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 "...
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 of other shows I might like? Answer: Of course! If you enjoyed "Breaking...
disable_tqdm=disable_tqdm, report_to="tensorboard", seed=42)# Create the trainertrainer = SFTTrainer( model=model, train_dataset=dataset, peft_config=peft_config, max_seq_length=max_seq_length, tokenizer=tokenizer, packing=packing, formatting_func=format_instruction, ...
max_seq_length=max_seq_length,tokenizer=tokenizer,packing=True)# start training, the model will b...
--model_max_length 2048 --gradient_checkpointing True --lazy_preprocess True --bf16 True --tf32 True --report_to "none" """ 微调脚本 微调使用 torchrun + DeepSpeed 进行分布式训练 %%writefile./src/ds-train-dist.sh#!/bin/bashCURRENT_HOST="${SM_CURRENT_HOST}"IFS=','read-ra hosts_ar...
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,这个脚本已经可以运行了。保存脚本,回到 Conda 环境,输入 python < 脚本名称 >.py 并按回车键来运行脚本。
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...
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: 复制 sequences = pipeline ( 'I have tomatoes, basil and cheese at home. What can I cook for dinner?\n', ...
model=model, train_dataset=dataset, peft_config=peft_config, max_seq_length=max_seq_length, tokenizer=tokenizer, packing=packing, formatting_func=format_instruction, args=args, ) # train the model trainer.train() # there will not be a progress bar since tqdm is disabled ...
model_id = "NousResearch/Llama-2-7b-hf" max_length = 512 device_map = "auto" batch_size = 128 micro_batch_size = 32 gradient_accumulation_steps = batch_size // micro_batch_size bnb_config = BitsAndBytesConfig( load_in_4bit=True, # load the model into memory using 4-bit precision...