This model was converted to MLX format from huihui-ai/Huihui-gemma-4-26B-A4B-it-abliterated using BaseQuant_XL 8-bit mixed quantization — the maximum-fidelity XL variant. The vision encoder is preserved (full multimodal).
BaseQuant_XL keeps the most routing-critical layers in full bf16 precision — the MoE router.proj (gate) and the mlp (shared/dense expert that processes all tokens) — while applying 8-bit quantization to everything else. Compared to the 6-bit XL variant, only the routed experts (experts.switch_glu) move from 6-bit to 8-bit; all other layer assignments are identical.
This is the abliterated (uncensored) version of Gemma-4-26B-A4B, a 25.2B-parameter MoE (Mixture of Experts) model with 128 experts per layer (avg. 3.8B active parameters per token) and a vision encoder. The unquantized BF16 base was used as the source for maximum fidelity.
Note: tie_word_embeddings=True means there is no separate lm_head (tied to embed_tokens).
About XL Quantization
BaseQuant_XL is a fully data-agnostic, static quantization. No calibration dataset, no sensitivity analysis, no importance matrix. Precision is allocated purely by architectural role — routing-critical layers get higher precision, bulk expert parameters get lower precision. The result is a transparent, faithful capture of the source model.
Data-dependent calibration quantizations (iMatrix, AWQ, GPTQ, oQ, oQ4e, etc.) use a calibration set to guide bit allocation. This can produce a skewed representation of the model: domains well-represented in the calibration data (English, popular topics, public or leaked benchmarks) are preserved better, while underrepresented domains (non-English languages, niche use cases, your own data) are preserved worse. XL avoids this trade-off entirely — it generalizes honestly because it is never fit to any particular data distribution.
