Huihui-Nex-N2-mini-abliterated-5bit-XL-mlx

leonsarmiento/Huihui-Nex-N2-mini-abliterated-5bit-XL-mlx

This model was converted to MLX format from huihui-ai/Huihui-Nex-N2-mini-abliterated using BaseQuant_XL 5/8-bit mixed quantization optimized for Apple Silicon. The vision encoder is preserved and quantized at 5-bit, making this a full multimodal model.

BaseQuant_XL keeps the most routing-critical layers in full bf16 precision — the MoE router gate, shared expert gate, shared expert, and lm_head — while applying aggressive quantization to the bulk parameters. This preserves routing accuracy and output quality where it matters most.

This is the abliterated (uncensored) version of Nex-N2-mini, a 35B-parameter MoE (Mixture of Experts) model fine-tuned from Qwen3.5-35B-A3B-Base by Nex-AGI, featuring 256 experts (8 active per token + 1 shared expert), hybrid full + linear (Gated DeltaNet) attention, an "Agentic Thinking" framework (Adaptive Thinking + Coherent Thinking), and a vision encoder for multimodal input. Despite 35B total parameters, only ~3B are activated per token for efficient inference.

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.

Use with mlx

pip install -U mlx-vlm

python -m mlx_vlm.generate --model leonsarmiento/Huihui-Nex-N2-mini-abliterated-5bit-XL-mlx --max-tokens 256 --temperature 0.7 --top-p 0.95 --prompt "Hello"

BaseQuant_XL Quantization Strategy

Quantization Details

• Quantization type: BaseQuant_XL mixed (multimodal, vision preserved)
• Bits per weight: 5.881
• Total size: ~24 GB (5 shards)
• Group size: 64
• Method: Custom quant_predicate via mlx_vlm

Recommended Inference Parameters

Note: This is a Qwen3.5-based model — preserve_thinking is not applicable.