Bonsai-2-27B-Ternary-JANG
JANG-affine bundle of prism-ml/Ternary-Bonsai-2-27B-mlx-2bit, Prism ML's ternary Bonsai 2 27B (built on Qwen3.8-27B). The language model is a lossless repack of Prism's ternary weights: every 2-bit code, scale and bias is carried over unchanged after validation. This is proper affine JANG storage, not JANGTQ, MXTQ, or a codebook sidecar format.
OsaurusAI · osaurus.ai · JANG source
Bundle
| Property | Value |
|---|---|
| Architecture | Dense Qwen3.8-27B conditional-generation VLM (64 blocks: 48 GatedDeltaNet + 16 full attention) |
| JANG profile | JANG_AFFINE_TERNARY_2BIT |
| Text matrices | ternary {−s, 0, +s} in 2-bit slots, group size 128, exact |
| Weight basis | blockwise Hadamard rotation (block 1024, explicit signs), applied to activations at runtime |
| Vision linears | 6-bit affine, group size 128 |
| Norms and recurrent-state tensors | float32 passthrough (as in the source) |
| Weight shards | 7.64 GiB |
| Context | 262,144 tokens |
| Modalities | text, image, video |
| Audio | not supported |
Embeddings, the untied language-model head, full-attention projections, GatedDeltaNet projections, and MLP matrices are ternary. Bonsai is dense: it has no routed experts or router tensors. The ternary codes decode to exactly the source {−scale, 0, +scale} groups.
The bundle contains the original tokenizer, tokenizer config, the Qwen3.8 chat template (thinking, reasoning_effort, tools), image and video processor configs, the Prism hadamard.json sidecar, source license and notice. EOS metadata is normalized to <|im_end|> (248046).
Runtime
The language model is stored in a Hadamard-rotated basis. Stock mlx_lm / mlx_vlm loaders return wrong output silently because they skip the activation transform. Use Osaurus or a vMLX build with JANG Hadamard support (osaurus.json names the minimum Osaurus version); the loader applies the transform from the bundle's declared contract and refuses to load if any sign vector is missing.
vmlx serve OsaurusAI/Bonsai-2-27B-Ternary-JANG --host 127.0.0.1 --port 8000
OpenAI-compatible chat requests support text, image_url, and video_url content parts, tool definitions, and chat_template_kwargs for enable_thinking and reasoning_effort (low, medium, xhigh; default xhigh).
Sampling defaults follow the Qwen3.8 card that Prism also recommends: thinking mode temperature 1.0, top_p 0.95, top_k 20; instruct mode temperature 0.7, top_p 0.80, top_k 20, presence_penalty 1.5.
Verification
Verified on 2026-09-17 through the vMLX Python server on an Apple M5 Max with 128 GB unified memory.
| Gate | Result |
|---|---|
| Logit parity vs Prism's reference loader | PASS — argmax agreement 1.0 at every position on 4 prompts, identical greedy continuations |
| Single-turn text, thinking off | PASS — Paris |
Thinking on (reasoning_effort=medium) | PASS — closed think block, correct 391 |
| Multi-turn | PASS — exact ORCHID-4729 recall and combination |
| Long context | PASS — buried fact recalled from a 10,655-token prompt |
| Image | PASS — red background with centered blue square; green circle plus exact OCR of overlaid text |
| Video | PASS — red frames followed by blue frames |
| Tool calling | PASS — get_weather call emitted and tool result folded into the final answer |
The conversion report is included as jang_affine_report.json; authoritative per-tensor storage metadata is in jang_config.json.
Quantization notes
- 402 language-model modules (embedding, 64 layers, untied head) are the source ternary codes, scales and biases, copied without re-quantization. There is no full-precision source for these weights, so AWQ, imatrix and GPTQ do not apply.
- 83 eligible vision linears use native 6-bit affine storage;
blocks.N.mlp.linear_fc2(input 4304) and the patch/position embeddings stay float16. - 699 norms, GatedDeltaNet state projections, convolutions, biases and incompatible vision tensors pass through in their source precision.
- No
tq_packed,tq_norms,mxtq_bits, orjangtq_runtime.safetensorsartifacts are present.
License and attribution
Apache-2.0. See LICENSE and NOTICE.txt. This repository is a repacked conversion of the linked Prism ML Bonsai 2 checkpoint; the ternary weights are Prism ML's work.