Ternary-Bonsai-27B-gguf

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Ternary Bonsai 27B — GGUF

Full 27B-class reasoning in ternary transformer weights, for llama.cpp (CUDA, Metal, CPU)

> \~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
• GGUF Q2_0_g128 format with custom 2-bit hybrid-attention kernels for llama.cpp (CUDA, Metal) — 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
• MLX companion: also available as Ternary-Bonsai-27B-mlx-2bit for native Apple Silicon inference
• 1-bit companion: the phone-class operating point (\~3.9 GB) that fits an iPhone 17 Pro Max, published in GGUF as [Bonsa…

Source: https://huggingface.co/prism-ml/Ternary-Bonsai-27B-gguf