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Banaxi-Tech

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SLMs, training from scratch, LoRA, TTS, Ternary models

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reacted to wop's post with 🤗 about 6 hours ago

# 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 https://huggingface.co/bench-labs Blog post [read it here ⇗](https://huggingface.co/spaces/bench-labs/blog?post=pixelmodel-v1.html) See us on the [Leaderboard ⇗](https://huggingface.co/spaces/FlameF0X/Tiny-T2I-Leaderboard) Model card [here](https://huggingface.co/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.

updated a model about 6 hours ago

BananaMind/MathBananaMind-1.1

new activity about 6 hours ago

MiniT2I/MiniT2I:Dataset

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Post

841

Introducing BananaMind 2 Nano

BananaMind 2 Nano is the smallest member of the BananaMind 2.0 family — a 10M-parameter language model that shows how much you can squeeze out of a tiny footprint. It uses the family's digit-isolated tokenizer, so it keeps solid arithmetic despite its size, and it's small enough to run just about anywhere.

Trained on 30B tokens in about a day on a single RTX 5070 Ti (16GB), 4096-token context.

Benchmarks:

Average 35.77
ARC Easy 36.20
PIQA 55.98
ARC Challenge 23.38
HellaSwag 27.50

That 35.77 average edges out Pythia-31M (~34.79) at roughly a third the parameters.

Released under Apache 2.0 on Hugging Face: BananaMind/BananaMind-2-Nano — weights, tokenizer, and config included.

Post

112

We're excited to release BananaMind 2 MoE, a new addition to the BananaMind 2 series!
BananaMind 2 MoE is a sparse mixture-of-experts model with 25M total parameters but only 2M active per token, Like the rest of the series, it uses our custom digit-aware BPE tokenizer that keeps every digit isolated, fixing the core arithmetic weakness of our earlier models. It's trained on 30B tokens from FineWeb-Edu, DCLM, Cosmopedia-v2 and FineMath-4+, and outperforms Pythia-31M on average despite activating just 2M parameters.
Check it out at
BananaMind/BananaMind-2-MoE

BananaMind 2 Nano is Coming Next. Already training. Apache 2.0.