Feb
26
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
Greg Schoeninger
Feb
19
ArXiv Dives - Depth Anything
This paper presents Depth Anything, a highly practical solution for robust monocular depth estimation. Depth estimation traditionally requires extra hardware
Feb
12
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
Feb
05
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”
Jan
29
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 "
Jan
20
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
Jan
13
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
Jan
07
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
Jan
06
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
Dec
23
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