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Bonsai-27b-1bit-JANG
JANGQ-AI/Bonsai-27b-1bit-JANG
Bonsai-27b-1bit-JANG is a image-text-to-text model from JANGQ-AI. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for mlx. The card lists the license as apache-2.0.
<p align="center"<a href="https://huggingface.co/JANGQ-AI"<img src="./jangq-logo-dark.png" alt="JANGQ-AI" width="300"</a</p
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.safetensors4.7 GB · 100%
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How the weights are stored.
U321.2B · 67%
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Model card
From the Hugging Face model README
<p align="center"><a href="https://huggingface.co/JANGQ-AI"><img src="./jangq-logo-dark.png" alt="JANGQ-AI" width="300"></a></p>
<p align="center">
<a href="https://vmlx.net"><img src="vmlx-app.png" alt="vMLX — run JANG models on Apple Silicon" width="820"></a>
</p>
<h3 align="center">⚡ All JANG models are meant to be run in <a href="https://vmlx.net">vMLX</a></h3>
Bonsai-27b-1bit-JANG
Binary 1-bit JANG-affine conversion of prism-ml/Bonsai-27B-unpacked. This is proper affine JANG storage, not JANGTQ, MXTQ, or a codebook sidecar format.
Bundle
| Property | Value |
|---|---|
| Architecture | Dense Qwen3.5 conditional-generation VLM, 27B |
| JANG profile | JANG_AFFINE_1BIT |
| Text matrices | 1-bit codes, group size 128 |
| Vision linears | 4-bit affine, group size 64 |
| Norms and state tensors | float16 passthrough |
| Weight shards | 4.346 GiB |
| Modalities | text, image, video |
| Audio | not supported |
Embeddings, the untied language-model head, full-attention projections, GatedDeltaNet projections, and MLP matrices use the same discrete text profile. Bonsai is dense: it has no routed experts or router tensors. The packed one-bit codes are widened losslessly to native two-bit MLX slots in memory; scales and biases are unchanged.
The bundle contains the original tokenizer vocabulary, tokenizer config, Qwen chat template with thinking and tool support, image processor config, and video processor config. EOS metadata is normalized to <|im_end|> (248046). Source license and notice files are included.
Runtime
Use a vMLX Python build containing schema-2 discrete JANG-affine storage and mixed-precision vision support. Stock mlx_lm does not implement one-bit affine storage and does not honor this bundle's per-module mixed precision.
vmlx serve JANGQ-AI/Bonsai-27b-1bit-JANG --host 127.0.0.1 --port 8000
OpenAI-compatible chat requests support text, image_url, and video_url content parts. The included chat template supports enable_thinking and tool definitions.
Verification
Verified on 2026-07-14 with vMLX Python on an Apple M5 Max with 128 GB unified memory.
| Gate | Result |
|---|---|
| Single-turn text | PASS — coherent Paris answer |
| Multi-turn | PASS — exact ORCHID-4729 recall through three turns |
| Hybrid cache | PASS — paged KV plus SSM companion cache hit |
| Image | PASS — identified red background and centered blue square |
| Video | PASS — identified red frames followed by blue frames |
The conversion report is included as jang_affine_report.json; authoritative per-tensor storage metadata is in jang_config.json.
Quantization notes
- 498 discrete text matrices use the selected binary/ternary affine profile.
- 83 eligible vision linears use native 4-bit affine storage.
- 603 norms, state tensors, convolutions, biases, and incompatible vision tensors remain float16.
- No
tq_packed,tq_norms,mxtq_bits, orjangtq_runtime.safetensorsartifacts are present.
License and attribution
Apache-2.0. See LICENSE, LICENSE.txt, and NOTICE.txt. This repository is a quantized conversion of the linked PrismML Bonsai source checkpoint.
- Task
- Image-Text-to-Text
- Library
- mlx
- Type
- qwen3_5
- License
- apache-2.0
- Created
- Jul 15, 2026
- Updated
- Sep 8, 2026