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liked a dataset 9 days ago
nvidia/simready-assets
reacted to SeaWolf-AI's post with 🚀 9 days ago
No GPU. No cloud. Just your phone. Most “small” models still beg for a datacenter. Ours fit in your pocket. VIDRAFT POCKET is a family of GGUF models that run on an iPhone and on a GPU-free PC, straight out of the box with llama.cpp, Ollama, LM Studio, and PocketPal. Seven weeks after launch they crossed 1 million downloads. Today the family is past 2.81 million and still climbing, every day. Three models, one pocket: 🟢 POCKET-35B — a 35B mixture-of-experts that runs on an iPhone and on a PC with no GPU. Balanced power. → https://huggingface.co/FINAL-Bench/POCKET-35B-GGUF 🔵 POCKET-26B — a Gemma-4 based 26B MoE, tuned for on-device Korean. Lightweight and fast. → https://huggingface.co/FINAL-Bench/POCKET-26B-GGUF 🟣 POCKET-Darwin-180B — a 180B flagship running on a laptop in 4-bit GGUF. The datacenter, on your desk. → https://huggingface.co/FINAL-Bench/POCKET-Darwin-180B-GGUF The whole family lives here: https://huggingface.co/FINAL-Bench Try our models live in the browser: https://huggingface.co/spaces/FINAL-Bench/POCKET-Image-Studio Private by default. Offline by design. Yours to run. #OnDeviceAI #LocalLLM #llamacpp #GGUF #VIDRAFT #POCKET
reacted to tardellirs's post with 🔥 9 days ago
Model cards say what a model is: a fine-tune of X, a quantization of Y. I built Weightprint to check that against the weights. App: https://huggingface.co/spaces/tardellirs/weightprint Data (every pair, and the copy groups): https://huggingface.co/datasets/tardellirs/weightprint It reads the header and a few bytes of every tensor (no downloads) and compares a model with the parent its card declares. 120,976 models so far. Some of what came out: - 7% of the declared fine-tunes we could compare (1,630 of 22,847) hold their parent's weights unchanged. (Corrected from 12%: quants whose values the engine doesn't decode, like bitsandbytes and FP8, had been counted as unchanged. Thanks @dipankarsarkar for the question that caught it.) - 27% of the fine-tunes we could compare are LoRA-style: only the attention and MLP matrices moved, norms and embeddings untouched. That share went from 10% in 2023 to 28% in 2026. - Of 1,668 fine-tunes named abliterated, uncensored or heretic, 53% edit only the matrices that write to the residual stream, the signature of an abliteration. The rest were trained further, merged, or are the parent's weights. - 51% of the weight bytes on the Hub are byte-for-byte repeats of another repo's files. Search any Hub id to see which layers and parts changed against its parent, or compare any two models. A sample per tensor can miss things, and some formats can't be compared yet. If a verdict on a model you know looks wrong, tell me here and I'll check it against the engine. That's what this post is for, before a longer write-up.
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