Oxen.ai Blog

Welcome to the Oxen.ai blog 🐂

The team at Oxen.ai is dedicated to helping AI practictioners go from research to production. To help enable this, we host a research paper club on Fridays called ArXiv Dives, where we go over state of the art research and how you can apply it to your own work.

Take a look at our Arxiv Dives, Practical ML Dives as well as a treasure trove of content on how to go from raw datasets to production ready AI/ML systems. We cover everything from prompt engineering, fine-tuning, computer vision, natural language understanding, generative ai, data engineering, to best practices when versioning your data. So, dive in and explore – we're excited to share our journey and learnings with you 🚀

All Arxiv Dives Getting Started Practical ML

ArXiv Dives - Medusa

Abstract In this paper, they present MEDUSA, an efficient method that augments LLM inference by adding extra decoding heads to predict multiple subsequent tokens in parallel. The ...

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 pivotal challenge in video ...

ArXiv Dives - Depth Anything

This paper presents Depth Anything, a highly practical solution for robust monocular depth estimation. Depth estimation traditionally requires extra hardware and algorithms such as...

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 struggle with basic functionality such as ...

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”. Current language models are bottlenecked by labeled data from human...

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 "Direct Preference Optimization" (DPO). They reformulat...

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 into the millions of tokens while also maintaining a ...

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 outperforms Llama-2 70B and GPT-3.5 on a variety natural language und...

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 and language understanding. Paper: https://arxiv.org/abs/2304.084...

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 document search into a sequence-to-sequence neural network architecture. ...