Hugging Face's logo

Join the conversation

Join the community of Machine Learners and AI enthusiasts.

Sign Up

All HF Hub posts
danielhanchen 
posted an update 1 day ago
satgeze 
posted an update 2 days ago
view post
Post
3445
First GGUF quants of Tencent's Hy3 (299B MoE), built before official llama.cpp support exists.

Hy3 dropped ~30 hours ago with only MLX and MXFP4 quants, both datacenter-sized. So I converted it myself using a community llama.cpp fork that implements the hy_v3 architecture.

What's in the repo:

- IQ1_M (62GB, fits a 128GB MacBook), IQ2_M (90GB), Q2_K (101GB), all with 1M context baked in via YaRN
- IQ quants are importance-matrix: bootstrap style. The static Q2_K ran RAM-resident to compute the imatrix, then IQ1_M and IQ2_M were requantized from the archived f16 with it
- Fixed chat template (the stock one uses .format() calls llama.cpp's Jinja rejects)
- Build instructions for the fork, including the two gotchas that cost me three build attempts

Honesty section, because that is how these repos work: this is EXPERIMENTAL. Not needle-certified yet (1M is baked but unverified, certification ladder will be published either way). MTP layer exists in the checkpoint but no llama.cpp build can run hy_v3 MTP inference yet, so it is not included. Real gate outputs are on the card, misses and all, judge for yourself.

satgeze/Hy3-1M-GGUF

Full quant ladder (Q3 through Q8) is mirroring to ModelScope for bigger hardware.
  • 9 replies
·
PhysiQuanty 
posted an update 2 days ago
view post
Post
4429
🧠 Arithmetic-SLM : A 30M model that manages to compute simple arithmetic better than a 3B model 🚀
WhirlwindAI/Arithmetic-SLM
WhirlwindAI/arithmetic-slm

🏆 Leaderboard ArithMark-2 🏆
🥇 Qwen/Qwen2.5-Math-1.5B = 82.08%
🥈 WhirlwindAI/Arithmetic-SLM = 78.60% (31.7M Params)
🥉 Qwen/Qwen2.5-3B = 78.44%

Example WhirlwindAI/Arithmetic-SLM =
0.5 * 0.5 = 0.25 ✅
105 + 45 / 8 = 110 ✅
(132 / 12) + (46 - 15) = 42 ✅
(10 + 28) * 3 = 114 ✅
1 * (16 + 28) = 44 ✅
(21 + 27) * (14 - 7) = 336 ❌ 

leaderboard = """
|              Model               |    Params    |   Score   |
|----------------------------------|--------------|-----------|
|      Qwen/Qwen2.5-Math-1.5B      |     1.54B    |   82.08%  |
|    WhirlwindAI/Arithmetic-SLM    |    31.70M    |   78.60%  | <=
|         Qwen/Qwen2.5-3B          |     3.09B    |   78.44%  |
|        Qwen/Qwen2.5-1.5B         |     1.54B    |   77.72%  |
|    Qwen/Qwen2.5-Coder-1.5B       |     1.54B    |   74.88%  |
|   HuggingFaceTB/SmolLM2-1.7B     |     1.71B    |   66.12%  |
|        Qwen/Qwen2.5-0.5B         |      494M    |   63.04%  |
| facebook/MobileLLM-R1-140M-base  |      140M    |   53.88%  |
|     SupraLabs/Supra-50M-Base     |       52M    |   27.12%  |
"""

Bench =
AxiomicLabs/ArithMark-2.0
DataSet =
WhirlwindAI/Arithmetic
By Science AND FOR SCIENCE <3
  • 3 replies
·
loleg 
posted an update 3 days ago
view post
Post
1267
Thank you Hugging Face team for some very helpful and quick support today. Greetings from the AI for Good summit in Geneva!
  • 1 reply
·
Reubencf 
posted an update 2 days ago
ProCreations 
posted an update 3 days ago
sergiopaniego 
posted an update about 14 hours ago
view post
Post
1277
Frontier models use distillation as a step of their post-training pipelines.

