Fine-tuning large language models (LLMs) is an effective way to modify their behavior or style without retraining from scratch. This approach allows you to extend the capabilities and knowledge base of pre-trained models, making them more suitable for specific tasks or industries.
However, training a model from scratch can be computationally expensive and requires significant resources. Fortunately, fine-tuning existing models using techniques like Low Rank Adaptation (LoRA) and its quantized variant QLoRA can make this process more manageable.
Fine-tuning involves updating the weights of a pre-trained model to better suit your specific use case. This can be done by adjusting a few thousand or million weights to achieve the desired result, rather than retraining the entire model. LoRA freezes a portion of the model’s weights and tracks changes using a second set of matrices.
QLoRA takes this approach further by loading the model’s weights at lower precision, reducing memory requirements from 14 GB for the key value cache to under 16 GB of VRAM. This makes fine-tuning more accessible to users with limited resources.
By leveraging techniques like LoRA and QLoRA, you can fine-tune models using a single GPU and achieve significant reductions in computational and memory overhead. In this guide, we’ll explore how to fine-tune the Mistral 7B model using your own custom dataset with Axolotl and discuss the importance of data preparation, hyperparameters, and additional resources for faster and more efficient fine-tuning.
Source: https://www.theregister.com/2024/11/10/llm_finetuning_guide