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Jul
21
ArXiv Dives: How ReFT works

ArXiv Dives: How ReFT works

ArXiv Dives is a series of live meetups that take place on Fridays with the Oxen.ai community. We believe
10 min read
Jun
26
ArXiv Dives:💃 Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling

ArXiv Dives:💃 Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling

Modeling sequences with infinite context length is one of the dreams of Large Language models. Some LLMs such as Transformers
4 min read
Jun
04
ArXiv Dives: Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet

ArXiv Dives: Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet

The ability to interpret and steer large language models is an important topic as they become more and more a
9 min read
May
29
ArXiv Dives: Efficient DiT Fine-Tuning with PixART for Text to Image Generation

ArXiv Dives: Efficient DiT Fine-Tuning with PixART for Text to Image Generation

Diffusion Transformers have been gaining a lot of steam since OpenAI's demo of Sora back in March. The
8 min read
May
17
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
15 min read
Apr
29
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
16 min read
Apr
15
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
11 min read
Apr
08
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}
9 min read
Apr
01
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
10 min read
Mar
25
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
13 min read