Want more “good machine learning” in your X feed? Here is a new Space for you:
🔔 Top HF Users To Follow On X - https://huggingface.co/spaces/mvaloatto/HF2X

Ever since I fell down the AI rabbit hole, it hasn’t been super easy to spot and follow the most impactful Hugging Face contributors on X. So, inspired by @Weyaxi leaderboards, I decided to create a list just for this purpose.

Why, you ask?

First, it’s quite surprising how so many talented AI pioneers and independent contributors on X don't get the visibility/reach you might expect. Sad but true: follower count doesn't always match up with the value or innovation an individual brings to the table (just stating the obvious here).

Open source AI, in particular, thrives not just on innovation but also on the collective spirit of its believers and builders. With Hugging Face standing out as a prime hub for top AI engineers and contributors, compiling a directory of X profiles from influential figures on this platform felt like a natural step.

This Space aims to not only connect these top contributors but also guide open AI enthusiasts and newcomers towards the field's leading lights.

I put this modest page together using some web scraping and what I remember from my web dev class ages ago! Suggestions/likes are welcome - I’m hoping to keep tweaking/upgrading it, especially if you all find it useful.

Now, let’s follow each other! It’s time to accelerate the dissemination of our ideas, encourage collaboration within our community, and ensure that open AI developments receive the attention and recognition they deserve. 🔥

9 replies

reacted to clem's post with ❤️ 12 months ago

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Terribly excited about open-source + on-device AI these days! Great to see @qualcomm release 80+ models optimized and curated for their devices and chips on HF: https://huggingface.co/qualcomm

1 reply

reacted to vladbogo's post with 👍 12 months ago

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Genie is a new method from Google DeepMind that generates interactive, action-controllable virtual worlds from unlabelled internet videos using.

Keypoints:
* Genie leverages a spatiotemporal video tokenizer, an autoregressive dynamics model, and a latent action model to generate controllable video environments.
* The model is trained on video data alone, without requiring action labels, using unsupervised learning to infer latent actions between frames.
* The method restricts the size of the action vocabulary to 8 to ensure that the number of possible latent actions remains small.
* The dataset used for training is generated by filtering publicly available internet videos with specific criteria related to 2D platformer games for a total of 6.8M videos used for training.

Paper: Genie: Generative Interactive Environments (2402.15391)
Project page: https://sites.google.com/view/genie-2024/
More detailed overview in my blog: https://huggingface.co/blog/vladbogo/genie-generative-interactive-environments

Congrats to the authors for their work!

3 replies

reacted to merve's post with 👍 12 months ago

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I've tried DoRA (https://arxiv.org/abs/2402.09353) with SDXL using PEFT, outputs are quite detailed 🤩🌟
as usual trained on lego dataset I compiled, I compared them with previously trained pivotal tuned model and the normal DreamBooth model before that 😊

Notebook by @linoyts https://colab.research.google.com/drive/134mt7bCMKtCYyYzETfEGKXT1J6J50ydT?usp=sharing
Integration to PEFT by @BenjaminB https://github.com/huggingface/peft/pull/1474 (more info in the PR)