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.
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Code
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DWM: Diffusion World Model
What’s New Meta’s Diffusion World Model (DWM) introduces a groundbreaking approach to predict multistep future states and rewards concurrently, outperforming
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Build a Large Language Model (From Scratch)
“Build a Large Language Model (From Scratch)” by Sebastian Raschka blew up on Github this week and collected over 5000
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Direct Preference Optimization instead of RLHF
Can you still do cutting-edge research on LLM if you do not have massive compute resources? RLHF became a key
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LLM course in notebook
The “Large Language Model Course” blew up on Github this week and collected over 9000 stars. It’s a course on
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Checkpointing for Memory Efficiency
In PyTorch, torch.utils.checkpoint reduces GPU memory use by segmenting large models during training. It stores only one segment at a time in
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Sequential Modeling in Large Vision Models
The authors introduce a visual language model (LVM) without making use of any linguistic data trained on 1.64 billion unlabeled
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8 Microsoft free Github Courses
In this article, we are going to explore 8 different Microsoft Github hosted courses for machine learning and AI. You
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I-JEPA: Help vision models relate Details to the whole image
A novel twist on self-supervised learning aims to improve on earlier methods by helping vision models learn how parts of
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System 2 Attention by Meta
What’s New The research introduces System 2 Attention (S2A) in Large Language Models (LLMs) to address issues with soft attention
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