Bonsai-2-27B-1.75bit-JANG

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Bonsai-2-27B-1.75bit-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). This is the dense-packed edition: the same exact ternary weights as Bonsai-2-27B-Ternary-JANG, stored at Prism's PTQ1_0 density (1.75 bits/weight: 5 trits per byte plus one fp16 scale per 128-weight group, no stored bias) and expanded losslessly into MLX's native 2-bit affine kernels at load time. Output is bit-identical to the ternary bundle; only the download shrinks. Proper affine JANG storage, not JANGTQ, MXTQ, or a codebook sidecar format.

OsaurusAI · osaurus.ai · JANG source

Bundle

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 and ternary-packed support (osaurus.json names the minimum Osaurus version); the loader expands the packed trits (codes and scales unchanged, biases materialized as −scale), applies the activation transform from the bundle's declared contract, and refuses to load if any module or sign vector is missing. Runtime memory and speed equal the ternary bundle.

vmlx serve OsaurusAI/Bonsai-2-27B-1.75bit-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.

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 and scales, packed 5 trits per byte (26 bytes per 128-group) without re-quantization; biases are always −scale and are not stored. 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, or jangtq_runtime.safetensors artifacts 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.