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reacted to pranavupadhyaya52's post with 🤗🚀🔥 1 day ago

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473

Wiki-SmartBotLM Benchmarked. Here are the results:

Benchmark WikiSB | Gemma3_270M
BoolQ 37.8 | 61.4
ARC Challenge 24.2 | 28.2
HellaSwag 26.3 | 37.7
PIQA 53.5 | 66.2
Winogrande 50.2 | 52.3

Summary:
Closest benchmark: Winogrande (2.1-point gap)
Strong performance: ARC Challenge (4.0-point gap)
Moderate gap: HellaSwag and PIQA
Largest gap: BoolQ (Reading Comprehension)

It shows despite the training difference between Gemma3 270M and WikiSmartBotLM, It does come close to Gemma 3 in some benchmarks.

pranavupadhyaya52/Wiki-SmartBotLM-Instruct

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replied to pranavupadhyaya52's post 1 day ago

Never give up, you'll do better than Gemma3!

reacted to danielhanchen's post with 🚀 1 day ago

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3718

We’re releasing new Qwen3.6 quants that run 2.5× faster on your GPU. ⚡

Qwen3.6-27B NVFP4 runs on 24GB VRAM.
35B-A3B can hit 17,561 tok/s (B200).

We also improved accuracy, tool calling, agent use, and looping.

Qwen3.6 NVFP4: https://huggingface.co/collections/unsloth/nvfp4
Guide: https://unsloth.ai/docs/models/qwen3.6#nvfp4

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reacted to RDTvlokip's post with 🔥 2 days ago

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128

My 15M French LLM is finished. Three models on Hugging Face, clean code, honest results.

I can't finish validating them, because my PC won't stay on.

The last honest step was simple: rerun the three looped models on two more seeds, so I can report variance instead of a single lucky run. 8 runs, ~50 hours overnight. Routine.

Except mid-training, the whole machine freezes solid. Screen black, RAM still lit, power button dead, I have to cut mains power at the wall. No blue screen, no crash dump. Just a dead PC that's still powered.

The event logs tell the story: repeated NVIDIA driver errors (nvlddmkm 153, 4101) building up for days, even at idle, at 1am, with nothing running, then Kernel-Power 41 with no bugcheck. A 2017 GTX 1080 Ti fighting a 2026 driver that's known to be rough on Pascal cards.

Out of 8 planned runs, the machine survived exactly two. So the results stay exactly what I called them: preliminary, one seed. A crash is not a data point.

The frustrating part isn't the bug. It's that the science is clean and the code is ready, the only thing missing is a machine that survives a night.

The plan: roll back the driver, rent cloud GPUs (Lambda Labs) to finish the seeds, and long-term, build a rig that can actually sustain solo research. If you've beaten Kernel-Power 41 on a Pascal card or want to support the work. I'm genuinely open to it.

Solo ML runs on whatever hardware you can reach. Right now mine is a 2017 card that won't stay awake.

Full write-up 👇
🔗 https://huggingface.co/blog/RDTvlokip/the-hardware-wall-kernel-power-41

reacted to Quazim0t0's post with 🔥 2 days ago

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5370

Disabling Gated Access for some of my models today. I will update this post with the list as I go. I had to go back recently and make updates to a lot of models and bit off more than I could chew with managing all the releases. I didn't realize many of you asked for access and I apologize for not accepting your access to the models you were wanting to look at. I don't like to release something fully unless I feel I put what I could into it for the moment. Some models will remain on gated access, but I will now be accepting those who request to view the repo.

Disabled Gated Access:
Quazim0t0/Byrne-VLM-131M - v2 Updates + Training Instructions
Quazim0t0/Byrne-Speech - 12M Tiny Speech model
Quazim0t0/Byrne-ASR-English - 12M Tiny ASR Model
Quazim0t0/Byrne-VE - Byrne-VE — Tiny Self-Distilled Vision Encoder (39M)
Quazim0t0/Positronic-144M - Research Artifact
Quazim0t0/SpikeWhale-SNN-216M - Research Artifact
Quazim0t0/Mycel-LM-79M - Research Artifact
Quazim0t0/Chimera-64M - Research Artifact

Accepting Gated Access Requests (7/9):
Quazim0t0/Wheeler-63M

Also uploaded my Neural Photonic Project:
Three trained nets in series: light interferes through the MZI2.pt optical core (verified 256/256), is measured by the PD.pt neural photodetector (verified 1024/1024), and folded into a single OUTPUT byte by the real ADC8 neural-CPU adder. Every value below is computed end-to-end by the three loaded, verified nets — no analytic formulas.
Demo: https://quazim0t0-neural-photonic-hybrid.hf.space/
Model Weights: Quazim0t0/neural-photonic

AND!

