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Brooooooklyn/Qwen3.5-27B-UD-Q2_K_XL-mlx

sourceHugging Faceapache-2.0updated 6mo agoView on Hugging Face
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Model Card

Qwen3.5-27B — UD-Q2KXL (mlx-node)

2-bit base mixed-precision quantization of Qwen/Qwen3.5-27B for Apple Silicon, using the **Unsloth Dynamic** quantization strategy via mlx-node.

Original (BF16)This Model
Size~51 GB15 GB
FormatSafeTensors (sharded)SafeTensors (single file)
PrecisionBF16 uniformMixed 2/3/4/5/8-bit + BF16

All Variants

Per-Tensor Bit Assignments (N=2)

WeightBitsRationale
embed_tokens4-bitKLD ~0.15 — very low sensitivity
lm_head5-bitKLD ~0.05 — safest tensor
self_attn.q/k/v_proj4-bit + AWQKLD ~1.5-2.9, AWQ via layernorm
linear_attn.in_proj_qkv/z4-bit + AWQKLD ~2.9, AWQ via layernorm
self_attn.o_projbf16NOT AWQ-correctable
linear_attn.out_projbf16KLD ~6.0 — worst tensor
down_proj3-bit"Slightly more sensitive"
gate_proj, up_proj2-bit"Generally ok" at low bits

Based on Unsloth Dynamic 2.0 per-tensor KLD analysis with imatrix AWQ pre-scaling.

Usage

typescript
import {{ loadModel }} from '@mlx-node/lm';
const model = await loadModel('./Qwen3.5-27B-UD-Q2_K_XL-mlx');
const result = await model.chat(
  [{{ role: 'user', content: 'Hello!' }}],
  {{ maxNewTokens: 2048, temperature: 0.6, enableThinking: false }},
);
console.log(result.text);

How It Was Made

bash
mlx convert -i Qwen3.5-27B -o Qwen3.5-27B-UD-Q2_K_XL-mlx -q --q-bits 2 --q-recipe unsloth --imatrix-path imatrix_unsloth.gguf

Acknowledgments

  • —[Unsloth](https://unsloth.ai) — Per-layer KLD benchmarks and Dynamic 2.0 methodology
  • —[Qwen Team](https://huggingface.co/Qwen) — Qwen3.5 model family
  • —[Apple MLX](https://github.com/ml-explore/mlx) — Metal-accelerated ML framework

License

Apache 2.0 (inherited from base model).