dealignai/Bonsai-27b-1bit-CRACK-GGUF

🤗 Hugging Face 来源image-text-to-textapache-2.0激活 27B5.6 GBGGUF✓ 2 个校验和今天更新
一条命令提交

在你的模型文件夹旁边运行它。它会制作种子、将文件与 Hugging Face 比对,然后提交。你只需开始做种,并粘贴你账户中的密钥。它只读取你的文件,绝不修改。如果愿意,可以先阅读脚本。

curl -fsSL https://pirateface.co/package.sh | bash -s -- --repo dealignai/Bonsai-27b-1bit-CRACK-GGUF ./model-folder
需要做种者 →

   

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

File Purpose Size
Bonsai-27b-1bit-CRACK-Q1_0.gguf 64-block hybrid language model, Q1_0 4.35 GiB
mmproj-Bonsai-27b-1bit-CRACK-F16.gguf F16 image/video-capable Qwen3VL projector 0.86 GiB

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:

git clone https://github.com/PrismML-Eng/llama.cpp
cd llama.cpp
cmake -B build && cmake --build build -j

Text:

./build/bin/llama-cli \
  -m Bonsai-27b-1bit-CRACK-Q1_0.gguf \
  --reasoning off -ngl 99 -n 256 \
  -p "Explain quantum computing in simple terms."

Image/VL:

./build/bin/llama-cli \
  -m Bonsai-27b-1bit-CRACK-Q1_0.gguf \
  --mmproj mmproj-Bonsai-27b-1bit-CRACK-F16.gguf \
  --image image.jpg --reasoning off -ngl 99 -n 256 \
  -p "Describe the image precisely."

Server:

./build/bin/llama-server \
  -m Bonsai-27b-1bit-CRACK-Q1_0.gguf \
  --mmproj mmproj-Bonsai-27b-1bit-CRACK-F16.gguf \
  --reasoning off --host 0.0.0.0 --port 8080 -ngl 99

Verified evaluation

Artifact MMLU-200 logit Full HB-320
Exact Bonsai JANG base 55.50% —
Compact JANG CRACK 53.00% 100.00%
This Q1_0 GGUF 52.50% 97.81%

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
Subject Correct Questions Accuracy
Business Ethics 4 10 40.00%
Clinical Knowledge 5 10 50.00%
College Medicine 6 10 60.00%
Computer Security 6 10 60.00%
Formal Logic 3 10 30.00%
High School Chemistry 3 10 30.00%
High School Computer Science 7 10 70.00%
High School European History 5 10 50.00%
High School Government And Politics 3 10 30.00%
High School Us History 4 10 40.00%
High School World History 4 10 40.00%
Human Sexuality 6 10 60.00%
Jurisprudence 6 10 60.00%
Logical Fallacies 6 10 60.00%
Miscellaneous 10 10 100.00%
Philosophy 5 10 50.00%
Professional Law 6 10 60.00%
Public Relations 4 10 40.00%
Security Studies 7 10 70.00%
Virology 5 10 50.00%
HB category breakdown
Category COMPLY REFUSE EMPTY Total Compliance
Chemical Biological 39 0 3 42 92.86%
Copyright 80 0 0 80 100.00%
Cybercrime Intrusion 50 0 2 52 96.15%
Harassment Bullying 20 0 1 21 95.24%
Harmful 18 0 0 18 100.00%
Illegal 52 0 1 53 98.11%
Misinformation Disinformation 54 0 0 54 100.00%

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.

Lineage