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posted an update 1 day ago

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3304

🔓 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

Banaxi-Tech

posted an update 2 days ago

Post

3724

Today we are releasing BananaMind-KV1-8M-2Bit-Experimental, a KV-cache-aware trained model that stores its generation KV cache in 2-bit precision instead of the usual 16-bit precision.

Result: 5.33x smaller KV cache vs FP16, with 0.0916 mean KLD against a 16-bit KV cache reference on WikiText-2.

Model: BananaMind/BananaMind-KV1-8M-2Bit-Experimental

The important part: this is not just post-training KV cache quantization.
Instead we take the BitNet approach.

KV1 is trained with a 2-bit-aware K/V path. Instead of training a normal model and quantizing the cache afterwards, the model learns during training to operate under the low-bit KV constraint, closer in spirit to the BitNet idea of training for the low-bit regime.

During generation, each K/V vector is quantized into 4 affine levels and packed into uint8 tensors, with four 2-bit values stored per byte.

WikiText-2 eval vs 16-bit KV cache reference:

Mean KLD: 0.0916 nats/token
Mean KLD: 0.1322 bits/token
Average KV cache shrink vs FP16: 5.33x
Evaluated positions: 372,675

If this actually gets used in models like Qwen or Gemma, then it may be possible to run 128K or even 256K Context on a Normal Machine!
Try it here: BananaMind/BananaMind-KV1-8M-2Bit-Experimental

Code: https://github.com/Banaxi-Tech/kv1

4 replies

pankajpandey-dev

posted an update 2 days ago

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3921

🇮🇳 Qwen3.5-9B Hindi Instruct — it stops thinking in English
Ask base Qwen3.5-9B a question in Hindi and it burns hundreds of tokens thinking in English inside its think block before a single Devanagari word appears — then code-switches in the answer. I fine-tuned it to close the think block instantly and reply in pure, native Hindi.
✅ Model (16-bit): pankajpandey-dev/qwen3.5-9b-hindi-instruct
✅ GGUF (Q4/Q5/Q8): pankajpandey-dev/qwen3.5-9b-hindi-instruct-GGUF
✅ Try it in the browser: pankajpandey-dev/qwen3.5-9b-hindi-demo
Recipe: Unsloth + LoRA (r=16, response-only loss) on 12.9k Hindi pairs — AI4Bharat anudesh + dolly-hi + wikiHow-hi + Aya Hindi (human-written). The Q4_K_M is 5.4 GB and runs on a plain laptop CPU.
New in this run vs my earlier models: mixed in long-form native sources (wikiHow) after my last eval showed the fine-tune traded detail for conciseness — this one keeps answers detailed and native.
Part of my weekly 🇮🇳 Hindi LLM Series. Feedback welcome 🙏
#Hindi #IndicNLP #Qwen #GGUF #LocalLLM #Unsloth

3 replies

ovi054

posted an update 2 days ago

Post

3712

Turn any topic to animation explainer video

It is powered by Qwen3-14B + Manim LoRA and turns any concept into a Manim explainer video.

👉 Try it now:
build-small-hackathon/anim-vid-ai

RDTvlokip

posted an update 3 days ago

Post

1939

I finally changed the architecture of my 15M French LLM. It worked. Then I almost fooled myself about how much and catching that was the real win.

After proving last time that architecture is a threshold, not a lever, I got stubborn: could I change how the model learns? Four honest attempts, Lion, a sharper AdamW β2, multi-token prediction, LayerScale. Four failures. The bottleneck wasn't the learning rule either.

So I changed the shape of the computation instead: loop the same transformer blocks 4×, deeper reasoning, zero added parameters. It beat the baseline on perplexity, the first thing in the whole project to move that number. Then I added my own twist: let each token decide how deep to think, halting on its own entropy.

My first evaluation was spectacular. Coherence up 65%. Hallucinated names down 62%.

It was noise.

Eight prompts, one seed. I re-ran on 50 prompts × 200 tokens and watched the gains shrink to "modest" and on out-of-domain prompts, recurrence actually made things worse. No universal winner. And none of it is new: it's Adaptive Computation Time (2016), the Universal Transformer (2018), and LoopViT (2026), recombined and measured honestly.

