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 🚀
Diffusion models have been popular for computer vision tasks. Recently models such as Sora show how you can apply Diffusion + Transformers to generate state of the art videos with ...
This paper presents BitNet b1.58 where every weight in a Transformer can be represented as a {-1, 0, 1} instead of a floating point number. The model matches full precision transfo...
Today, we’re diving into a fun paper by the team at Sakana.ai called “Evolutionary Optimization of Model Merging Recipes”. The high level idea is that we have so many open weights ...
Today, we’re diving into the I-JEPA paper. JEPA stands for Joint-Embedding Predictive Architecture and if you have been following Yann LeCunn, is a technique he has been hyping up ...
About a month ago we went over the "Self-Rewarding Language Models" paper by the team at Meta AI with the Oxen.ai Community. The paper felt very approachable and reproducible, so w...
Oxen.ai makes it quick and easy to download any version of your data wherever and whenever you need it. When we say quick, we mean raw speed. Oxen chunks and transfers data faster...
Oxen.ai makes it quick and easy to upload your datasets, keep track of every version and share them with your team or the world. Oxen datasets can be as small as a single csv or as...
Diffusion transformers achieve state-of-the-art quality generating images by replacing the commonly used U-Net backbone with a transformer that operates on latent patches. They rec...
This post is an effort to put together a reading list for our Friday paper club called ArXiv Dives. Since there has not been an official paper released yet for Sora, the goal is fo...
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 ...