Fine-tuning stage,也就是微调阶段,其主要目的是「提高模型对指令(instruction)的遵循能力」。主要包括Instruction SFT(指令监督微调)、DPO、KTO等技术,本文重点讲解这三类微调技术; Prompting stage,也就是使用模型推理阶段,其主要目的是「让模型按照你的期望输出结果」。这个阶段所使用的模型学习技术主要是ICL(In-Conte...
(2)若不带RQA或RAR两个微调任务(训练中的第二个阶段中的任务),则有近1个点的下降; (3)如果训练中只进行SFT模型,不进行Stage-II训练,则有10个点下降,这说明论文中设计的Stage-II指令任务的有效性。 总结一下:RankRAG框架思路比较简洁,且操作起来也很容易:就是增加一些检索相关则子任务训练,然后让LLM具备重排...
In the fine-tuning stage, enter data using map 68-d output data and tag data for value, get error back propagation, on the rights to the original value of layer-by-layer to adjust the corresponding coefficients. 翻译结果4复制译文编辑译文朗读译文返回顶部 ...
path.join(MODELS_DIR, "ToucanTTS_MassiveDataBigModel_stage3_reworked_v10") if model_dir is not None: meta_save_dir = model_dir else: meta_save_dir = base_dir os.makedirs(meta_save_dir, exist_ok=True) print("Preparing") if gpu_count > 1: rank = int(os.environ["LOCAL_RANK"])...
# stage 2 conv_3 = Conv(pool, 80, kernel=(1, 1), name="conv_3") conv_4 = Conv(conv_3, 192, kernel=(3, 3), name="conv_4") pool1 = mx.sym.Pooling(data=conv_4, kernel=(3, 3), stride=(2, 2), pool_type="max", name="pool1") ...
Mahesan B,Lai W.Optimization of selected chromatographic responses using a designed experiment at the fine-tuning stage in reversed-phase high-performance liquid chromatographic method development.[J].2001,6(6).Mahesan B, Lai W. Optimization of selected chromatographic responses using a designed ...
This leads to a two-stage alignment process heavily incurring resources. By combining these stages into one, ORPO aims to preserve the domain adaptation benefits of SFT while concurrently discerning and mitigating unwanted generation styles as aimed towards by preference-...
stage-whisper stage whisper stagewhisper stage whispers stage-whispers stagey stagey stagflation stagflationary stagged stagger staggered staggered staggerer staggerers staggering staggering时代网英语在线翻译词典收录了283110条英语词汇在线翻译词条,基本涵盖了全部常用英语词汇的中英文双语翻译及用法,是英语学习的有利...
总结:在cross entory的基础上,引入SCL,仅此而已 论文:https://openreview.net/pdf?id=cu7IUiOhujH 资料:https://zhuanlan.zhihu.com/p/278127741 ABSTRACT we propose a supervised contrastive learning (SCL) objective for the fine-tuning stage INTRODUCTION ...
adaptation to human preferences. The reward model is learned via supervised learning, typically using a pretrained LLM as the base model, and is then used to adapt the pretrained LLM to human preferences via additional finetuning. The training in this additional finetuning stage uses a flavor of...