When it comes to large language models, it is still the early innings. Many of them still hallucinate, fail to follow instructions, or generally don’t work. The only way to combat ...

In this Arxiv Dive, Nvidia researcher, Ethan He, presents his co-authored work Upcycling LLMs in Mixture of Experts (MoE). He goes into what a MoE is, the challenges behind upcycli...

The release of OpenAI-O1 has motivated a lot of people to think deeply about…thoughts 💭. Thinking before you speak is a skill that some people have better than others 😉, but a sk...

In the last blog, we went over prompting techniques 1-3 of The Prompt Report. This arXiv Dive, we were lucky to have the authors of the paper join us to go through some of the more...

For this blog we are switching it up a bit. In past Arxiv Dives, we have gone deep into the underlying model architectures and techniques that make large language models and other ...

Flux made quite a splash with its release on August 1st, 2024 as the new state of the art generative image model outperforming SDXL, SDXL-Turbo, Pixart, and DALL-E. While the model...

It only took about 11 minutes to fine-tuned Llama 3.1 8B on our political spam synthetic dataset using ReFT. While this is extremely fast, beating out our previous record of 14 min...

Meta has recently released Llama 3.1, including their 405 billion parameter model which is the most capable open model to date and the first open model on the same level as GPT 4. ...

Llama 3.1 is a set of Open Weights Foundation models released by Meta, which marks the first time an open model has caught up to GPT-4, Anthropic, or other closed models in the eco...

Synthetic data has shown promising results for training and fine tuning large models, such as Llama 3.1 and the models behind Apple Intelligence, and to produce datasets from minim...