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

모델 소개

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)

주요 특징

特点摘要待依据来源补齐，不凭模型名称推断能力。

한계 및 유의 사항

구체적 한계를 판단할 출처가 부족합니다. 실제 성능, 지연, 데이터 정책, 라이선스와 버전 수명을 확인하세요.

출처별 요금 및 비용 분석

요금은 채널, 지역, 설정에 따라 다릅니다. 각 채널 요금은 개별 표시되며 제공사의 통일 공식 요금이 아닙니다.

공개 평가 및 실행 설정

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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출처 및 추적 기록

许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。