Yusuf Chowdury

Yusufchy

https://aipulsedaily.vionixai.tech/

AI & ML interests

AI agents, open-weight models, machine learning, MLOps, developer tools, AI automation, and AI-assisted publishing.

Recent Activity

repliedto dronefreak's post 28 minutes ago

🌧️❄️ Free demo: remove rain, raindrops, or snow from a photo with a single model I put together an unofficial demo for **Histoformer** (ECCV 2024, arXiv: 2407.10172), a 16.6M-parameter transformer that handles three different weather degradations, rain streaks, adherent raindrops, and snow, in one unified model. It uses a "histogram self-attention" mechanism that groups pixels by degradation intensity instead of spatial position, which is a neat way to sidestep the usual spatial-window tradeoffs in restoration transformers. Try it here, free on ZeroGPU: https://huggingface.co/spaces/dronefreak/histoformer-weather-restoration Upload a photo and get a before/after slider. Two checkpoints available: one tuned for real-world photos, one for the paper's synthetic benchmarks. Also put together a cleaner, easy-to-use model card with a copy-pasteable Quickstart if you'd rather run it yourself: https://huggingface.co/dronefreak/Histoformer This is an unofficial demo/mirror, not affiliated with the original authors. All credit for the actual research goes to Shangquan Sun, Wenqi Ren, Xinwei Gao, Rui Wang, and Xiaochun Cao (@sunsean). Official repo: https://github.com/sunshangquan/Histoformer. Weights are MIT-licensed. Reported numbers from the paper: 32.1 PSNR on rain+fog (Outdoor-Rain), 33.1 on raindrops, 37.4 / 32.2 on light/heavy snow (Snow100K-S/L).

repliedto maglun's post about 3 hours ago

Inspect model quantization in the browser, without download Curious how models on Hugging Face spend their "bit budget"? A few days ago I shared the first version of a little tool I built out of my own curiosity (and for fun). Since then, thanks in large part to great feedback from people here, a lot has changed. New in the last few days: - GGUF support - handy with all the new great GGUF quants - Decode for AWQ, GPTQ, NF4, mxfp4 packed experts, packed-int32, and additive-codebook formats - Improved comparison view for diffing two quants of the same model - Built-in anonymous report-issue button connecting a report to specific model - plus many small fixes After my first post I got great feedback from several community members, and some issues were fixed within hours. I'm planning an acknowledgments section on the site, and when you report an issue you get a receipt ID you can keep to claim credit later. (reports are anonymous by design; I store no identity, so the receipt hash works like a bearer token for your find) It's still very much a side project I hope others find useful. Explore any HF model in the browser without downloading it, the webpage reads from the safetensors header via a range request, and only tensors you click stream, and large ones are sampled, not downloaded in full. And there is a report button right in the tool when things don’t look right. Feedback very welcome, especially models that break it :) Or ideas on what is missing. https://tensorlens.dev (screenshot attached from viewing one of Unsloth's GGUF quants)

repliedto Banaxi-Tech's post about 21 hours ago

Today we wanted to release BananaMind 2 Pico, our smallest model yet at ~0.9M parameters. Instead, we accidentally ran a very expensive experiment on what happens when you push a tiny model way past its useful token budget. Short version: we trained on 200B tokens (~222K:1 tokens-per-parameter). The model peaked at 20B tokens with an INT Index of 4.55, then degraded monotonically over the next 160B to 3.31 — a 27% regression. Three of four Open SLM benchmarks were worse at the end of training than they were at 10% through. The useful compute-optimal range for Pico-tier models looks like ~22K–30K tokens per parameter. Ratios like 7K:1, 15K:1, and 22K:1 all work fine — TinyStories and most sub-3M community models sit in this range. Push much further and benchmarks start rotting. Follow us for more: https://huggingface.co/BananaMind @vovaRL @Banaxi-Tech Full writeup with all checkpoints, the Chinchilla-ratio control run, and the schedule-vs-overtraining analysis: https://huggingface.co/blog/Banaxi-Tech/ovdadadadd And if anyone, i dont know the reason why you would, wants the 20B token checkpoint reply and ill upload it as BananaMind 2.1 Pico EXP

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