BayJarvis: Blogs on peft

paper GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection - 2024-03-21

Training Large Language Models (LLMs) presents significant memory challenges predominantly due to the growing size of weights and optimizer states. While common memory-reduction approaches, such as Low-Rank Adaptation (LoRA), have been employed to mitigate these challenges, they typically underperform training with full-rank weights in both pre-training and fine-tuning stages. This limitation arises because these approaches restrict the parameter search to a low-rank subspace, altering training dynamics and potentially requiring a full-rank warm start. …

paper Scaling Laws for Forgetting When Fine-Tuning Large Language Models - 2024-03-16

When fine-tuning Large Language Models (LLMs) like GPT-3 or BERT for specific tasks, a common challenge encountered is "forgetting" – where the model loses some of its pre-trained capabilities. This phenomenon is particularly noticeable in Parameter-Efficient Fine-Tuning (PEFT) methods such as Low-Rank Adapters (LoRA). …

paper A Rank Stabilization Scaling Factor for Fine-Tuning with LoRA - 2024-03-14

Low-Rank Adapters (LoRA) have emerged as a popular parameter-efficient fine-tuning method for large language models. By adding trainable low-rank "adapters" to selected layers, LoRA enables effective fine-tuning while dramatically reducing the number of parameters that need to be trained. However, the conventional LoRA method uses a scaling factor for the adapters that divides them by the rank. A new paper by researcher Damjan Kalajdzievski shows that this rank-dependent scaling actually slows down learning and limits performance improvements when using higher-rank adapters. …

llm In Brief: Welcome Google's Gemma - New Open LLM - 2024-02-22

Google has just introduced Gemma, an innovative family of state-of-the-art open Large Language Models (LLMs), marking a significant stride in the open-source AI landscape. This release, featuring both 7B and 2B parameter models, underscores Google's ongoing commitment to open-source AI. The Hugging Face team is thrilled to support this launch, ensuring seamless integration within our ecosystem. …

llm Harnessing Zephyr's Breeze: DPO Training on Mistral-7B-GPTQ for Language Model Alignment - 2023-11-09

We've taken on the exciting challenge of implementing the cutting-edge strategies presented in "ZEPHYR: Direct Distillation of LM Alignment". This paper's approach is not just theoretical—it's a blueprint for a significant leap in language model training. By adopting ZEPHYR's distilled direct preference optimization (dDPO), we've embarked on a code journey that brings these innovations from concept to reality. …

llm Fine-tuning Zephyr 7B GPTQ with 4-Bit Quantization for Custom Data and Inference - 2023-11-08

Model fine-tuning and quantization play pivotal roles in creating efficient and robust machine learning solutions. This blog post explores the fine-tuning process of the Zephyr 7B GPT-Q model using 4-bit quantization to boost its performance for custom data inference tasks. …

llm Building the Future of Instruction-Based Code Generation: An Exploration of Code Alpaca's LLaMA Models with Ludwig's Fine-Tuning QLORA Technique - 2023-09-01

In the vast realm of machine learning, fine-tuning stands out as one of the most crucial techniques for adapting pre-trained models to new tasks. Ludwig, a deep learning toolkit, offers a diverse palette of fine-tuning strategies that cater to different needs. In this blog, we'll delve into these techniques, especially focusing on the Quantization-Based Fine-Tuning (QLoRA) method, as we explore the Code Alpaca project's efforts in instruction-based code generation using LLaMA models. …