Ternary-Bonsai-27B-mlx-2bit

모델 소개

Prism ML Website  |  Whitepaper  |  Demo & Examples  |  Discord > **\~9.4x** smaller than FP16 (ideal) | **95%** of FP16 intelligence retained | **\~26 tok/s** on an Apple M5 Pro laptop - **\~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

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。

자주 묻는 질문

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。