Interesting. Did you learn how the creator of the dataset synthesised it?
nomadicsynth PRO
AI & ML interests
Recent Activity
Organizations


10 days? Rookie numbers 😊. so many side-quests. no idea what to do with myself lol. i'd love to hear about your ideas, and happy to give some feedback (for what it's worth)

I've seen some unsuccessful attempts at running Wan2GP inside a Hugging Face Space, which is a shame as it is a great Gradio app!
So here is a fork that you can use, with some instructions on how to do this:
jbilcke-hf/Wan2GP_you_must_clone_this_space_to_use_it#1
Note : some things like persistent models/storage/custom LoRAs might not be fully working out of the box. If you need those, you might have to dig into the Wan2GP codebase, see how to tweak the storage folder. Happy hacking!

I recently shared this list of resources I’ve been using to learn AI:
🔗 https://github.com/ArturoNereu/AI-Study-Group

System Prompt Learning (SPL) enables LLMs to automatically learn problem-solving strategies from experience, rather than relying on static prompts.
🚀 How it works:
Your LLM builds a database of effective strategies, selects the best ones for each problem, and refines them over time based on success rates.
📊 Results across math benchmarks:
Arena Hard: 29% → 37.6% (+8.6%)
AIME24: 23.33% → 30% (+6.67%)
OptILLMBench: 61% → 65% (+4%)
The best part? All strategies are human-readable and the system gets progressively better at problem types you use frequently.
✨ Key benefits:
🔄 Cumulative learning over time
📖 Transparent, inspectable strategies
🔌 Works with any OpenAI-compatible API
⚡ Simple integration: just add "spl-" prefix to your model
Built as an open-source plugin in optillm. After 500 queries, our system developed 129 strategies and refined 97 of them!
This feels like a genuine step toward AI that learns from experience while staying completely interpretable.
🔗 GitHub: https://github.com/codelion/optillm/tree/main/optillm/plugins/spl
📖 Full article: https://huggingface.co/blog/codelion/system-prompt-learning
🐦 Original Karpathy tweet: https://x.com/karpathy/status/1921368644069765486
Have you experimented with advanced system prompting? What strategies would you want your LLM to learn?

🚀 Project Introduction
Hello! Today we're excited to introduce an AI-powered solo podcast generator that creates high-quality voice cloning with authentic emotional expression.
Transform any PDF document, web URL, or keyword into a professional podcast with just a few clicks! 📚➡️🎧
VIDraft/Voice-Clone-Podcast
✨ Key Features
1. 🎯 Multiple Input Methods
URL: Simply paste any blog or article link
PDF: Upload research papers or documents directly
Keyword: Enter a topic and AI searches for the latest information to create content
2. 🎭 Emotionally Expressive Voice Cloning
Powered by Chatterbox TTS:
🎤 Voice Cloning: Learn and replicate your unique voice perfectly
📢 Natural intonation and emotional expression
🌊 Customizable emotion intensity with Exaggeration control
⚡ Seamless handling of long texts with automatic chunking
3. 🤖 State-of-the-Art LLM Script Generation
Professional-grade English dialogue using Private-BitSix-Mistral
12 natural conversational exchanges
Real-time web search integration for up-to-date information
Fully editable generated scripts! ✏️
💡 Use Cases
📖 Educational Content
Transform complex research papers into easy-to-understand podcasts
Create English learning materials in your own voice
📰 News & Information
Convert international articles into engaging audio content
Produce global trend analysis podcasts
🎨 Creative Content
Tell stories in English with your own voice
Build your global personal brand with custom audio content
🛠️ Tech Stack
🧠 LLM: Llama CPP + Private-BitSix-Mistral
🗣️ TTS: Chatterbox (Voice Cloning & Emotional Expression)
🔍 Search: Brave Search API
📄 Document Processing: LangChain + PyPDF
🖥️ Interface: Gradio
🎉 What Makes Us Special
🎤 Voice Cloning: Perfect voice replication from just a short audio sample
😊 Emotion Contro 📏 Unlimited Length 🔄 Real-time Updates

Every time you use a HF space you randomly start dancing for 5 minutes
This one fr i'm dancing all day anyway idk how people survive in cubicles

It's an evolutionary coding agent that uses LLMs to discover and optimize algorithms. I successfully replicated DeepMind's results on circle packing (99.97% match!) and evolved a random search into a simulated annealing algorithm.
✨ Key features:
- Evolves entire codebases (not just single functions)
- Works with any OpenAI-compatible API
- LLM ensemble approach for better results
- Multi-objective optimization
👉 Check it out:
GitHub: https://github.com/codelion/openevolve
Blog post: https://huggingface.co/blog/codelion/openevolve
Would love to hear your thoughts or answer any questions about it!

