A look at this year’s crop of LoRA alternatives, including SVF, SVFT, MiLoRA, PiSSA, and LoRA-XS, all based on SVD (Singular Value Decomposition). The article compares these techniques to the original LoRA method for fine-tuning Large Language Models.
| Method | Description | Key Feature(s) | Reference |
|--------------|---------------------------------------------|---------------------------------------------|-|
| LoRA | Freezes the model and trains a small pair of low-rank “adapter” matrices. | Saves memory and compute cycles by reducing the number of trainable parameters. | arxiv.org/abs/2106.09685 |
| SVF | Uses SVD on the model’s weight matrices and fine-tunes the singular values directly. | More economical in parameters than LoRA; makes tuned models composable. | arxiv.org/abs/2501.06252v2 |
| SVFT | Adds more trainable weights on the diagonal and evaluates various alternatives. | Provides more trainable values than just the diagonal, useful for better fine-tuning. | arxiv.org/abs/2405.19597 |
| PiSSA | Tunes only the large principal values. | Designed to approximate full fine-tuning by adapting the principal singular components. | arxiv.org/abs/2404.02948 |
| MiLoRA | Tunes only the small principal values. | Retains base model’s knowledge while adapting to new tasks. | arxiv.org/abs/2406.09044 |
| LoRA-XS | Similar to PiSSA but with a slightly different mechanism. | Shows good results with significantly fewer parameters than LoRA. | arxiv.org/abs/2405.17604 |
| DoRA | Splits weights into magnitudes and directions then tunes those. | | arxiv.org/abs/2402.09353 |
| AdaLoRA | Complex mechanism for finding the best tuning rank for a given budget of trainable weights. | | arxiv.org/abs/2303.10512 |
| Method | Description | Key Feature(s) | Reference |
|--------------|---------------------------------------------|---------------------------------------------|-|
| LoRA | Freezes the model and trains a small pair of low-rank “adapter” matrices. | Saves memory and compute cycles by reducing the number of trainable parameters. | arxiv.org/abs/2106.09685 |
| SVF | Uses SVD on the model’s weight matrices and fine-tunes the singular values directly. | More economical in parameters than LoRA; makes tuned models composable. | arxiv.org/abs/2501.06252v2 |
| SVFT | Adds more trainable weights on the diagonal and evaluates various alternatives. | Provides more trainable values than just the diagonal, useful for better fine-tuning. | arxiv.org/abs/2405.19597 |
| PiSSA | Tunes only the large principal values. | Designed to approximate full fine-tuning by adapting the principal singular components. | arxiv.org/abs/2404.02948 |
| MiLoRA | Tunes only the small principal values. | Retains base model’s knowledge while adapting to new tasks. | arxiv.org/abs/2406.09044 |
| LoRA-XS | Similar to PiSSA but with a slightly different mechanism. | Shows good results with significantly fewer parameters than LoRA. | arxiv.org/abs/2405.17604 |
| DoRA | Splits weights into magnitudes and directions then tunes those. | | arxiv.org/abs/2402.09353 |
| AdaLoRA | Complex mechanism for finding the best tuning rank for a given budget of trainable weights. | | arxiv.org/abs/2303.10512 |
This tutorial guides readers on how to fine-tune the Mistral 7B large language model using QLoRA with the Axolotl library, focusing on managing limited GPU resources for efficient training. It covers environment setup, dataset creation, configuration of QLoRA hyperparameters, the fine-tuning process, and testing the fine-tuned model.
The article explores techniques to improve Large Language Model (LLM) accuracy, focusing on Lamini Memory Tuning. It discusses fine-tuning methods like Low-Rank Adaptation (LoRA), the advantages and disadvantages of fine-tuning, and practical steps using Lamini to achieve higher precision in SQL query generation. The author demonstrates a step-by-step approach to creating a high-quality dataset, fine-tuning, and evaluating model accuracy.
This article provides a comprehensive guide on fine-tuning the Llama 3.1 language model using Unsloth for efficient parameter-efficient training. It covers concepts like supervised fine-tuning, LoRA, QLoRA, and practical steps for training on a high-quality dataset.
This article announces a comprehensive course on fine-tuning large language models (LLMs) offered on the freeCodeCamp.org YouTube channel. The course, developed by Krish Naik, covers topics such as QLORA, LORA, quantization with LLama2, gradient, and Google Gemma Model, among others. The course aims to help learners deepen their understanding of machine learning and artificial intelligence.
efficient method for fine-tuning LLM using LoRA and QLoRA, making it possible to train them even on consumer hardware
