Train Your Own O1 Preview Model Within $450

Also, at $450 no one expect it to truly be a from-scratch complete recreation of a model that cost hundreds of millions to produce.

Instead, they built a model (via fine tuning) using similar technique and got similar results within their attempted are of experimentation that they created their training data for.

I personally was not mislead by the title at all.

There are open source models better than OpenAI's image and video models, and OpenAI is not winning the LLM space by any measure.

The hobbyist absolutely won't feel as though they're trying to fake a Huracan with a Camry here. They're going to build useful products with whatever they choose, regardless of what vendor or open source project produced the model.

Your analogy is silly. OpenAI is more like Band-Aid(r) than Lamborghini Huracan.

I think we can all agree there does need to be an update. You don't want to forever outlaw deep learning (even if you do want to, that's not going to happen so it's worth helping to shape the future)

It's very complicated with a bunch of moving parts but I really want society to start arguing about it so we can get to a semi-fair place

You weren't going to buy a book instead of asking a question.

$25 to Elsevier per GPU purchase

I do share your opinion. Others may argue "What about x country? They don't care!", even though that position is about as good as making anything excusable because someone else did it.

I might add, I'm really not trying to be toxic. Just saying this based on what I see when this comes up.

Never mind that you've just handed control of an incredibly-powerful tool over to nations that DGAF about copyright law.

If copyright interests want to fight AI, then copyright has to go. It's that simple. It's an unnecessary fight, but somebody needs to convince them of that.

> Language model post-training is applied to refine behaviors and unlock new skills across a wide range of language models, but open recipes for applying these techniques lag behind proprietary ones. The underlying training data and recipes for post-training are simultaneously the most important pieces of the puzzle and the portion with the least transparency. To bridge this gap, we introduce Tülu 3, a family of fully-open state-of-the-art post-trained models, alongside its data, code, and training recipes, serving as a comprehensive guide for modern post-training techniques. Tülu 3, which builds on Llama 3.1 base models, achieves results surpassing the instruct versions of Llama 3.1, Qwen 2.5, Mistral, and even closed models such as GPT-4o-mini and Claude 3.5-Haiku. The training algorithms for our models include supervised finetuning (SFT), Direct Preference Optimization (DPO), and a novel method we call Reinforcement Learning with Verifiable Rewards (RLVR). With Tülu 3, we build a multi-task evaluation scheme for post-training with development and unseen evaluations, standard benchmark implementations, and substantial decontamination of existing open datasets on said benchmarks. We conclude with analysis and discussion of training methods that did not reliably improve performance. The Tülu 3 release includes model weights, a demo, and the complete recipe — datasets for diverse core skills, a robust toolkit for data curation and evaluation, the training code and infrastructure, and, most importantly, a detailed report for reproducing and further adapting the Tülu 3 approach to more domains.

https://arxiv.org/pdf/2411.15124