本期code:https://github.com/chunhuizhang/llms_tuning/blob/main/tutorials/finetune/trl/collate_fn_formatting_func.ipynbLLM loss function:BV1ZA4m1w7D5,BV1ox4y147o1, 视频播放量 2585、弹幕量 4、点赞数 57、投硬币枚数 35、收藏人数 52、转发人数 3, 视频作者
App [LLMs tuning] 01 trl SFTTrainer 中的 formatting_func 与 DataCollatorForCompletion 2047 1 25:54 App [LLMs tuning] 05 StackLlama、SFT+DPO(代码组织、数据处理,pipeline) 6106 0 23:12 App [LLMs inference] quantization 量化整体介绍(bitsandbytes、GPTQ、GGUF、AWQ) ...
def formatting_func(example): text = f"### Question: {example['question']}\n ### Answer: {example['answer']}" return text trainer = SFTTrainer( "facebook/opt-350m", train_dataset=dataset, packing=True, formatting_func=formatting_func ) trainer.train() 七、通过预训练模型控制参数 控制...
Hi I also have same questions, I use this function as the prompt then the loss turn to 0 when I start training, what should I do? def formatting_prompts_func(example): a_list=[] for i in range(len(example['input'])): text = ( f"### Instruction: {example['instruction'][i]}\...
formatting_func=formatting_prompts_func, data_collator=collator, ) trainer.train() ``` ## Conclusions Packing instruction tuning examples, instead of padding, is now fully compatible with Flash Attention 2, thanks to a recent PR and the new `DataCollatorWithFlattening`. The method is compatible...
text_field（Optional[str]）是训练数据集中包含文本的字段的名称，该文本仅在formatting_func为None时才...
本期code:https://github.com/chunhuizhang/llms_tuning/blob/main/tutorials/finetune/trl/collate_fn_formatting_func.ipynbLLM loss function:BV1ZA4m1w7D5,BV1ox4y147o1, 视频播放量 2683、弹幕量 4、点赞数 59、投硬币枚数 35、收藏人数 54、转发人数 3, 视频作者