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Coconut by Meta AI – Better LLM Reasoning With Chain of CONTINUOUS Thought?
Discover how Meta AI’s Chain of Continuous Thought (Coconut) empowers large language models (LLMs) to reason in their own language…
Hymba by NVIDIA: A Hybrid Mamba-Transformer Language Model
Discover NVIDIA’s Hymba model that combines Transformers and State Space Models for state-of-the-art performance in small language models…
LLaMA-Mesh by Nvidia: LLM for 3D Mesh Generation
Dive into Nvidia’s LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models, a LLM which was adapted to understand 3D objects…
Tokenformer: Rethinking Transformer Scaling with Tokenized Model Parameters
Dive into Tokenformer, a novel architecture that improves Transformers to support incremental model growth without training from scratch…
Generative Reward Models: Merging the Power of RLHF and RLAIF for Smarter AI
In this post we dive into a Stanford research presenting Generative Reward Models, a hybrid Human and AI RL to improve LLMs…
Sapiens by Meta AI: Foundation for Human Vision Models
In this post we dive into Sapiens, a new family of computer vision models by Meta AI that show remarkable advancement in human-centric tasks!…
Mixture of Nested Experts: Adaptive Processing of Visual Tokens
In this post we dive into Mixture of Nested Experts, a new method presented by Google that can dramatically reduce AI computational cost…
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…
Self-Rewarding Language Models by Meta AI
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…
Fast Inference of Mixture-of-Experts Language Models with Offloading
Diving into a research paper introducing an innovative method to enhance LLM inference efficiency using memory offloading…
TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones
In this post we dive into TinyGPT-V, a small but mighty Multimodal LLM which brings Phi-2 success to vision-language tasks…