Ternary-Bonsai-27B-mlx-2bit

Prism ML Website  |  Whitepaper  |  Demo & Examples  |  Discord Ternary Bonsai 27B Full 27B-class reasoning in ternary transformer weights — on everyday laptops **\~9.4x** smaller than FP16 (ideal) | **95%** of FP16 intelligence retained | **\~26 tok/s** on an Apple M5 Pro laptop Highlights **\~7.2 GB** deployed footprint (down from \~54 GB FP16) — full 27B-class reasoning on a standard laptop or a single GPU **95% of FP16 intelligence retained**: 80.49 average across 15 thinking-mode benchmarks — a *higher* score than the conventional IQ2_XXS build (72.73) at less than two-thirds of its footprint **Retains thinking, reasoning, and agentic behavior** deep in the sub-4-bit regime, where conventional low-bit representations collapse: math within two points of full precision (93.40), coding at 85.96, agentic tool use at 74.01 **End-to-end ternary language weights** across embeddings, attention projections, MLP projections, and LM head, at a *true* 1.71 bits per weight — no high-precision escape hatches behind a low-bit label; the vision tower ships in compact 4-bit HQQ **262K-token context** on-device, kept practical by the Qwen3.6-27B hybrid-attention backbone (\~75% linear attention) and 4-bit KV-cache quantization **Custom 2-bit hybrid-attention kernels** on Apple MLX (Python, Swift) and CUDA — packed weights are consumed directly, never expanded back to FP16 **Ships with a DSpark speculative-decoding drafter layer** trained against the Bonsai 27B target — a lossless **1.34x** decode speedup on the CUDA serving path **1-bit companion**: also available as 1-bit Bonsai 27B (\~3.9 GB), the phone-class operating point that fits an iPhone 17 Pro Max Resources **Whitepaper** — full methodology, benchmarks, and measurement notes **Demo & examples** — serving, benchmarking, and integrating Bonsai **Low-bit kernels**: MLX fork (Apple Silicon) · mlx-swift fork (iOS/macOS) · llama.cpp fork (CUDA) **Discord** — join the community for support, d