QLoRA: efficiently LLM Fine-Tuning
Parameter-efficient training (PEFT) techniques offer a way to fine-tune large language models (LLMs) on custom datasets with minimal computational resources.
Read MoreParameter-efficient training (PEFT) techniques offer a way to fine-tune large language models (LLMs) on custom datasets with minimal computational resources.
Read MoreWant to understand Language Models? Stanford CS25, titled “Transformers United,” is a comprehensive lecture series available on YouTube, presented by
Read MoreGoogle’s Gemini Ultra, set for release this Wednesday according to a leaked document, targeting it as a direct competitor to
Read More“Build a Large Language Model (From Scratch)” by Sebastian Raschka blew up on Github this week and collected over 5000
Read MoreThe “Large Language Model Course” blew up on Github this week and collected over 9000 stars. It’s a course on
Read MoreIn PyTorch, torch.utils.checkpoint reduces GPU memory use by segmenting large models during training. It stores only one segment at a time in
Read MoreThe authors introduce a visual language model (LVM) without making use of any linguistic data trained on 1.64 billion unlabeled
Read MoreIn this article, we are going to explore 8 different Microsoft Github hosted courses for machine learning and AI. You
Read MoreThe COLMAP-Free 3D Gaussian Splatting (CF-3DGS) framework efficiently synthesizes photo-realistic views without needing Structure-from-Motion (SfM) pre-processing, significantly reducing training time
Read MoreA novel twist on self-supervised learning aims to improve on earlier methods by helping vision models learn how parts of
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