If AI could master just one thing, what should it be?
Human-level conversation that actually feels real? Flawless, bug-free code? Perfect math and logic every single time?
Whatever you pick, just know the AI will not be so good at the other topics! No picking "all of them" either lol What do you think matters most for AI to truly level up? Drop your thoughts in the comments — we’ll share our answer (and maybe a few of yours 👇) in the next post.
Come check out ProCreations/black-hole-sim-randomized a high-fidelity dataset with 400,000+ randomized black hole simulations — packed with relativistic metrics, Kerr geometry, and GR weirdness to help AIs actually understand physics.
🕳️ Teach your model: • Time dilation • Redshift • Orbital dynamics • Frame dragging • Full Kerr tensors …and more, all in raw JSONL!
This release celebrates SimpleMath hitting 200 downloads — thank you all so much for the support! 🙌
Training works fine, normal loss, no more gradient explosion or gradient vanishing, etc. BUT, before I officially flip the switch and turn on training, I wanna make sure its the best possible 100m parameter model it can be, so I am working a bit more (probably an extra 3-5 days) to add even more innovative AI improvements to intellite.
🧠 Post of the Day: Quantum AI – Your Thoughts + Our Take
Yesterday we asked: “What will quantum computing do to AI?” Big thanks to solongeran for this poetic insight:
“Quantum computers are hard to run error-free. But once they’re reliable, AI will be there. Safer than the daily sunset. Shure – no more queues ;)”
🚀 Our Take – What Quantum Computing Will Do to AI (by 2035)
By the time scalable, fault-tolerant quantum computers arrive, AI won’t just run faster — it’ll evolve in ways we’ve never seen:
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🔹 1. Huge Speedups in Optimization & Search Why: Quantum algorithms like Grover’s can cut down search and optimization times exponentially in some cases. How: They’ll power up tasks like hyperparameter tuning, decision-making in RL, and neural architecture search — crunching what now takes hours into seconds.
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🔹 2. Quantum Neural Networks (QNNs) Why: QNNs can represent complex relationships more efficiently than classical nets. How: They use entanglement and superposition to model rich feature spaces, especially useful for messy or high-dimensional data — think drug discovery, finance, or even language structure.
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🔹 3. Autonomous Scientific Discovery Why: Quantum AI could simulate molecular systems that are impossible for classical computers. How: By combining quantum simulation with AI exploration, we may unlock ultra-fast pathways to new drugs, materials, and technologies — replacing years of lab work with minutes of computation.
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🔹 4. Self-Evolving AI Architectures Why: Future AI systems will design themselves. How: Quantum processors will explore massive spaces of model variants in parallel, enabling AI to simulate, compare, and evolve new architectures — fast, efficient, and with little trial-and-error.
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⚛️ The Takeaway: Quantum computing won’t just speed up AI. It’ll open doors to new types of intelligence — ones that learn, discover, and evolve far beyond today’s limits.
Quantum Computing + AI = 🤯? What do you think quantum computing will do to AI? Will it revolutionize training speed? Unlock whole new algorithms? Or maybe… just complicate things?
💬 Drop your thoughts below — we’ll share our take and highlight some of your replies in tomorrow’s post!
For every new follower I get on my account over the next 2 weeks, that’s how big my upcoming AI project will be — in millions of parameters. (Example: 16 new followers = 16M parameters!)
I’ll post full training logs and updates so you can watch it being built live. If it somehow hits 1 billion parameters (1,000 followers), that’s the cap — my poor GPUs need mercy 😅 I dare you guys to make me suffer. 😈
You control the size. Let’s build something absolutely insane together. ❤️
Hey there- might have to delay the Qwen fine tune on math project a tiny bit due to testing not going so great: but instead of giving you guys a bad model I’ll take the time to fix it soon. I’d rather be honest and fix it than rush and dishonest so yea. Also yesterday I released another dataset so check it out if you want ProCreations/Simple-FriendlyMath
I’m fine-tuning Qwen 2.5-0.5B to be extremely good at math, using high-quality datasets and some smart training strategies. The logs are looking really promising so far!
Expected release: Tomorrow morning? I’ll post as soon as it’s ready — stay tuned.
If you want faster updates or just wanna chat about it, come join my Discord: https://discord.gg/EXsug2Ux29 (Heads up: we might ask a couple quick questions when you join — just making sure we keep the server safe.)
This project is also helping shape the future of IntellIte. The insights and techniques we’re developing here — better dataset curation, fine-tuning tricks, and evaluation methods — will directly contribute to making IntellIte even sharper, faster, and more reliable, especially for math and reasoning tasks.
Big progress ahead. Can’t wait to share it with you all!
Come check out my new dataset "SimpleMath"! Designed to help small models get simple math almost always right, instead of complex math almost always wrong. ProCreations/SimpleMath Also useful for introducing math slowly to LLM's. Instead of jumping into the complex stuff headfirst, it can slowly learn it better by starting easy and going hard.
A compact chat model built for speed, efficiency, and simplicity.
IntellIte‑Chat v1.0 is the debut model in the IntellIte series—a lightweight conversational transformer crafted to be fast, memory-efficient, and easy to work with. It’s designed for devs and enthusiasts who want sharp results without huge resource demands.
🧠 Parameters & Architecture • Model Size: ~100M parameters • Architecture: Modified GPT-NeoX • Focus: Chat performance with low latency and efficient memory use
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🧃 Support the Build Every dollar you donate is an extra amount of VRAM I get to work with. 😅 This project is fully independent and entirely self-funded. If you want to help bring it to life: 👉 https://buymeacoffee.com/procreations
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💛 Early Supporters All early supporters will be credited here when the model launches. Even the smallest support means the world and pushes this project forward.
Special thanks to: Maybe you?
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🛠️ Development Status • Architecture Design: Completed ✅ • Dataset Planning: Completed ✅ • Training Code: Near Completion 🛠️ • Training Launch: Starting Soon ⏳ • Evaluation Setup: Coming soon 🔜 • Final Release: Coming soon 🔜
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Built to chat. Built on a budget. Built to prove what small models can do.
Intellite chat, a new up and coming small-efficient English-chat based AI model is coming soon! Based off of the GPT 2 tokenizer it will crush any chat (for a model its size).
