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NJX-njxย 
posted an update 1 day ago
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3793
Recently, I have open-sourced an AI emotional companion product based on openclaw, called opensoul.

On this platform, you can create a "soulmate" that matches your personality, and configure it with the skills, tools you want it to have, as well as the platforms it can integrate with (such as Telegram, Discord, etc.).
You can even create group chats, invite multiple agents and your friends to chat about recent events, discuss projects together, and so on.

On the one hand, I hope it can better accompany you in daily life by virtue of its unique memory mechanism, self-feedback and iteration mechanism, and the modeling of users' emotions. On the other hand, I also hope it can help you better handle your work with its unique skills, tools and ability to deal with complex task scenarios.

Although the entire product has taken shape, I think there are still many areas that need adjustment and optimization. I also hope to rely on the strength of the community to do a good job in AI emotional companionship.

This is the project introduction URL: https://opensoul-web.vercel.app
This is the GitHub project URL: https://github.com/NJX-njx/opensoul
@AdinaY @lilianweng@burtenshaw@clem
let's just do it

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etemizย 
posted an update 2 days ago
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5011
AHA 2026 scores of Qwen3.5

27B Huihui abliteration 65%
27B Heretic abliteration 55%
27B Normal 50%

35B Huihui abliteration 64%
35B @jiaojjjjje abliteration 57%
35B @LeadFootThrottleCock abliteration 56%
  • 6 replies
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ajibawa-2023ย 
posted an update 3 days ago
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3144
Python-Code-Large
Dataset: ajibawa-2023/Python-Code-Large

Python-Code-Large is a large-scale corpus of Python source code comprising more than 2 million rows of Python code. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and program analysis for the Python ecosystem.

By providing a high-volume, language-specific corpus, Python-Code-Large enables systematic experimentation in Python-focused model training, domain adaptation, and downstream code understanding tasks.

Python-Code-Large addresses the need for a dedicated Python-only dataset at substantial scale, enabling focused research across data science, backend systems, automation, scientific computing, and AI-driven Python environments.
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DavidAUย 
posted an update 2 days ago
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2447
Gemma 3 27B - The record breaker (Heretic'ed (uncensored) ; then training on Unsloth):

arc_challenge,arc_easy,boolq,hellaswag,openbookqa,piqa, winogrande
0.661 ,0.816 ,0.878,0.763 ,0.464 ,0.808 ,0.762

For comparison:
Qwen3.5-27B-Text
qx86-hi 0.443,0.498,0.857,0.701,0.372,0.770,0.752

Trained on a HERETIC uncensored base too ;

DavidAU/Gemma3-27B-it-vl-Polaris-HI16-Heretic-Uncensored-INSTRUCT
SeaWolf-AIย 
posted an update 3 days ago
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2613
AI Is Training on Your Content Without Permission โ€” Fight Back with Invisible Watermarks

FINAL-Bench/security-scan

Most generative AI training data is crawled without consent. Your text gets summarized, images reprocessed, videos clipped โ€” with no way to prove you're the original creator. Existing watermarks are either visible or wiped out by a single AI preprocessing pass.

Detect Before, Track After

Pre-embed โ€” Detect theft without any watermark. Text plagiarism check, image similarity analysis (perceptual hash, SSIM, color histogram, feature matching), and video temporal matching catch copies, edits, and excerpts.

Post-embed โ€” Embed invisible multi-layer watermarks. If one layer is destroyed, others survive independently. Even full removal leaves forensic traces as evidence.

Text: 4 Independent Layers

Four mechanisms work simultaneously: zero-width Unicode characters at morpheme/word boundaries (Korean Kiwi + English NLP), style fingerprinting via synonym-ending-connective substitution, SHA-256 timestamped evidence packages, and punctuation-anchored micro-marks. Each layer uses a different Unicode category, so attacks on one cannot eliminate the others. Full bilingual support, zero readability impact.

34-Attack Defense

7 categories, 34 attacks simulated: Unicode normalization, invisible character removal, homoglyph substitution (9,619 confusables), and AI rewriting. Each scored on Signal (watermark survival) + Trace (forensic evidence of attack) โ€” proving deliberate removal even when watermarks are destroyed.

Image & Video

Images: DCT frequency-domain watermarks surviving JPEG compression and resize. Videos: keyframe watermarking with temporal propagation and majority-vote extraction. Both support pre-embed similarity detection.

