Idea 分析了SGD可以finetune LLM的原因,不用Adam改用SGD,在SGD的基础上提出了一个LOw-Memory Optimization(LOMO)的优化器,来全参数finetune LLM,并在下游任务上获得了比lora等更好的效果。(可能因为资源问题没对比Adam的全参数finetune的结果,这个还不够有说服力)8张3090能微调65B的模型了 重要前提 通过SGD减少op...
Full Parameter Fine-tuning for Large Language Models with Limited Resources O网页链接ChatPaper综述:本文论述了如何解决大规模语言模型(LLMs)的训练困难问题,即使用有限资源进行全参数微调。作者提出了一种新的优化器LOMO,将梯度计算和参数更新融合在一起,以减少内存使用。将LOMO与现有的内存节省技术相结合,将内存...
Use PEFT or Full-parameter to finetune 450+ LLMs (Qwen2.5, InternLM3, GLM4, Llama3.3, Mistral, Yi1.5, Baichuan2, DeepSeek-R1, ...) and 150+ MLLMs (Qwen2.5-VL, Qwen2-Audio, Llama3.2-Vision, Llava, InternVL2.5, MiniCPM-V-2.6, GLM4v, Xcomposer2.5, Yi-VL, De
参数高效微调(Parameter Efficient Fine-Tuning,PEFT)通过保持预训练模型参数冻结,仅调整少量参数就可实现大模型在垂直应用领域的高效适配。但目前大多数 PEFT 方法,尤其是视觉领域的 PEFT 方法的性能相较于全量微调而言还存在劣势。Mona 通过更适合视觉信号处理的设计以及对预训练特征分布的动态优化在小于 5% 的参数成本...
data and methods. Easy define and easy start. A large-scale model training framework that supports tasks such as LoRA and full-parameter fine-tuning. Easily initiate your large model training and fine-tuning work by defining a YAML file specifying the base model, dataset, and training parameter...
CLASSIFICATIONENGLISH languageDESIGN techniquesAdapters and Low-Rank Adaptation (LoRA) are parameter-efficient fine-tuning techniques designed to make the training of language models more efficient. Previous results demonstrated that these methods can even improve performance on some...
Discovery biological motifs plays a fundamental role in understanding regulatory mechanisms. Computationally, they can be efficiently represented as kmers, making the counting of these elements a critical aspect for ensuring not only the accuracy but als
Currently, we support full-parameter training and LoRA training for AnimateDiff. 🎉 News 2024.04.13: Support the fine-tuning and inference of Mixtral-8x22B-v0.1 model, use this script to start training! 2024.04.13: Support the newly launched MiniCPM series: MiniCPM-V-2.0、MiniCPM-2B-128k...
OpenRLHF supports convenient and efficient Reinforced Fine-tuning. You only need to implement afile containing the customreward_funcfunctionand pass its path to theremote_rm_urlparameter. Such as # reward_func.pyimporttorchdefreward_func(queries,prompts,labels):# queries is prompts + responses# la...
and always converge to the same solution after repeated operations; furthermore, DE converges fast and is very accurate for high-dimensional problems, which has three main parameters (initialize solution size, scaling factor F, crossover probability CR), but it is not sensitive for parameter setup...