:LlamaForCausalLM 是 Llama 2 的模型类,LlamaTokenizer 为模型准备所需的 prompt,pipeline 用于生成模型的输出,torch 用于引入 PyTorch 并指定想要使用的数据类型。 import torch import transformers from transformers import LlamaForCausalLM, LlamaTokenizer 加载模型 接下来,用下载好...
train_dataset=dataset, peft_config=peft_config, max_seq_length=max_seq_length, tokenizer=tokenizer, packing=True, formatting_func=format_instruction, args=args,)通过调用 Trainer
AutoTokenizer简化了为NLP任务对文本数据进行标记的过程。我们可以看到在下面初始化AutoTokenizer,后面我们会使用SFTTrainer将初始化的AutoTokenizer作为参数。 model_name = "NousResearch/Llama-2-7b-chat-hf" # Load LLaMA tokenizer tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) Bi...
sequences = pipeline ('I have tomatoes, basil and cheese at home. What can I cook for dinner?\n',do_sample=True,top_k=10,num_return_sequences=1,eos_token_id=tokenizer.eos_token_id,max_length=400,)for seq in sequences:print (f"{seq ['generated_text']}") ...
pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200) 从transformer导入的最后一个内容是logging。这是一个日志系统,这在调试代码时非常有用。 logging.set_verbosity(logging.CRITICAL) 从peft库中导入的LoraConfig数据类是一个配置类,它主要存储初始化LoraModel所需的配置,...
sequences = pipeline ('I have tomatoes, basil and cheese at home. What can I cook for dinner?\n',do_sample=True,top_k=10,num_return_sequences=1,eos_token_id=tokenizer.eos_token_id,max_length=400,)for seq in sequences:print (f"{seq ['generated_text']}") ...
deftokenize(self, prompt, add_eos_token=True):#there's probably a way to do this with the tokenizer settings#but again, gotta move fastresult =self.tokenizer( prompt, truncation=True, max_length=self.sequence_len, padding=False, return_tensors=None ...
pipe = pipeline(task="text-generation",model=model,tokenizer=tokenizer,max_length=200) 从transformer导入的最后一个内容是logging。这是一个日志系统,这在调试代码时非常有用。 logging.set_verbosity(logging.CRITICAL) 从peft库中导入的LoraConfig数据类是一个配置类,它主要存储初始化LoraModel所需的配置,LoraMo...
tokenizer_name="StabilityAI/stablelm-tuned-alpha-3b", model_name="StabilityAI/stablelm-tuned-alpha-3b", device_map="auto", stopping_ids=[50278,50279,50277,1,0], tokenizer_kwargs={"max_length":4096}, # uncommentthisifusingCUDA to reduce memory usage ...