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Introduction to Mixture-of-Experts | Original MoE Paper Explained
Diving into the original Google paper which introduced the Mixture-of-Experts (MoE) method, which was critical to AI progress…
Mixture-of-Agents Enhances Large Language Model Capabilities
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…
Arithmetic Transformers with Abacus Positional Embeddings
In this post we dive into Abacus Embeddings, which dramatically enhance Transformers arithmetic capabilities with strong logical extrapolation…
CLLMs: Consistency Large Language Models
In this post we dive into Consistency Large Language Models (CLLMs), a new family of models which can dramatically speedup LLMs inference!…
ReFT: Representation Finetuning for Language Models
Learn about Representation Finetuning (ReFT) by Stanford University, a method to fine-tune large language models (LLMs) efficiently…
Stealing Part of a Production Language Model
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…
How Meta AI ‘s Human-Like V-JEPA Works?
Explore V-JEPA, which stands for Video Joint-Embedding Predicting Architecture. Another step in Meta AI’s journey for human-like AI…
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
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…