Check out
https://huggingface.co/spaces/ProCreations/realtime-ai-visualization
This cool space visualizes a real neural net in real time. It trains a real 199 parameter model on XOR. With baby mode for non-devs and advanced mode for developers or enthusiasts, (hopefully) everyone will understand!

Samsung Electronics' official Hugging Face account has been hacked. Approximately 17 hours ago, two new language models (LLMs) were registered under Samsung Electronics' official Hugging Face account. These models are:
https://huggingface.co/Samsung/MuTokenZero2-32B
https://huggingface.co/Samsung/MythoMax-L2-13B
The model descriptions contain absurd and false claims, such as being trained on "1 million W200 GPUs," hardware that doesn't even exist.
Moreover, community participants on Hugging Face who have noticed this issue are continuously posting that Samsung Electronics' account has been compromised.
There is concern about potential secondary and tertiary damage if users download these LLMs released under the Samsung Electronics account, trusting Samsung's reputation without knowing about the hack.
Samsung Electronics appears to be unaware of this situation, as they have not taken any visible measures yet, such as changing the account password.
Source: https://discord.gg/openfreeai

Skywork/Matrix-Game
✨ 17B with MIT licensed
✨ Diffusion-based image-to-world video generation via keyboard & mouse input
✨ GameWorld Score benchmark for Minecraft world models
✨ Massive Matrix Game Dataset with fine-grained action labels

I turned this into a GitHub repo:
https://github.com/ArturoNereu/AI-Study-Group
If you’re just getting started, I recommend:
📘 Deep Learning – A Visual Approach: https://www.glassner.com/portfolio/deep-learning-a-visual-approach
🎥 Dive into LLMs with Andrej Karpathy: https://youtu.be/7xTGNNLPyMI?si=aUTq_qUzyUx36BsT
🧠 The 🤗 Agents course](https://huggingface.co/learn/agents-course/
The repo has grown with help from the community (Reddit, Discord, etc.) and I’ll keep updating it.
If you have any favorite resources, I’d love to include them.

Is the GPU offer still available? 😂

@ProCreations relatable

A few months ago it was just a wild idea I shared with @bygimenez , now it's real.
Dione (Beta) is here, the easiest way to discover and install open-source apps, especially AI ones.
Think of it as the Steam of open source. Installing open-source tools is often a mess. Dione fixes that.
Beautiful UI and workflow. Soon multi-platform, multilingual & fully open-source.
Users can even write and share their own installation scripts. This is just the beginning.
🚀 Join our exclusive Beta
→ https://getdione.app/beta/join

After nights of development, we’re finally open-sourcing TFrameX, a powerful AI agent communication and coordination library.
TFrameX lets you:
🤖 Run agents in dynamic flows
🔁 Compose reusable patterns like Sequential, Parallel, Router, and more
🧠 Enable agent-to-agent collaboration and delegation
⚡ Build modular, complex multi-agent systems that just work
👉 GitHub: TFrameX
https://github.com/TesslateAI/TFrameX
But we didn’t stop there.
We also built a sleek visual builder to design, deploy, and debug your agent patterns without writing boilerplate!
🧩 Visual Studio for TFrameX: https://github.com/TesslateAI/Studio
If you’re building agent frameworks, LLM tools, or agentic apps, TFrameX gives you the tools to move fast and reason deeply.

I'm attempting to use a 7B LLM, Llama in this case, with an embedding head stuck on the end instead of the lm_head. I used an LLM to rank a ton of randomly selected pairs of papers based on if they have good connections, and trained the embedding head on triplets mined from those ranked pairs.
The idea is for the embedding head to learn to align features from paper abstracts that complement each other.
this is the first version and yeah, I'm not overly impressed. I think I'm seeing results that kinda vibe with the concept sometimes, but I think the ranking criteria for the dataset were a bit loose. I'm going to try making a new dataset with better, more strict, more nuanced criteria and train a second version of the model from that.

Thanks for letting me know. I've fixed the issue. Feel free to try again.