I have recently compiled a simple and easy-to-understand guide on the GPU memory requirements for fine-tuning with LoRA and QLoRA, which can help you estimate the memory needed when fine-tuning using LoRA and QLoRA. Below, we will explain step by step, requiring minimal background knowledge.1. What are LoRA and QLoRA? LoRA (Low-Rank Adaptation):This is a … Read more
As mentioned in previous articles, LoRA fine-tuning primarily targets the weight matrices of linear layers, such as the Q, K, and V projection matrices in the attention mechanism, as well as the weight matrices in the feedforward network (FFN). So, when fine-tuning a model with a Transformer architecture using LoRA, which weight matrices should we … Read more
Source: DeepHub IMBA This article is approximately 2000 words long, suggesting an 8-minute read. GaLore can save VRAM, allowing training of a 7B model on consumer-grade GPUs, but it is slower, taking almost twice as long as fine-tuning and LoRA. Training large language models (LLMs), even those with “only” 7 billion parameters, is a computationally … Read more
The open-source dataset found contains less than 50,000 Q&A pairs, and it is recommended to have over 200G of memory. My local setup with 60G of memory cannot run it. The lora uses Hugging Face’s peft: https://github.com/huggingface/peft Two versions of the training part were written: One references the peft example: https://github.com/huggingface/peft/tree/main/examples. With 60G memory and … Read more
MLNLP community is a well-known machine learning and natural language processing community, covering domestic and international NLP master’s and doctoral students, university teachers, and corporate researchers. The Vision of the Community is to promote communication and progress between the academic and industrial circles of natural language processing and machine learning at home and abroad, especially … Read more
This article introducesThe Hong Kong University of Science and Technology (Guangzhou)a paper on efficient fine-tuning of large models (LLM PEFT Fine-tuning) titled “Parameter-Efficient Fine-Tuning with Discrete Fourier Transform”, which has been accepted by ICML 2024, and the code has been open-sourced. Paper link: https://arxiv.org/abs/2405.03003 Project link: https://github.com/Chaos96/fourierft Background Large foundation models have achieved remarkable successes … Read more
MLNLP community is a well-known machine learning and natural language processing community at home and abroad, covering domestic and international NLP graduate students, university teachers, and corporate researchers. The vision of the community is to promote communication and progress between academia, industry, and enthusiasts in natural language processing and machine learning, especially for beginners. Selected … Read more
As the parameter count of large models continues to grow, the cost of fine-tuning the entire model has become increasingly unacceptable. To address this, a research team from Peking University proposed a parameter-efficient fine-tuning method called PiSSA, which surpasses the fine-tuning effects of the widely used LoRA on mainstream datasets. Paper Title: PiSSA: Principal Singular … Read more
The mainstream method for fine-tuning LLMs, LoRA, has a new variant. Recently, NVIDIA partnered with Hong Kong University of Science and Technology to announce an efficient fine-tuning technique called DoRA, which achieves more granular model updates through low-rank decomposition of pre-trained weight matrices, significantly improving fine-tuning efficiency. In a series of downstream tasks, both training … Read more
Article Link: https://arxiv.org/abs/2409.01035 Code Link: https://github.com/Chongjie-Si/Subspace-Tuning Project Homepage: https://chongjiesi.site/project/2024-lora-dash.html Due to the rich content of the LoRA-Dash paper, compressing 30 pages of content into 10 pages is a highly challenging task. Therefore, we have made careful trade-offs between readability and content integrity. The starting point of this article may differ from the original paper, aligning … Read more