In 2026 it has three jobs: compress a big model into a small one, merge RL experts into a single model, and let a model teach itself.

I wrote up which frontier models use each one and how: https://huggingface.co/blog/sergiopaniego/distillation-2026

It pairs with Class 2 of the Training an Agent series Ben and I are doing, where we teach these techniques hands-on with TRL!
pranavupadhyaya52 
posted an update 3 days ago
view post
Post
3557
After several weeks of experimenting, debugging, and iterating, I am excited to share WikiSmartBotLM.

WikiSmartBotLM is a compact decoder only language model built from the ground up as an educational and practical project. The goal was not simply to train another language model, but to create one that is easy to understand, modify, and experiment with while following many of the architectural ideas used in modern LLMs.

The model is built on a custom Transformer architecture featuring Rotary Positional Embeddings, RMSNorm, SwiGLU feed forward layers, grouped query attention, and an efficient autoregressive decoder optimized for local inference.

The repository includes the complete model implementation, configuration files, tokenizer integration, training pipeline, inference scripts, checkpoint conversion utilities, and examples that demonstrate how each component works together. Whether you want to understand the forward pass, train your own model, or build applications on top of WikiSmartBotLM, everything is designed to be approachable.
You can directly run the model via the models Huggingfaces space, which I've included in the post.

Model Repository: pranavupadhyaya52/Wiki-SmartBotLM-Instruct

I hope WikiSmartBotLM becomes a useful resource for anyone who enjoys learning by building. Feedback, issues, feature requests, and contributions are always welcome.
https://huggingface.co/spaces/pranavupadhyaya52/WikiSmartBot
  • 1 reply
·
SeaWolf-AI 
posted an update 4 days ago
view post
Post
5096
🔓 We ran genuine quantum key-recovery on 'real IBM quantum hardware' — and pushed the frontier well past the largest hardware demos we're aware of (which sat at N=4).

Using Simon's algorithm on ibm_kingston, we recovered the secret key of two symmetric-cipher structures:
• Even–Mansour — N=5 → N=10
• 3-round Feistel (DES-family) — block 6 → 8

Each verified against an 'independent control key', using error mitigation only (no QEC).

🧭 Honest scope: this is not a quantum speedup (the effective difficulty tracks the classical birthday bound ~2^{n/2}), not a break of real AES/RSA, and not 16-round DES (ours is 3-round). The recovery method is reserved for a forthcoming paper; formal record status is pending peer review.

📄 Write-up: https://huggingface.co/blog/FINAL-Bench/quantum
🕹️ Try it live in your browser: https://vidraft-quantumos.hf.space/crypto
🏆 Leaderboard: FINAL-Bench/quantum-bench-leaderboard

#quantum #cryptography #quantumcomputing
Quazim0t0 
posted an update 5 days ago
view post
Post
139
I built/building a small neural Raytracing/Projective-dynamics (PD)/Physics engine from scratch

The idea I wanted to test: in a projective-dynamics solver, could you replace each hand-derived local constraint projection with a learned one, while keeping the analytic parts (rotations, the global solve) exactly as they are? It's one tiny network, shared across every element and across constraint types through material tokens. A new material isn't a new network, just a new token row. Fluids fall out of the same idea, with water treated as one more token.

A few things held up in testing: one tied projector matched five separate per-material solutions, the neural fluid tracked the exact analytic solver closely on a dam-break sim, and a learned warm-start trimmed solver iterations without touching correctness.

Try it here:

(Projective-dynamics)
Dam Break Demo:
https://quazim0t0-neural-physics-engine-demo.hf.space/
FPS Shooter Demo:
https://quazim0t0-neural-combat-evolved.hf.space/

(Raytracing)
Voxel World:
https://quazim0t0-neural-world.static.hf.space/index.html

(Rigid-Body Physics) (Work in progress lol)
Ashen Depths:
https://quazim0t0-ashen-depths.static.hf.space/index.html

Model Repo:
Quazim0t0/neural-physics-engine
Quazim0t0/neural-raytracing
Quazim0t0/physgait-weights
Recently active users