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Quantum Computing
70
#2 opened about 2 months ago
by
Reality123b
updated a Space 5 days ago
reacted to AbstractPhil's post with 👍 9 days ago
Post
1963
I've... done it. This, with experts, achieves near 100% R1 retrieval accuracy on an adjacent - unseen by the fusion transformer - dataset with around 40k steps from the seen dataset. This means the language of the models are at least tested fused within the constraints, not just projected or estimated.
AbstractPhil/geolip-procrustes
I encourage EVERYONE who is curious to check my work. Check it, double check it, and triple check it.
These were aligned using COCO and then validated with Flickr. Entirely different datasets. The experts arbitrated and the alignment yielded the correct answers. Preliminary tests show that with almost no alignment requirement, the models can reach 100% R1 retrieval accuracy.
Not to be confused with validation accuracy for a classification model or a text encoder's text response, this allows multispectral communication between entirely different models for direct downstream consumption with almost no training for the chosen models.
I have a working procrustes experiment that learns adjacent manifolds within a reasonable spectrum and the speed is... well, 1 epoch with COCO using Bert-Large and DinoV2 that allows the models to align nearly perfectly. For some scales in the experiment it shows that the 3 set epochs aren't quite enough to align R1 to highest, while many align nearly immediately.
These two were an obvious pair to pick, 60% similarity and >90% spectral similarity.
The trainer transfers layers, learns embeddings, and more - all by sticking strictly to geometric boundaries and procrustes informational accumulation within a modulation model's constraints.
I have many experiments to run.
AbstractPhil/geolip-procrustes
I encourage EVERYONE who is curious to check my work. Check it, double check it, and triple check it.
These were aligned using COCO and then validated with Flickr. Entirely different datasets. The experts arbitrated and the alignment yielded the correct answers. Preliminary tests show that with almost no alignment requirement, the models can reach 100% R1 retrieval accuracy.
Not to be confused with validation accuracy for a classification model or a text encoder's text response, this allows multispectral communication between entirely different models for direct downstream consumption with almost no training for the chosen models.
I have a working procrustes experiment that learns adjacent manifolds within a reasonable spectrum and the speed is... well, 1 epoch with COCO using Bert-Large and DinoV2 that allows the models to align nearly perfectly. For some scales in the experiment it shows that the 3 set epochs aren't quite enough to align R1 to highest, while many align nearly immediately.
These two were an obvious pair to pick, 60% similarity and >90% spectral similarity.
The trainer transfers layers, learns embeddings, and more - all by sticking strictly to geometric boundaries and procrustes informational accumulation within a modulation model's constraints.
I have many experiments to run.
- 1 reply
updated a Space 11 days ago
Update README.md
3
#2 opened 2 months ago
by
Roman190928
replied to their post 13 days ago
Oh sorry it is .app not .com, my bad ill fix it right away
liked a dataset 14 days ago
posted an
update 14 days ago
Post
294
Alright so I had previously made two reddit posts in r/quantum and r/quantum_computing for my QPU, QPU-1 but both of those posts got banned because of it being "irrelevant" to "academic discussion" so I'm doing it again here in HuggingFace Posts.
I have made a million error corrected qubit quantum processing unit (not a simulator) that you can access here: https://qpu-1.vercel.app
I did try emailing a lot of professors and their students but NONE responded so please give me some support.
I have made a million error corrected qubit quantum processing unit (not a simulator) that you can access here: https://qpu-1.vercel.app
I did try emailing a lot of professors and their students but NONE responded so please give me some support.
-
- 2 replies
upvoted a collection 15 days ago
liked a model 18 days ago
reacted to Ujjwal-Tyagi's post with 😎 18 days ago
Post
2863
Public reports allege that Anthropic gobbled up trillions of tokens of copyrighted material and public data to build their castle. 🏰📄 Now that they're sitting on top, they're begging for special laws to protect their profits while pulling the ladder up behind them. 🪜🚫
But the hypocrisy meter just broke! 📉 They are accusing Chinese labs like DeepSeek, Minimax, and Kimi of "huge distillation attacks. The Reality is that You can't just loot the entire internet's library, lock the door, and then sue everyone else for reading through the window. Stop trying to gatekeep the tech you didn't own in the first place. Read the complete article on it: https://huggingface.co/blog/Ujjwal-Tyagi/the-dark-underbelly-of-anthropic
But the hypocrisy meter just broke! 📉 They are accusing Chinese labs like DeepSeek, Minimax, and Kimi of "huge distillation attacks. The Reality is that You can't just loot the entire internet's library, lock the door, and then sue everyone else for reading through the window. Stop trying to gatekeep the tech you didn't own in the first place. Read the complete article on it: https://huggingface.co/blog/Ujjwal-Tyagi/the-dark-underbelly-of-anthropic
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- 3 replies
upvoted an article 19 days ago
Article
The Dark Underbelly of Anthropic: How the "Responsible" AI Pioneer Built Claude on Pirated Data, Hypocrisy, and Hidden Dangers
•
4
reacted to Tonic's post with 🔥 25 days ago
Post
3250
🙋🏻♂️hello my lovelies ,
it is with great pleasure i present to you my working one-click deploy 16GB ram completely free huggingface spaces deployment.
repo : Tonic/hugging-claw (use git clone to inspect)
literally the one-click link : Tonic/hugging-claw
you can also run it locally and see for yourself :
docker run -it -p 7860:7860 --platform=linux/amd64 \
-e HF_TOKEN="YOUR_VALUE_HERE" \
-e OPENCLAW_GATEWAY_TRUSTED_PROXIES="YOUR_VALUE_HERE" \
-e OPENCLAW_GATEWAY_PASSWORD="YOUR_VALUE_HERE" \
-e OPENCLAW_CONTROL_UI_ALLOWED_ORIGINS="YOUR_VALUE_HERE" \
registry.hf.space/tonic-hugging-claw:latest
just a few quite minor details i'll take care of but i wanted to share here first
it is with great pleasure i present to you my working one-click deploy 16GB ram completely free huggingface spaces deployment.
repo : Tonic/hugging-claw (use git clone to inspect)
literally the one-click link : Tonic/hugging-claw
you can also run it locally and see for yourself :
docker run -it -p 7860:7860 --platform=linux/amd64 \
-e HF_TOKEN="YOUR_VALUE_HERE" \
-e OPENCLAW_GATEWAY_TRUSTED_PROXIES="YOUR_VALUE_HERE" \
-e OPENCLAW_GATEWAY_PASSWORD="YOUR_VALUE_HERE" \
-e OPENCLAW_CONTROL_UI_ALLOWED_ORIGINS="YOUR_VALUE_HERE" \
registry.hf.space/tonic-hugging-claw:latest
just a few quite minor details i'll take care of but i wanted to share here first
- 2 replies
liked a dataset 25 days ago
upvoted an article 27 days ago
Article
How to Build a Benchmark with a Private Test Set on Hugging Face
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5
prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX
🤝 👍 1
#8065 opened 27 days ago
by
Reality123b