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Shrijanagain  updated a Space 23 days ago
sKT-Ai-Labs/README
Shrijanagain  updated a model about 1 month ago
sKT-Ai-Labs/SKT-ST-X-0-3B
Shrijanagain  updated a model about 1 month ago
SKT-NRS/SKT-SURYA-H
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wop 
posted an update 8 days ago
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2160
bench-labs/GCTokenizer-v1 , a multilingual tokenizer which does not require a training corpus

bench-labs developed **GCTokenizer-v1**, which is a multi-lingual tokenizer
Available in four sizes: 32K, 65K, 131K and 262K tokens "S, M, L, XL"
It utilizes an encoding scheme which allows it to handle characters in any language around the world

General (multi lingual)
Consensus (from multiple model tokenizers consensus)
Tokenizer

We included an implementation script too,
built like BPE- it can encode arbitrary text, most of the time, efficiently
Bc-AI 
posted an update 8 days ago
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115
Please stand by, we will be providing the Nova-1 series with a major architectural and training update. Expect the New Nova-1-Standard release in late October to early November. - Regards, Bc-AI on behalf of Smilyai-Labs
wop 
posted an update 10 days ago
wop 
posted an update 11 days ago
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2312
🧪 SlopFinder is here!!

We're building a dataset to study what humans actually consider AI slop.

SlopFinder shows you a random piece of AI-generated text and gives you one simple control: **how slop is it?**
No categories. No complicated forms. Just vote and move on.

Every vote helps build the dataset. 🧩

How does it work?
Samples are pulled from existing datasets, shown anonymously, and collected into our annotation pool. After enough votes, they're exported to Hugging Face for everyone to use.

This is an early MVP, so the dataset is small and the system is still evolving.

Vote here:
https://bench-labs.web.app/slopfinder.html
(refresh page if you want to skip)

Dataset:
bench-labs/slop-classification

@benchlabs
  • 17 replies
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wop 
posted an update 14 days ago
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117
🧩 PixelModel v6 is here! 155M parameters
Try it out on our demo (~15 seconds per image) 256x256 🎉
BenchLabs/Demo

Disclaimer: This model does not produce high quality (4k) and does not follow detailed prompts. Does not have negative prompt. 😔

How long did it take to train?
55 hours across two A100 gpu's 🔥

Model repo:
bench-labs/PixelModel-v6
@benchlabs
  • 3 replies
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Bc-AI 
posted an update 20 days ago
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173
Hello Everyone! Bc-AI here from Smilyai-labs! Today we have done our latest update for CodVa-1-Small. It is very powerful for coding, and benchmark results will come soon. However, it is NOT good for other tasks, with high hallucination rates. We will perform RLHF and DPO very soon!
Shrijanagain 
updated a Space 23 days ago
Bc-AI 
posted an update 24 days ago
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150
Hello everyone! Today we announce our latest coding model, CodVa-1-Small! It is our most capable model to date for coding, which has completed pretraining and support multi-turn conversation! We will Instruction Tune it very soon! its at: Smilyai-labs/CodVa-1-Small
Bc-AI 
posted an update 26 days ago
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113
I have begun training a new LLM on a Single RTX 6000 Pro Blackwell GPU on MoLab free notebooks. This model is a 10B parameter model designed for coding tasks named CodVa-Large. Please expect a launch in a few months! Meanwhile, our CodVa-Small model is wrapping up pretraining and will launch in the coming weeks. Nova-1-Standard is complete as is and we will launch Large very soon.
Shrijanagain 
updated a model about 1 month ago
Shrijanagain 
updated a model about 1 month ago
wop 
posted an update about 1 month ago
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186
# One Script, Every Benchmark

Every Bench Labs benchmark had its own eval files. Now there's one script, hosted in the leaderboard Space:

curl -sLO https://huggingface.co/spaces/bench-labs/BenchLabs-Leaderboard/resolve/main/script.py
pip install torch transformers
python script.py --model your/model


It runs the full suite with the official scoring — Effortless (exact-match), Easy (hybrid category-aware), Mid (loglikelihood: acc, acc_norm, soft_score_norm) — and reports every category and subcategory, not just one number.

Output includes leaderboard.json: a ready-to-paste models.json entry. Run the script, paste it, open a PR on the [leaderboard]( bench-labs/BenchLabs-Leaderboard). Done.
bench-labs
  • 30 replies
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wop 
posted an update about 1 month ago
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3661
# PixelModel v1

Last month we released PixelModel — a neural network whose weights are literally the pixels of a PNG. It was a toy: 202,752 parameters, welded to 32×32 output, trained on six solid-color swatches. It scored FID 566.84 on the Tiny-T2I-Leaderboard, mostly by producing the same yellow noise for every prompt.

Today we're releasing PixelModel v1. It is 8.5× smaller — 23,747 parameters — and it beats v0 on both benchmark metrics while being trained on 20,000 real MS-COCO caption/image pairs instead of six color swatches. The entire model now fits in a 160×149 PNG.

That image is not a visualization of the model. It is the model. All 23,747 weights, one per pixel.

## links
bench-labs
Blog post [read it here ⇗]( bench-labs/blog)
See us on the [Leaderboard ⇗]( FlameF0X/Tiny-T2I-Leaderboard)
Model card [here]( bench-labs/pixelmodel-v1)

## The catch
A 23K-parameter model does not draw sandwiches. With ~1 parameter per training image, the loss-minimizing behavior is to output the average of all plausible images for a caption — caption-conditioned color, light, and layout statistics. Food prompts come out warm and brown; sky prompts come out cool and bright. That is the ceiling for this size class, and we'd rather show it than crop around it.

# cherry on top 🍒
The model generates 600 images (cpu) in 5 (five) seconds.
Thats 5000 images in 24 seconds on cpu.
The model trained on cpu for just 30 minutes.
Shrijanagain 
updated a collection about 1 month ago
Shrijanagain 
updated a dataset about 1 month ago
Shrijanagain 
published a dataset about 1 month ago
Bc-AI 
posted an update about 1 month ago
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500
we are going to release our latest NOVEL model next! it's called Nova-1-EXP and will be launched as private preview in the smilyai Laboratories BETA TESTERS organisation.
  • 3 replies
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PhysiQuanty 
posted an update about 1 month ago
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4869
🧠 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
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ST-x-Tony 
updated a collection about 1 month ago