Fine-Tuning

Why LoRA Has Become an Indispensable Core Technology for Fine-Tuning Large Models?

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Why LoRA Has Become an Indispensable Core Technology for Fine-Tuning Large Models?

In the field of artificial intelligence, large language models (LLMs) such as Claude, LLaMA, and DeepSeek are becoming increasingly powerful. However, adapting these models to specific tasks, such as legal Q&A, medical dialogues, or internal knowledge queries for a company, traditionally involves “fine-tuning” the model. This often entails significant computational overhead and high resource costs. … Read more

No-Code LoRA Fine-Tuning: Easily Build Your Own LLM

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No-Code LoRA Fine-Tuning: Easily Build Your Own LLM

In the previous article, we discussed the theory of fine-tuning. How do we implement it? First, let’s take a look at the effects of fine-tuning. The ultimate goal of fine-tuning is to enhance the model’s performance on specific tasks. We demonstrate the effects of fine-tuning by comparing the original model, the model with added system … Read more

Guide to Calculating GPU Memory Requirements for LoRA and QLoRA Fine-Tuning: Understandable for Beginners

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Guide to Calculating GPU Memory Requirements for LoRA and QLoRA Fine-Tuning: Understandable for Beginners

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

Essential Tips for LoRA Fine-Tuning

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Essential Tips for LoRA Fine-Tuning

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

Efficient LLM Fine-Tuning Using GaLore on Local GPU

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Efficient LLM Fine-Tuning Using GaLore on Local GPU

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

Fine-tuning CPU Lora ChatGLM2-6B

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Fine-tuning CPU Lora ChatGLM2-6B

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

Full-Scale Fine-Tuning Is Harmful!

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Full-Scale Fine-Tuning Is Harmful!

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

ICML 2024: New Fourier Fine-Tuning Method Reduces Parameters

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ICML 2024: New Fourier Fine-Tuning Method Reduces Parameters

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

Cost-Effective Fine-Tuning with LoRA for Large Models

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Cost-Effective Fine-Tuning with LoRA for Large Models

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

New Method PiSSA Significantly Enhances Fine-Tuning Effects

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New Method PiSSA Significantly Enhances Fine-Tuning Effects

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