In this article, we will learn how to make predictions using the 4-bit quantized 🤗 Idefics-9B model and fine-tune it on a specific dataset.
In this article, we review the paper on MambaOut and explore the nature of Mamba. We will delve into the potential of MambaOut and test the model using Paperspace.
In this article we will explore an open-access version of Deepmind's visual language model, IDEFICS2. In this article, we'll show you how to use the IDEFICS model for image-text tasks.
In this tutorial we will explore Google's vision-model PaliGemma. A lightweight, open model designed for easy fine-tuning, training and deploying.
In this article, we'll explore a recently introduced ML algorithm and examine whether Kolmogorov-Arnold Networks have the potential to replace Multi-layer Perceptrons.
In this article we introduce pyreft, a novel fine-tuning method called Representation Fine-Tuning (ReFT), which offers superior efficiency and interpretability compared to state-of-the-art methods like PEFTs.
In this tutorial, we discuss the new IDM-VTON application, discuss some improvements we have added with Grounded Segment Anything, and show off some examples of the models potential.
In this short tutorial, we will learn how to prepare a balanced dataset that can be used to train a large language model (LLM).
In this article, we will understand how to fine-tune Llama3 using the Llama Index. Moreover, one of the best parts is that you can achieve that with very few easy steps and just few lines of code.
