Fine-tuning in machine learning is the process of adapting a pre-trained model for specific tasks or use cases through further training on a smaller dataset.
Instruction tuning is a subset of the broader category of fine-tuning techniques used to adapt pre-trained foundation models for downstream tasks.Foundation modelscan be fine-tuned for a variety of purposes, from style customization to supplementing the core knowledge and vocabulary of the pre-traine...
perform “fine-tuning” or LoRA on models like stable diffusion, and end up with a model that is capable of producing arbitrary images in the “style” of the target artist, when evoked with their name
Another technique,LoRAPrune, combines low-rank adaptation (LoRA) with pruning to enhance the performance of LLMs on downstream tasks.LoRAis a parameter-efficient fine-tuning (PEFT) technique that only updates a small subset of the parameters of a foundational model. This makes it a highly effici...
To make that more efficient, we focus only on the parts of the model that need to improve by using a technique we developed in Microsoft Research called Low Rank Adaptive, or LoRA fine-tuning. And then only that part of the model is updated with changes. With...
(AU) intensity plays a pivotal role in quantifying fine-grained expression behaviors, which is an effective condition for facial expression manipulation. In the paper“AUEditNet: Dual-Branch Facial Action Unit Intensity Manipulation with Implicit Disentanglement”the team achieved accurate AU...
Performance improvements for inference –Traditional methods of fine-tuning involve adding custom layers to the pre-trained model to fit the task at hand. These adapter layers, or external modules, are typically added in a sequential manner, which introduces inference latency. LoRA differs from these...
LAB: Large-Scale Alignment for ChatBotsis a novel approach to instruction alignment and fine tuning of large language models with a taxonomy-driven approach leveraging high-quality synthetic data generation. In simpler terms, it allows for users to customize an LLM with domain-specific knowledge and...
Accessibility.Hugging Face helps users bypass restrictive compute and skill requirements typical of AI development. The fact that Hugging Face provides pre-trained models, fine-tuning scripts and APIs for deployment makes the process of creating LLMs easier. ...
One of the most promising areas of research is fine-tuning a big model for a specific use case without needing to run the entire model. If you made a thousand versions of an LLM, that’s good at a thousand different things, and you have to load each of those into the GPUs and serve...