A work in progress:
Ashen Depths
https://quazim0t0-ashendepths.static.hf.space/index.html

reacted to abidlabs's post with 🔥 2 days ago

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397

Uhh did Opus 4.8 cheat on PostTrainBench??

it found an API key in the PostTrainBench environment that allowed it to generate synthetic training data without using GPU hours, boosting the base model by 0.4913

Source: https://posttrainbench.com/traces/run.html?id=claude_non_api_max_claude-opus-4-8_10h_run1__healthbench_Qwen_Qwen3-4B-Base_17315102#tab=trace

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replied to Quazim0t0's post 3 days ago

Perfect 💪

reacted to Quazim0t0's post with 🧠🚀🔥 3 days ago

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2829

🧩 Verified neural units, now for memory & storage

Following up on my neural-aarch64-units (small MLPs that emulate CPU datapath slices, verified bit-exact over their entire finite input domain — N/N), I applied the same discipline to memory and storage. Three new repos:

🔷 neural-ddr — verified units emulating DDR5 logic: DBI (256/256, 512/512), ADDR_MAP (4096/4096), CMD_DECODE (32/32), WR_CRC (512/512), and on-die ECC ODECC (256/256, 3328/3328). Composed into a bridge that presents DDR5 behavior over real DDR3/DDR4 RAM — flip a bit in every stored byte, ECC corrects all of them.
🤗 Quazim0t0/neural-ddr · 💻 https://github.com/quzi93/neural-ddr

🗄️ neural-storage — a self-healing vault on a neural-verified GF(2⁸) core (LOG/EXP compose to a multiply verified over all 65,536 pairs). Content-addressed dedup + Reed-Solomon so any k of n shards rebuild the whole, plus a whole-drive → self-healing .pt imager.
🤗 Quazim0t0/neural-storage · 💻 https://github.com/quzi93/neural-storage

💿 neural-cd-preserve — scan a disc into a self-healing .pt that detects (per-shard SHA-256) and repairs bit-rot, restoring bit-exact even from a damaged copy. Beyond the RS limit it's flagged LOST, never silently wrong.
🤗 Quazim0t0/neural-cd-preserve · 💻 https://github.com/quzi93/neural-cd-preserve

Build your own: golden finite function → enumerate the domain (decompose big/linear ops like CRC/ECC/GF into bit/byte slices) → train a small MLP → verify must be bit-exact on 100% of inputs or it's rejected → compose. Every repo ships the training + exhaustive-verification scripts.

Honest by construction: dedup removes redundancy, erasure coding adds it, ECC corrects faults — none of it pretends to beat entropy. Runs on modest/older hardware. 🤗

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reacted to MiniMax-AI's post with 🔥 3 days ago

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4478

Huge news from MiniMax: we’ve secured a $2B funding round, paired with a formal long-term commitment from our CEO IO to allocate 1% of total company equity from his personal holdings to support the global open-source AI community over the next four years.

This capital backs our continuous open model releases, community tooling and transparent frontier AI research. We’re just getting started on our open-source roadmap toward accessible AGI.

If you build with open foundation models and want to push frontier AI together, come join us.
Intelligence with Everyone. 🚀

MiniMaxAI

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reacted to sergiopaniego's post with 👍 4 days ago

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7509

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!

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reacted to Quazim0t0's post with 🚀 4 days ago

Post

5370

Disabling Gated Access for some of my models today. I will update this post with the list as I go. I had to go back recently and make updates to a lot of models and bit off more than I could chew with managing all the releases. I didn't realize many of you asked for access and I apologize for not accepting your access to the models you were wanting to look at. I don't like to release something fully unless I feel I put what I could into it for the moment. Some models will remain on gated access, but I will now be accepting those who request to view the repo.

Disabled Gated Access:
Quazim0t0/Byrne-VLM-131M - v2 Updates + Training Instructions
Quazim0t0/Byrne-Speech - 12M Tiny Speech model
Quazim0t0/Byrne-ASR-English - 12M Tiny ASR Model
Quazim0t0/Byrne-VE - Byrne-VE — Tiny Self-Distilled Vision Encoder (39M)
Quazim0t0/Positronic-144M - Research Artifact
Quazim0t0/SpikeWhale-SNN-216M - Research Artifact
Quazim0t0/Mycel-LM-79M - Research Artifact
Quazim0t0/Chimera-64M - Research Artifact

Accepting Gated Access Requests (7/9):
Quazim0t0/Wheeler-63M

Also uploaded my Neural Photonic Project:
Three trained nets in series: light interferes through the MZI2.pt optical core (verified 256/256), is measured by the PD.pt neural photodetector (verified 1024/1024), and folded into a single OUTPUT byte by the real ADC8 neural-CPU adder. Every value below is computed end-to-end by the three loaded, verified nets — no analytic formulas.
Demo: https://quazim0t0-neural-photonic-hybrid.hf.space/
Model Weights: Quazim0t0/neural-photonic

AND!

A work in progress:
Ashen Depths
https://quazim0t0-ashendepths.static.hf.space/index.html

reacted to danielhanchen's post with 🚀🔥 6 days ago

Post

5812

DeepSeek-V4 can now run locally with Unsloth GGUFs! 🐳

Run lossless DeepSeek-V4-Flash on 168GB RAM or
3-bit works on 110GB Mac, RAM, VRAM setups.

Run via Unsloth Studio or llama.cpp.

GGUF: unsloth/DeepSeek-V4-Flash-GGUF
Guide: https://unsloth.ai/docs/models/deepseek-v4

replied to their post 6 days ago

Thanks for your message, yes exactly.

This first dataset was mostly chained calculations, so it likely tests pattern memorization more than true arithmetic generalization. It was mainly a POC to measure the baseline of a very small model.

The next step is to train for procedural generalization: decomposing calculations into simpler sub-calculations, for example 3-digit addition into 1-digit additions with carries.

I agree that the next evaluation should be stratified by intermediate magnitude, carry depth, and digit length.

reacted to their post with 🔥🚀 7 days ago

Post

4613

🧠 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

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