ArXiv Dives - Lumiere
This paper introduces Lumiere – a text-to-video diffusion model designed for synthesizing videos that portray realistic, diverse and coherent motion – a
ArXiv Dives - Depth Anything
This paper presents Depth Anything, a highly practical solution for robust monocular depth estimation. Depth estimation traditionally requires extra hardware
Arxiv Dives - Toolformer: Language models can teach themselves to use tools
Large Language Models (LLMs) show remarkable capabilities to solve new tasks from a few textual instructions, but they also paradoxically
Arxiv Dives - Self-Rewarding Language Models
The goal of this paper is to see if we can create a self-improving feedback loop to achieve “superhuman agents”
Arxiv Dives - Direct Preference Optimization (DPO)
This paper provides a simple and stable alternative to RLHF for aligning Large Language Models with human preferences called "
Arxiv Dives - Efficient Streaming Language Models with Attention Sinks
This paper introduces the concept of an Attention Sink which helps Large Language Models (LLMs) maintain the coherence of text
Arxiv Dives - How Mixture of Experts works with Mixtral 8x7B
Mixtral 8x7B is an open source mixture of experts large language model released by the team at Mistral.ai that
Arxiv Dives - LLaVA 🌋 an open source Large Multimodal Model (LMM)
What is LLaVA?
LLaVA is a Multi-Modal model that connects a Vision Encoder and an LLM for general purpose visual
Practical ML Dive - Building RAG from Open Source Pt 1
RAG was introduced by the Facebook AI Research (FAIR) team in May of 2020 as an end-to-end way to include
Arxiv Dives - How Mistral 7B works
What is Mistral 7B?
Mistral 7B is an open weights large language model by Mistral.ai that was build for
