Q-GaLore Released: A Memory-Efficient Training Approach for Pre-Training and Fine-Tuning Machine Learning Models
What are Large Language Models (LLMs) primarily used for?
Large Language Models (LLMs) are primarily used for understanding and generating human-like text, performing tasks such as content creation, language translation, summarization, information retrieval, and conversational interactions3. They are also employed in domains like healthcare, software development, and robotics, among others.
How much memory is required to train a LLaMA 7B model?
Training a LLaMA 7B model from scratch typically requires around 58 GB of memory, including 14 GB for trainable parameters, 42 GB for Adam optimizer states and weight gradients, and 2 GB for activation2. However, techniques like GaLore and Q-GaLore can significantly reduce memory requirements, even allowing training on GPUs with 16GB of memory.
What is the GaLore method's approach to reducing memory usage?
The GaLore method reduces memory usage during Large Language Model (LLM) training by leveraging the low-rank structure of gradients. It projects the gradient matrix into a low-rank subspace using projection matrices, significantly reducing memory requirements for storing optimizer states5. This approach allows for full-parameter learning while being more memory-efficient than common low-rank adaptation methods like LoRA.