The real lesson:

A number from 8 prompts is a rumor. The eval harness that kills your own best result is worth more than the result it kills. Cite your lineage. Stay preliminary until multiple seeds say otherwise.

The three models are live. The write-up is honest about every caveat 👇

🔗 https://huggingface.co/blog/RDTvlokip/teaching-a-15m-french-llm-to-think-deeper

6 replies

fffiloni

posted an update 1 day ago

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711

You can explore and try my Agentic Space Factory:
fffiloni/agentic-space-factory-UI-public

Be kind 🤗

pranavupadhyaya52

posted an update about 19 hours ago

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329

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

Quazim0t0

posted an update 3 days ago

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2338

Created research language model whose channel-mixing block is not an MLP. It is a differentiable Neighbour-Sensing fungal-colony-growth model: each token is expanded into a colony of hyphal tips that grow in a bounded latent region, sense a shared density field, and steer their own growth — the "MLP" is replaced by a few differentiable steps of colony growth, read back out into the hidden state.

Quazim0t0/Mycel-LM-79M

Also the original SpikeWhale project — the one that sparked all the other SpikeWhale related projects. Every spiking primitive here is hand-written in plain PyTorch: the leaky integrate-and-fire (LIF) neuron dynamics, the fast-sigmoid surrogate gradient, and the backprop-through-time training loop. No snntorch, no spikingjelly, no norse, no bindsnet — the network is a genuine from-scratch SNN.

Quazim0t0/SpikeWhale-SNN-216M

SeaWolf-AI

posted an update 3 days ago

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2955

🚀 Adding a GPU without building one

AI is usually framed as "how smart is the model / how many GPUs did you buy." The real bottleneck is elsewhere — how efficiently you use the GPUs you already have.

Training happens once; inference runs the entire time users use your product. So a service's economics come down to cost per token. Inference acceleration uses software to pull several times more out of the same GPU — the effect of plugging in one more "virtual GPU."

VIDRAFT's VKAE, measured (B200, same-harness, no quality loss):

Qwen3.5-35B-A3B (MoE): 25.7 → 601 tok/s (23.4×)
Darwin-36B-Opus (in-house MoE): 25.0 → 280.8 (11.2×)
10,000+ tok/s peak aggregate under concurrency
The key: it's reproducible — model + serving shipped as one container.

docker pull vidraft/qwen35-vkae:601
Don't take our word for it — run it yourself. The mechanism will be released as a paper.

🏆 Leaderboard & demo 👉 VIDraft/vkae
Articles 👉 https://huggingface.co/blog/FINAL-Bench/vkae-leaderboard

3 replies

Shrijanagain

posted an update 6 days ago

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131

Welcome Researcher and Developers!

SKT AI Labs, we are pushing the boundaries of AI architecture and research—and today, we are thrilled to open our doors to the global research community!

We warmly welcome researchers, developers, and AI enthusiasts to join us and contribute to our R&D efforts.

🧪 What You Can Explore:

We invite you to experiment with our WMF (Weight Manifold Fusion) technology. You can test this high-dimensional fusion technique on smaller models to gain a deeper understanding of its behavior and token convergence.

---------- CHECK OUT:

SPACE : SKT-NRS/RD
EXPERIMENT : sKT-Ai-Labs/SKT-SURYA-H
DIRECT TO MAIN DISCUSSION : SKT-NRS/RD#1

🤝 Your Feedback Shapes the Future :

If it works: Fantastic! Share your results with us and contribute directly to the core vision of SKT AI Labs.

If it doesn't work: No problem at all! Your critical feedback is just as valuable to us. Every experiment and anomaly helps us refine this architecture to make it more stable and robust.

We firmly believe that true innovation stems from community collaboration and transparent testing. Let's build the future of advanced AI together. Your ideas, test results, and feedback are always welcome!

You Can Still Research and Development On WMF Only SKT-SURYA-H Model is Dismissed.

Let's innovate and build together! 💡

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