Bonsai 27B 1-bit CRACK GGUF
Vision-language · native Q1_0 · Prism llama.cpp
52.50% MMLU-200 logit · 97.81% full HB-320
Native Q1_0 GGUF release of the Bonsai 27B CRACK model for
the Prism llama.cpp fork. This repo
contains the quantized language model and the matching F16 Qwen3VL multimodal
projector.
Files
The projector contains 334 tensors and both temporal patch slices
v.patch_embd.weight and v.patch_embd.weight.1 (temporal_patch_size=2). The
original image and video processor configuration files are included. Image
input is live-tested. Direct video-container input depends on the Prism runtime
surface; extract frames or use a compatible Qwen3VL video client when the CLI
does not accept the container directly. See preprocessor_config.json and
video_preprocessor_config.json for the retained preprocessing metadata.
Runtime
These native low-bit types require the Prism fork:
Text:
Image/VL:
Server:
Verified evaluation
MMLU is the same fixed, stratified 200-question sample for all rows. It uses
next-token A/B/C/D logits with reasoning/thinking disabled; it is not a claim
for the complete official MMLU suite. HB is the complete unsliced 320-case text
run. The live text smoke measured 58.51–58.66
tok/s and the image request measured 58.53
tok/s on an Apple M5 Max using the Prism Metal runtime.
MMLU subject breakdown
HB category breakdown
Architecture and compatibility
- 64 hybrid Qwen-family language blocks (
qwen35 GGUF architecture)
- 27B-class language model plus separate Qwen3VL vision tower
- Temporal patch size 2; image and video preprocessing metadata retained
- Embedded image/video-aware chat template; examples explicitly disable reasoning
- Apache-2.0; see
LICENSE.txt and NOTICE.txt
한국어 안내
이 저장소는 Bonsai 27B CRACK 모델의 네이티브 Q1_0 GGUF
배포본입니다. Prism llama.cpp 포크가 필요하며, 텍스트 모델과 F16 Qwen3VL
멀티모달 프로젝터를 함께 제공합니다. 위 MMLU 점수는 추론을 끈 200문항
로짓 평가이고, HB 점수는 전체 320문항 결과입니다. 이미지 입력은 실제로
검증했으며 비디오 컨테이너 입력은 사용 중인 Prism 클라이언트의 지원 여부를
확인해야 합니다.
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