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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…
LLM in a flash: Efficient Large Language Model Inference with Limited Memory
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…