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 🚀

ArXiv Dives: Evaluating LLMs for Code Completion with HumanEval
ArXiv Dives: Evaluating LLMs for Code Completion with HumanEval

Large Language Models have shown very good ability to generalize within a distribution, and frontier models have shown incredible flexibility under prompting. Now that there is so...

Alex owen
Alex owen
May 17, 2024
15 min read
How to Train Diffusion for Text from  Scratch
How to Train Diffusion for Text from Scratch

This is part two of a series on Diffusion for Text with Score Entropy Discrete Diffusion (SEDD) models. Today we will be diving into the code for diffusion models for text, and see...

Greg Schoeninger
Greg Schoeninger
Apr 30, 2024
- Arxiv Dives
16 min read
ArXiv Dives: Text Diffusion with SEDD
ArXiv Dives: Text Diffusion with SEDD

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 ...

Greg Schoeninger
Greg Schoeninger
Apr 16, 2024
- Arxiv Dives
11 min read
ArXiv Dives: The Era of 1-bit LLMs, All Large Language Models are in 1.58 Bits
ArXiv Dives: The Era of 1-bit LLMs, All Large Language Models are in 1.58 Bits

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...

Greg Schoeninger
Greg Schoeninger
Apr 8, 2024
- Arxiv Dives
9 min read
ArXiv Dives: Evolutionary Optimization of Model Merging Recipes
ArXiv Dives: Evolutionary Optimization of Model Merging Recipes

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 ...

Greg Schoeninger
Greg Schoeninger
Apr 1, 2024
- Arxiv Dives
10 min read
ArXiv Dives: I-JEPA
ArXiv Dives: I-JEPA

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 ...

Greg Schoeninger
Greg Schoeninger
Mar 26, 2024
- Arxiv Dives
13 min read
How to train Mistral 7B as a "Self-Rewarding Language Model"
How to train Mistral 7B as a "Self-Rewarding Language Model"

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...

Greg Schoeninger
Greg Schoeninger
Mar 20, 2024
- Practical ML
17 min read
Downloading Datasets with Oxen.ai
Downloading Datasets with Oxen.ai

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...

Greg Schoeninger
Greg Schoeninger
Mar 18, 2024
- Getting Started
4 min read
Uploading Datasets to Oxen.ai
Uploading Datasets to Oxen.ai

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...

Greg Schoeninger
Greg Schoeninger
Mar 18, 2024
- Getting Started
4 min read
ArXiv Dives - Diffusion Transformers
ArXiv Dives - Diffusion Transformers

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...

Greg Schoeninger
Greg Schoeninger
Mar 12, 2024
- Arxiv Dives
14 min read