Last week I released a CLI that can do this at scale: https://github.com/radi-cho/datasetGPT. Will use personal funds to generate somewhat big task oriented dataset later today with gpt-3.5 or gpt-4. Will open source it along a way for people to contribute their own datasets so we can collect bigger ones. Would be helpful both for analysis of how LLMs work and for fine tuning downstream models (Alpaca-like).

For people looking for open-source tools around the GPT-4 API, we're currently actively updating the list at https://github.com/radi-cho/awesome-gpt4. Feel free to check it out or contribute if you're a tool developer. I guess some of the ChatGPT plugins will be open-source as well.

Great. Congratulations. I was planning on attempting the same basically, so thanks for open-sourcing it:)

Thanks for the suggestion:)

Stable Diffusion conditioned with Controlnet-Scribble or Controlnet-canny. For the editing option, instruct-pix2pix.

Paper: https://arxiv.org/abs/2303.01500
Code: https://github.com/facebookresearch/dropout

More models will be added continuously. Depending on Apple's review timing and app usage, the iOS version might be coming in 1-2 weeks.

The app: https://play.google.com/store/apps/details?id=com.radicho.diffground

Much more models and an iOS version are coming soon:)

Paper: https://arxiv.org/pdf/2302.12251.pdf GitHub: https://github.com/nvlabs/voxformer

Abstract: Humans can easily imagine the complete 3D geometry of occluded objects and scenes. This appealing ability is vital for recognition and understanding. To enable such capability in AI systems, we propose VoxFormer, a Transformer-based semantic scene completion framework that can output complete 3D volumetric semantics from only 2D images. Our framework adopts a two-stage design where we start from a sparse set of visible and occupied voxel queries from depth estimation, followed by a densification stage that generates dense 3D voxels from the sparse ones. A key idea of this design is that the visual features on 2D images correspond only to the visible scene structures rather than the occluded or empty spaces. Therefore, starting with the featurization and prediction of the visible structures is more reliable. Once we obtain the set of sparse queries, we apply a masked autoencoder design to propagate the information to all the voxels by self-attention. Experiments on SemanticKITTI show that VoxFormer outperforms the state of the art with a relative improvement of 20.0% in geometry and 18.1% in semantics and reduces GPU memory during training by ~45% to less than 16GB.

About the intuition that it would produce responses further from the human ones (in fact, we see that for this variant, the BLEU is lower) - in a way, it could work as a regularization to produce more diverse responses and prevent some overfitting. That loss mostly affects the additional head's weights which are removed during inference, but we also multiply it by an optimal constant to be sure it doesn't affect the whole architecture too much. I've sent you a PM if you wish to receive some more details or empirical insights.

Thanks for the interest! You can follow me on Twitter: https://twitter.com/radi_cho

Just linked it in a top-level comment.

Paper: https://aclanthology.org/2022.icnlsp-1.2/
GitHub: https://github.com/radi-cho/RSTOD

DALL-E was disrupted by Stable Diffusion, can OpenAssistant disrupt ChatGPT in your opinion?

Paper: https://arxiv.org/abs/2302.04761

Implementation by lucidrains (in progress): https://github.com/lucidrains/toolformer-pytorch