Repo	GGUF Equivalent	Size	Decode (tok/s)	Speedup vs BF16
Brooooooklyn/Qwen3.5-9B-UD-Q2_K_XL-mlx	UD-Q2_K_XL	5 GB	TBD	TBD
Brooooooklyn/Qwen3.5-9B-UD-Q3_K_XL-mlx	UD-Q3_K_XL	6 GB	TBD	TBD
Brooooooklyn/Qwen3.5-9B-UD-Q4_K_XL-mlx	UD-Q4_K_XL	8 GB	TBD	TBD
Brooooooklyn/Qwen3.5-9B-UD-Q5_K_XL-mlx	UD-Q5_K_XL	9 GB	TBD	TBD
Brooooooklyn/Qwen3.5-9B-UD-Q6_K_XL-mlx	UD-Q6_K_XL	9 GB	TBD	TBD
Brooooooklyn/Qwen3.5-9B-UD-Q8_K_XL-mlx	UD-Q8_K_XL	10 GB	TBD	TBD

Benchmarked on Apple M3 Max 128GB, multi-turn chat (Turn 4 decode, steady-state).

Per-Tensor Bit Assignments (N=2)

Weight	Bits	Rationale
`embed_tokens`	4-bit	KLD ~0.15 — very low sensitivity
`lm_head`	5-bit	KLD ~0.05 — safest tensor
`self_attn.q/k/v_proj`	4-bit + AWQ	KLD ~1.5–2.9, AWQ via layernorm
`linear_attn.in_proj_qkv/z`	4-bit + AWQ	KLD ~2.9, AWQ via layernorm
`self_attn.o_proj`	bf16	NOT AWQ-correctable
`linear_attn.out_proj`	bf16	KLD ~6.0 — worst tensor
`down_proj`	3-bit	"Slightly more sensitive"
`gate_proj`, `up_proj`	2-bit	"Generally ok" at low bits

Quantization Strategy

Based on Unsloth Dynamic 2.0 per-tensor KLD analysis. Sensitive layers get higher bits with AWQ correction, while FFN weights are aggressively quantized. imatrix AWQ pre-scaling amplifies important weight channels and fuses inverse scales into preceding layer norms (zero inference overhead).

AWQ-correctable projections (q/k/v, in_proj_qkv/z) are quantized at 4-bit via input_layernorm. Non-AWQ-correctable projections (o_proj, out_proj) are kept at bf16.

Usage

typescript

import { loadModel } from '@mlx-node/lm';

const model = await loadModel('./Qwen3.5-9B-UD-Q2_K_XL-mlx');

const result = await model.chat(
  [{ role: 'user', content: 'Explain the hybrid attention mechanism in Qwen3.5.' }],
  { maxNewTokens: 2048, temperature: 0.6, enableThinking: false },
);
console.log(result.text);

How It Was Made

bash

mlx convert \
  -i Qwen3.5-9B \
  -o Qwen3.5-9B-UD-Q2_K_XL-mlx \
  -q --q-bits 2 --q-recipe unsloth \
  --imatrix-path imatrix_unsloth.gguf

Acknowledgments

Unsloth — Quantization strategy based on their per-layer KLD benchmarks and Dynamic 2.0 methodology
Qwen Team — For the Qwen3.5 model family
Apple MLX — For the Metal-accelerated ML framework

License

Apache 2.0 (inherited from base model).

Brooooooklyn/Qwen3.5-9B-UD-Q2_K_XL-mlx

Qwen3.5-9B — UD-Q2_K_XL (mlx-node)

All Variants