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
Qwen 3.5 Model is here! Supporting 1m context length by default, It is giving much good performance and competitive to Claude Opus 4.6, Qwen/Qwen3.5-397B-A17B, here it's GGUF: unsloth/Qwen3.5-397B-A17B-GGUF, Follow me and turn on the notification for the latest news!
Introducing Seekify — a truly non‑rate‑limiting search library for Python
Tired of hitting rate limits when building search features? I’ve built Seekify, a lightweight Python library that lets you perform searches without the usual throttling headaches.
🔹 Key highlights
- Simple API — plug it in and start searching instantly
- No rate‑limiting restrictions
- Designed for developers who need reliable search in projects, scripts, or apps
📦 Available now on PyPI:
pip install seekify
👉 Check out the repo: https:/github.com/Parveshiiii/Seekify I’d love feedback, contributions, and ideas for real‑world use cases. Let’s make search smoother together!
The moment we've been waiting for — ACE-Step dropped their new model: Ace-Step 1.5 🎉 🔗 ACE-Step/Ace-Step1.5 And the best part? It's released under the MIT license. We've already started integrating it into our project. Let's go 🚀
🚀 Wanna train your own AI Model or Tokenizer from scratch?
Building models isn’t just for big labs anymore — with the right data, compute, and workflow, you can create **custom AI models** and **tokenizers** tailored to any domain. Whether it’s NLP, domain‑specific datasets, or experimental architectures, training from scratch gives you full control over vocabulary, embeddings, and performance.
✨ Why train your own? - Full control over vocabulary & tokenization - Domain‑specific optimization (medical, legal, technical, etc.) - Better performance on niche datasets - Freedom to experiment with architectures
⚡ The best part? - Tokenizer training (TikToken / BPE) can be done in **just 3 lines of code**. - Model training runs smoothly on **Google Colab notebooks** — no expensive hardware required.
🏙️ Hugging Face Community Post Title: 🧬 Experimenting with "Dynamic Chaos" in Tamil SLMs
Hi everyone! I just published a new experimental study on Small Language Model (SLM) resilience.
I took the Qwen2.5-0.5B model and put it through a "Chaos Phase" to see how much weight data a tiny model can lose before its understanding of classical Tamil grammar breaks.
Key highlights of the study:
Target Data: Fine-tuned on the Thirukkural (1,330 couplets + modern explanations). The Chaos Step: Applied 20% random weight pruning but implemented "Layer Protection" for the Token Embeddings and LM Head to keep the characters readable. Compression: 4-bit (Q4_K_M) quantization for extreme efficiency. Result: A surrealist classical Tamil model that is ultra-light (~300MB) and ultra-fast!
There is a new open-source music generation model called HeartMuLa. It offers strong, competitive performance compared to Suno and supports English, Chinese, Japanese, Korean, and Spanish. It is optimized to run easily on RTX GPUs and other consumer-grade hardware. HeartMuLa/HeartMuLa-oss-3B https://github.com/HeartMuLa/heartlib
📢 The Announcement Subject: XenArcAI is now Modotte – A New Chapter Begins! 🚀
Hello everyone,
We are thrilled to announce that XenArcAI is officially rebranding to Modotte!
Since our journey began, we’ve been committed to pushing the boundaries of AI through open-source innovation, research, and high-quality datasets. As we continue to evolve, we wanted a name that better represents our vision for a modern, interconnected future in the tech space.
What is changing?
The Name: Moving forward, all our projects, models, and community interactions will happen under the Modotte banner.
The Look: You’ll see our new logo and a fresh color palette appearing across our platforms.
What is staying the same?
The Core Team: It’s still the same people behind the scenes, including our founder, Parvesh Rawal.
Our Mission: We remain dedicated to releasing state-of-the-art open-source models and datasets.
Our Continuity: All existing models, datasets, and projects will remain exactly as they are—just with a new home.
This isn’t just a change in appearance; it’s a commitment to our next chapter of growth and discovery. We are so grateful for your ongoing support as we step into this new era.
Think you know which AI papers go viral? Test your instincts! I built a little game where you try to guess the popularity of AI research papers from the Hugging Face Daily Papers feed.
How it works: You'll see two papers side by side—read the titles, check the abstracts, and pick which one you think got more upvotes from the HF community.
It's a great way to discover trending AI research while having fun. Tests your intuition about what the ML community finds interesting.
The core idea: instead of treating physics as a soft condition the model can work around during optimization, enforce it strictly via reinforcement learning. The paper focuses on rigid body dynamics - collisions, pendulums, free fall, rolling.
So, Koreans are also doing great progress behind Chinese, Their two open source ai models that are actually good in coding. upstage/Solar-Open-100Bskt/A.X-K1
I’m excited to release hawky-ai-Qwen3-0.6B-Marketing-MoT, a specialized SLM designed for deep strategic reasoning in performance marketing.
While small at 0.6B parameters, this model punches way above its weight class by utilizing a Mixture of Thoughts (MoT) framework. It doesn't just give you an answer; it thinks through the logic of Meta Ads scaling, GA4 attribution, and unit economics before providing a strategic recommendation.
Key Features:
Thinking-First: Trained on 1,500+ critical thinking scenarios. MoT Framework: 5 distinct reasoning styles (Linear, Exploratory, Critical, Deconstructive, Analogical). SLM Speed: Perfect for low-latency, high-precision marketing audits. Check it out on Hugging Face: 🔗 Sri-Vigneshwar-DJ/hawky-ai-Qwen3-0.6B-Marketing-MoT
We Built a Music App with ACE-Step – Looking for Feedback
Hey everyone,
We've been building AceSteps – a platform where anyone can create music using the ACE-Step model (ACE-Step/ACE-Step-v1-3.5B). You can mint your tracks as NFTs, tokenize them into 100,000 fractional shares, and trade them on Uniswap V4. When your song gets popular, token holders earn from ad revenue automatically. It's a Farcaster Mini-App on Base Network.
But we want to make it better, and we'd love your input:
What's the one feature that would make you actually use an AI music tool regularly? Andd any suggestions on how we can make this model better? Actually sharing here for this question. 🤗
I am very excited to see the release of nyuuzyou/gitee-code. This is exactly what I have been looking for. Thank you to @nyuuzyou for his hard work on this.