Who Is This For

Creators, rights holders needing legal evidence, media companies, and organizations tracking document leaks. Korean/English bilingual, open source, Gradio-based.
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OzTianluย 
posted an update about 23 hours ago
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1062
๐Ÿ”ฅ UPGRADE in Kai: 30B Scaling! ๐Ÿ”ฅ
NoesisLab/Kai-30B-Instruct
NoesisLab/Kai-30B-Instruct
We are incredibly excited to announce that the Kai-30B-Instruct model and its official Space are now LIVE! ๐Ÿš€
If you've been following the journey from Kai-0.35B to Kai-3B, you know we're rethinking how models reason. Tired of verbose, slow Chain-of-Thought (CoT) outputs that flood your screen with self-talk? So are we.
Kai-30B-Instruct scales up our Adaptive Dual-Search Distillation (ADS) framework. By bridging classical A* heuristic search with continuous gradient descent , we use an information-theoretic log-barrier to physically prune high-entropy reasoning paths during training.
The result? Pure implicit reasoning. The model executes structured logic, arithmetic carries, and branch selections as a reflex in a single forward passโ€”no external scaffolding required.
At 3B, we observed a phase transition where the model achieved "logical crystallization". Now, at 30B, we are giving the ADS regularizer the massive representational capacity it needs to tackle higher-order symbolic abstractions and complex reasoning tasks.
๐Ÿงช Test Kai yourself in our new Space:
NoesisLab/Kai-30B-Instruct
๐Ÿ“ฆ Model Weights:
NoesisLab/Kai-30B-Instruct
Bring your hardest math, logic, and coding benchmarks. We invite the community to stress-test the limits of the penalty wall! ๐Ÿงฑ๐Ÿ’ฅ
imnotkittyย 
posted an update 3 days ago
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1301
In the Text-to-Video arena, Seedance 2.0 has first secured a spot in the LMArena Top 10, while Kling 3.0 has topped the Artificial Analysis leaderboard, with the Kling family claiming 7 spots in the top 15.

Which one do you prefer?
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hannayukhymenkoย 
posted an update about 14 hours ago
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149
Do you translate your benchmarks from English correctly? ๐Ÿค”
Turns out, for many languages it is much harder than you can imagine!

Introducing Recovered in Translation ๐ŸŒ together with @aalexandrov
ritranslation.insait.ai

Translating benchmarks is a painful process, requiring a lot of manual inspection and adjustments. You start from setting up the whole pipeline and adapting to every format type, including task specifics. There already exist some massive benchmarks, but they still have some simple (and sometimes silly) bugs, which can hurt the evaluations :( We present a novel automated translation framework to help with that!

Eastern and Southern European languages introduce richer linguistic structures compared to English and for benchmarks which heavily rely on grammatical coherence machine translation presents a risk of harming evaluations. We discover potential answer leakage or misleading through grammatical structure of the questions. Some benchmarks are also just outdated and need to be retranslated with newer and better models.

We present a framework with novel test-time scaling methods which allow to control time and cost investments, while at the same time mitigate the need for human-in-the-loop verification. While working on Ukrainian-focused MamayLM models, we had to translate 10+ benchmarks in a short span of time. Finding human evaluators is costly and time-consuming, same goes for using professional translators. With our pipeline we were able to do it in 3 days๐ŸŽ๏ธ

We hope our findings will help enable stronger multilingual evaluations and developments. We release all produced benchmarks on Hugging Face together with the source code and Arxiv paper ๐Ÿค—

Paper: Recovered in Translation: Efficient Pipeline for Automated Translation of Benchmarks and Datasets (2602.22207)
Code: https://github.com/insait-institute/ritranslation
Benchmarks: https://huggingface.co/collections/INSAIT-Institute/multilingual-benchmarks
ukosem7ย 
posted an update about 23 hours ago
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127
Hey everyone ๐Ÿ‘‹

We just launched the ** [RMBG-2.0 API Sandbox](https://catalog.bria.ai/image-editing/remove-background/sandbox) ** โ€” a new way to test our background removal API directly in your browser, no setup required.

### What just launched

The sandbox is a live, interactive playground for the RMBG-2.0 API. You can:

- **Upload any image** and see background removal results instantly
- **Get the exact API call** โ€” copy the request and drop it into your codebase
- **No signup required** to try it โ€” just open the link and upload

** [โ†’ Open the Sandbox](https://catalog.bria.ai/image-editing/remove-background/sandbox) **

### Why we built this

RMBG has been downloaded over **5 million times** across 1.4 and 2.0. We kept hearing the same thing from developers shipping to production: *"The model is great, but I need a managed endpoint โ€” I don't want to provision GPUs and manage inference infrastructure."*

The sandbox is the front door to that. Test the quality, grab the API call, get a token, and you're in production.

### Nothing changes for open-source users

The HuggingFace weights, the code, the Spaces demo โ€” all still here, still free for non-commercial use. This is an addition, not a replacement.

** [โ†’ Try the new sandbox](https://catalog.bria.ai/image-editing/remove-background/sandbox) **
Reality123bย 
posted an update 1 day ago
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172
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.
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