trainer = SFTTrainer( model=model, train_dataset=dataset, peft_config=peft_config, dataset_text_field="text", max_seq_length=max_seq_length, tokenizer=tokenizer, args=training_arguments, ) 8)在微调的时候,对LN层使用float 32训练更稳定 for name, module in trainer.model.named_modules(): if "...
运行ln -h ./tokenizer.model ./llama-2-7b-chat/tokenizer.model,创建在下一步的转换时需要使用的 tokenizer 的链接。 转换模型权重,以便与 Hugging Face 一起运行: TRANSFORM=`python -c"import transformers;print ('/'.join (transformers.__file__.split ('/')[:-1])+'/models/llama/convert_llama_...
from trl import SFTTrainermax_seq_length = 2048# 数据集的最大长度序列trainer = SFTTrainer( model=model, 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...
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']}") 步骤4:运行 Llama 现在...
通过更改 max_length 可以指定希望生成响应的长度。将 num_return_sequences 参数设置为大于 1,可以生成多个输出。在脚本中添加以下内容,以提供输入以及如何运行 pipeline 任务的信息: sequences = pipeline ('I have tomatoes, basil and cheese at home. What can I cook for dinner?\n',do_sample=True,top...
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([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 887, 526, 451, 263, 13563, 7451, 29889], ...
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,...
config=peft_config, max_seq_length=max_seq_length, tokenizer=tokenizer, packing=packing, formatting_func=format_instruction, args=args,)# train the modeltrainer.train() # there will not be a progress bar since tqdm is disabled# save model in localtrainer.save_model()这些参数...
\n', do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, max_length=200,)for seq in sequences: print(f"Result: {seq['generated_text']}")Result: I liked "Breaking Bad" and "Band of Brothers". Do you have any recommendations...
, model="codellama/CodeLlama-7b-hf", torch_dtype=torch.float16, device_map="auto",)sequences = pipeline('def fibonacci(', do_sample=True, temperature=0.2, top_p=0.9, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, max_length=100,)for seq...