In this post we dive into a recent research paper which presents a promising novel direction for fine-tuning LLMs, achieving remarkable results when considering both parameters count and performance. Before diving in, if you prefer a video format then check out the following video: Motivation – Finetuning a Pre-trained Transformer is Expensive A common method […]
ReFT: Representation Finetuning for Language Models Read More »
Many of the top large language models today such as GPT-4, Claude 3 and Gemini are closed source, so a lot about the inner workings of these models is not known to the public. One justification for this is usually the competitive landscape, since companies are investing a lot of money and effort to create
Stealing Part of a Production Language Model Read More »
In this post, we dive into V-JEPA, which stands for Video Joint-Embedding Predicting Architecture, a new collection of vision models by Meta AI. V-JEPA is another step in Meta AI’s implementation of Yann LeCun’s vision about a more human-like AI. Several months back, we’ve already covered Meta AI’s I-JEPA model, which is a JEPA model
How Meta AI ‘s Human-Like V-JEPA Works? Read More »
In this post we dive into the era of 1-bit LLMs paper by Microsoft, which shows a promising direction for low cost large language models
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits Read More »
On January 18, Mark Zuckerberg announced that the long-term goal of Meta AI is to build general intelligence, and open-source it responsibly. So Meta AI is officially working on building an open-source AGI. On the same day, Meta AI have released a new research paper titled “Self-Rewarding Language Models”, which can be a step that
Self-Rewarding Language Models by Meta AI Read More »
In this post, we dive into a new research paper, titled: “Fast Inference of Mixture-of-Experts Language Models with Offloading”. Motivation LLMs Are Getting Larger In recent years, large language models are in charge of remarkable advances in AI, with models such as GPT-3 and 4 which are closed source and with open source models such
Fast Inference of Mixture-of-Experts Language Models with Offloading Read More »
In this post we dive into TinyGPT-V, a new multimodal large language model which was introduced in a research paper titled “TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones”. Before divining in, if you prefer a video format then check out our video review for this paper: Motivation In recent years we’ve seen a
TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones Read More »
In this post we dive into a new research paper from Apple titled: “LLM in a flash: Efficient Large Language Model Inference with Limited Memory”. Before divining in, if you prefer a video format then check out our video review for this paper: Motivation In recent years, we’ve seen a tremendous success of large language
LLM in a flash: Efficient Large Language Model Inference with Limited Memory Read More »
Up until vision transformers were invented, the dominating model architecture in computer vision was convolutional neural network (CNN), which was invented at 1989 by famous researchers including Yann LeCun and Yoshua Bengio. At 2017, transformers were invented by Google and took the natural language processing domain by storm, but were not adapted successfully to computer
How Do Vision Transformers Work? Read More »
Several months ago, Microsoft released the first version of Orca, which achieved remarkable results, even surpassing ChatGPT on data from BigBench-Hard dataset, and the ideas from Orca 1 helped to create better language models released in the recent period. The Orca 2 model, presented in the paper we review in this post, achieves significantly better
Orca 2: Teaching Small Language Models How to Reason Read More »
