BananaMind 2 Pro is training! The current checkpoint (ONLY 20% DONE) GETS #6 On the entire Open SLM Leaderboard. We are going to release the first public preview on August 2-4 (estimated from speed)
Give us a follow to know when it releases!
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reactedtoProCreations'spost with ๐คฏ๐๐ง ๐โค๏ธ๐ฅ๐คโ๐๐3 days ago
We're excited to announce BananaMind 2V, our small vision model series! These models are NOT released yet. We will release them in mid-august! BananaMind 2V will include: BananaMind 2V 256M, the flagship based on BananaMind 2 Pro (BananaMind 2 Pro is not released yet). BananaMind 2V 100M, our mid model, based on BananaMind 2 Medium. BananaMind 2V 50M, our smallest vision model, based on BananaMind 2 Mini. These are currently unreleased and will release in mid-august. Our training will start after BananaMind 2 Pro has finished training.
reactedtoSeaWolf-AI'spost with ๐โ๐๐คโค๏ธ๐6 days ago
๐ฑ POCKET โ a 35-billion-parameter model that runs on your iPhone, and on your PC with no GPU
We're releasing POCKET, VIDRAFT's flagship Darwin-36B-Opus compressed for on-device use. No fork, no CUDA, no cloud โ it runs on stock llama.cpp. It's a sparse Mixture-of-Experts model (256 experts, only 8 active per token), so the file can be large while the work per token stays small. That's what lets a 35B model run on a phone, and generate fast on a CPU with no graphics card.
Measured (POCKET-35B IQ1_M vs Bonsai-27B Q1_0): โข CPU generate (Xeon, 16 threads): 27.0 vs 10.1 tok/s โ 2.69ร faster โข GPU generate (H100): 197 vs 89 tok/s โ 2.22ร faster โข GPU prompt processing (H100): 753 vs 1816 โ 0.41ร (Bonsai wins this one โ MoE prefill wakes every expert, so sparsity stops helping there. We say so.) โข Quality (HellaSwag, 400 q): 61.0% vs 60.0% โ a tie (confidence intervals overlap)
On a real consumer laptop โ MacBook M3 Pro (18 GB) โ POCKET wins every axis, prompt processing included: โข Metal generate: 25.4 vs 12.8 โ 1.99ร โข CPU generate: 13.8 vs 4.4 โ 3.13ร โข Metal prompt: 240.7 vs 73.4 โ 3.28ร
One more quiet fact: the same-size, quality-oriented rival Ternary-Bonsai-27B (7.2 GB) fails to load in upstream llama.cpp at all โ it needs the PrismML fork. POCKET runs on the tools you already have: LM Studio, Ollama, PocketPal, MLX.
