MODEL PROFILE

Qwen3.8-27B-Ternary-Bonsai-2-DFlash2-MLX

nathansutton · Qwen3.8-27B-Ternary-Bonsai-2-DFlash2-MLX

Prism ML's ternary `Ternary-Bonsai-2-27B` build of `Qwen/Qwen3.8-27B`, repacked for chad, a Claude-Code-style local coding agent for Apple Silicon, **with its speculative decoder bundled in**. This is chad's default model. Created using Bonsai by Prism ML. One download gets both halves: the ternary target model and the block drafter it decodes with, already quantized. Nothing is built on first run. Every projection of Qwen3.8-27B (a dense `qwen3_5` hybrid: 64 layers, 48 GatedDeltaNet + 16 full attention) is stored in a **Hadamard-rotated basis**: multiplied by a fixed sign vector and put through a blockwise Walsh-Hadamard transform offline, then quantized to 2-bit affine group-128 whose three levels reproduce the ternary set {−s, 0, +s}. The rotation costs no extra bits and no extra weight traffic, but the matching transform must be applied to the activations at runtime and inverted after the embedding lookup. **An ordinary MLX affine loader finds tensors of exactly the right shapes, skips both transforms, and produces plausible garbage without raising.** chad routes the pack on its `model_type` (`prism_hadamard_qwen35`) to a loader that does the rotation; mlx-lm alone will not run it correctly.

Text generationlicense:apache-2.0
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/11/2 19:16:54
OVERVIEW

Model overview

Prism ML's ternary `Ternary-Bonsai-2-27B` build of `Qwen/Qwen3.8-27B`, repacked for chad, a Claude-Code-style local coding agent for Apple Silicon, **with its speculative decoder bundled in**. This is chad's default model. Created using Bonsai by Prism ML. One download gets both halves: the ternary target model and the block drafter it decodes with, already quantized. Nothing is built on first run. Every projection of Qwen3.8-27B (a dense `qwen3_5` hybrid: 64 layers, 48 GatedDeltaNet + 16 full attention) is stored in a **Hadamard-rotated basis**: multiplied by a fixed sign vector and put through a blockwise Walsh-Hadamard transform offline, then quantized to 2-bit affine group-128 whose three levels reproduce the ternary set {−s, 0, +s}. The rotation costs no extra bits and no extra weight traffic, but the matching transform must be applied to the activations at runtime and inverted after the embedding lookup. **An ordinary MLX affine loader finds tensors of exactly the right shapes, skips both transforms, and produces plausible garbage without raising.** chad routes the pack on its `model_type` (`prism_hadamard_qwen35`) to a loader that does the rotation; mlx-lm alone will not run it correctly.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Frequently asked questions

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Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。