Ternary-Bonsai-2-27B-mlx-2bit

Prism ML Website  |  Whitepaper  |  Demo & Examples  |  Discord > **8.60 GB** on disk, language model + vision tower | **98.2%** of FP16 intelligence retained | **\~47 tok/s** on an Apple M5 Max laptop - **8.60 GB** on disk: a 7.67 GB language model (down from \~54 GB FP16) plus the 0.92 GB vision tower. MLX's container stores a scale and a bias per group, so the language model costs 2.25 bits/weight where the same ternary weights take 1.75 in the GGUF PTQ1_0 packing - **98.2% of FP16 intelligence retained**: 84.78 average across 14 thinking-mode benchmarks — far above the conventional IQ2_XXS build (72.59) at less than two-thirds of its footprint, and within 0.4 points of UD-Q4_K_XL at three times the footprint - **Retains thinking, reasoning, and agentic behavior** deep in the sub-4-bit regime, where conventional low-bit representations collapse: math within half a point of full precision (96.57), coding level with the baseline (89.42), agentic tool calling at 74.92 - **End-to-end ternary language weights** across embeddings, attention projections, MLP projections, and LM head, with no high-precision escape hatches behind a low-bit label — a *true* 1.72 bits per weight as a representation, 2.25 as MLX stores it; the vision tower is bundled in this pack, unquantized - **262K-token context** on-device, kept practical by the Qwen3.8-27B hybrid-attention backbone (\~75% linear attention) - **Custom ternary hybrid-attention kernels** on Apple MLX (Python, Swift) and CUDA — packed weights are consumed directly, never expanded back to FP16 - **GGUF companion**: also available as Ternary-Bonsai-2-27B-gguf for llama.cpp (CUDA, Metal, CPU), in two packings — PTQ1_0 (5.95 GB) and PQ2_0 (7.21 GB)

번역 준비 중입니다. 원문을 표시합니다.