CoolFace
Modelpublic

jclyons52/Qwen3.8-27B-UD-Q2_K_XL-MLX-imatrix-direct

sourceHugging Faceapache-2.0updated 1mo agoView on Hugging Face
0likes560downloads
Model Card

Qwen3.8-27B UD-Q2KXL — MLX imatrix-direct (native MLX, dynamic quant)

Native-MLX port of unsloth's UD-Q2_K_XL dynamic quantization for Qwen/Qwen3.8-27B. Runs the quantizer search natively in MLX's affine format (group size 64) and writes final MLX weights directly — no GGUF round-trip, no mlx_lm.convert re-quantization.

Perplexity (wikitext-2-raw first 32k tokens, 512-token windows):

BuildPPL ↓
unsloth UD-Q2KXL GGUF in llama.cpp7.180
this model9.133

Note: at 2 bits the gap to the GGUF widens (+1.95) because unsloth's 2-bit quants use non-linear IQ codebooks that MLX's affine kernels cannot represent (details).

Method

  1. 1.Bit map from unsloth's Qwen3.8-27B-UD-Q2_K_XL.gguf (dynamic per-tensor bit allocation).
  2. 2.Importance-weighted affine search per group using unsloth's published imatrix (per-column activation energies).
  3. 3.Final MLX weights emitted directly (packed uint32 codes + bf16 scales/biases) — the emitted weights are exactly what the search chose.

Limitations

  • —Affine vs IQ-codebook gap. MLX quantized matmul supports only uniform affine grids; unsloth's ≤3-bit GGUFs use non-linear IQ codebooks. The gap vs GGUF is inherent to MLX's current kernels, not the weights.
  • —Text-only. The base checkpoint's visual tower is not included.
  • —Eval scope: wikitext-2, 32k tokens only; downstream tasks uncharacterized.
  • —Not for further training.

Calibration data

Importance weighting: unsloth's published imatrix_unsloth.gguf.

Credits

Built 2026-08-28. Base model license inherited from Qwen/Qwen3.8-27B.