Bonsai-27b-Ternary-JANG
Ternary 2-bit-slot JANG-affine conversion of prism-ml/Ternary-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_TERNARY_2BIT |
| Text matrices | ternary 2-bit slots, group size 128 |
| Vision linears | 4-bit affine, group size 64 |
| Norms and state tensors | float16 passthrough |
| Weight shards | 7.477 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. Ternary codes represent the source {-scale, 0, +scale} groups directly with affine scales and biases.
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-Ternary-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.