Diving into the original Google paper which introduced the Mixture-of-Experts (MoE) method, which was critical to AI progress
Introduction to Mixture-of-Experts | Original MoE Paper Explained Read More »
In this post we explain the Mixture-of-Agents method, which shows a way to unite open-source LLMs to win GPT-4o on AlpacaEval 2.0
Mixture-of-Agents Enhances Large Language Model Capabilities Read More »
In this post we dive into Abacus Embeddings, which dramatically enhance Transformers arithmetic capabilities with strong logical extrapolation
Arithmetic Transformers with Abacus Positional Embeddings Read More »
In this post we dive into Consistency Large Language Models (CLLMs), a new family of models which can dramatically speedup LLMs inference!
CLLMs: Consistency Large Language Models Read More »
Learn about Representation Finetuning (ReFT) by Stanford University, a method to fine-tune large language models (LLMs) efficiently.
ReFT: Representation Finetuning for Language Models Read More »
What if we could discover OpenAI models internal weights? In this post we dive into a paper which presents an attack that steals LLMs data.
Stealing Part of a Production Language Model 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 »
In this post we dive into the Self-Rewarding Language Models paper by Meta AI, which can possibly be a step towards open-source AGI
Self-Rewarding Language Models by Meta AI Read More »
Diving into a research paper introducing an innovative method to enhance LLM inference efficiency using memory offloading
Fast Inference of Mixture-of-Experts Language Models with Offloading Read More »
In this post we dive into LLM in a flash paper by Apple, that introduces a method to run LLMs on devices that have limited memory
LLM in a flash: Efficient Large Language Model Inference with Limited Memory Read More »
