community-events VS gpt-2

Best applications that will come out of this sprint will receive prizes. You can find more information here. If you want to get started, simply join huggingface.co/discord, take the role 🧨 Diffusers and head to #jax-diffusers-ideas to share your idea or join one of the teams, and fill this form: https://forms.gle/t3M7aNPuLL9V1sfa9

It will start on 17th of April. To join us, you can join huggingface.co/join/discord and take the role Diffusers from #role-assignment. After this, simply fill the form provided in this guide to later get access to TPUs. https://github.com/huggingface/community-events/tree/main/jax-controlnet-sprint

Details and sign-up: https://github.com/huggingface/community-events/tree/main/jax-controlnet-sprint

For the training, I extremely recommend checking out the Whisper Fine-Tuning Event. It has a python script to train in one command, tons of tips, even a walkthrough video.

We are happy to invite you to the Gradio Blocks Party - a community event in which we will create interactive demos for state-of-the-art machine learning models. Demos are powerful because they allow anyone — not just ML engineers — to try out models in the browser, give feedback on predictions, identify trustworthy models. The event will take place from May 17th to 31st. We will be organizing this event on Github and the Hugging Face discord channel. Prizes will be given at the end of the event, see the Prizes section

If you're interested in generative models, Hugging Face is putting on an event around generative models right now called the HugGAN sprint, where they're giving away free access to compute to train models like this.

You can join it by following the steps in the guide here: https://github.com/huggingface/community-events/tree/main/hu...

There will also be talks from awesome folks at EleutherAI, Google, and Deepmind

Medium models: Roughly between 1B to 10B parameters. This is where Mistral 7B, Phi-3, Gemma from Google DeepMind, and wizardlm2 sit. Fun fact: GPT 2 was a medium sized model, much smaller than its latest versions.

"Our model, called GPT-2 (a successor to GPT), was trained simply to predict the next word in 40GB of Internet text. Due to our concerns about malicious applications of the technology, we are not releasing the trained model. As an experiment in responsible disclosure, we are instead releasing a much smaller model for researchers to experiment with, as well as a technical paper. "

They did release GPT-2 under the MIT License.

Did you arrive at this certainty through reading something other than what OpenAI has published? The document [0] that describes the training data for GPT-2 makes this assertion hilarious to me.

[0]: https://github.com/openai/gpt-2/blob/master/model_card.md#da...

I made a clone of https://github.com/openai/gpt-2 on my local laptop