APEX Vision Agentic Abliterated

Huihui-Nex-N2-mini

Abliterated Agentic Vision MoE — APEX Quantized GGUF

⚡ Thinking Mode Requires Nex's Patched llama.cpp

Nex-N2-mini's original chat template uses complex vision processing macros that stock llama.cpp's Jinja parser cannot handle correctly. This causes thinking tags to not be injected, breaking thinking mode output.

The official fix: Use Nex's patched llama.cpp, which works with the unmodified GGUF and unmodified template. Once Nex's upstream patch is merged into stock llama.cpp, this workaround will no longer be needed.

⚠️ Do NOT modify chat_template.jinja. The model was trained strictly on the current template — editing the tags deviates from the training-time format and may degrade output quality. See discussion #3.

⚠️ Abliterated Model — Use at Your Own Risk

This is an abliterated (uncensored) version of Nex-N2-mini created by huihui-ai. Safety filtering has been significantly reduced.

May generate sensitive, controversial, or inappropriate content
Not suitable for public settings, underage users, or production use
Users are solely responsible for compliance with local laws and ethical standards
Recommended for research, testing, or controlled environments only

Original model: huihui-ai/Huihui-Nex-N2-mini-abliterated

💡 What is APEX?

These GGUF files are quantized using APEX, a MoE-aware mixed-precision quantization technique that outperforms standard quantization methods while being significantly smaller.

APEX beats Q8_0 perplexity at half the size — and even beats F16.

APEX classifies every tensor by its role — routed expert, shared expert, or attention — and applies a layer-wise precision gradient, giving the most sensitive edge layers higher precision and compressing the redundant middle layers more aggressively.

📦 Available Files

File	Size	BPW	Note
`Huihui-Nex-N2-mini-abliterated.BF16.gguf`	64.6 GB	16.0	Full precision reference
`Huihui-Nex-N2-mini-abliterated-APEX-Quality.gguf`	21.3 GB	5.23	Highest quality, best accuracy
`Huihui-Nex-N2-mini-abliterated-APEX-Balanced.gguf`	23.6 GB	5.85	Best all-rounder, recommended
`Huihui-Nex-N2-mini-abliterated-APEX-Compact.gguf`	15.4 GB	3.81	Best quality/size ratio, 16GB VRAM
`mmproj-Nex-N2-mini.F16.gguf`	858 MB	-	Vision projector (required for image/video)
`chat_template.jinja`	7.9 KB	-	Original unmodified chat template

🧠 Model Details

Architecture	Qwen3.5 MoE (GatedDeltaNet + Full Attention) + Vision Encoder
Parameters	35B total, 3B active per token
Experts	256 routed experts, 8 active per token
Layers	40 layers (30 linear_attn + 10 full_attn)
Context	262,144 tokens
Vision	Image support (mmproj 858MB)
Thinking	Qwen3-style think tags — requires Nex's patched llama.cpp
Modification	Abliterated by huihui-ai (safety filters removed)

🚀 Usage

Download Nex's patched llama.cpp

Binaries: nex-agi/llama.cpp | Docker: ghcr.io/nex-agi/llama.cpp:server-cuda-nex-b9596-fix-b9598-8c0d5c9

./llama-server \ -m Huihui-Nex-N2-mini-abliterated-APEX-Quality.gguf \ -ngl 99 -ncmoe 19 -c 32768 \ --host 0.0.0.0 --port 8081

Replace Huihui-Nex-N2-mini-abliterated-APEX-Quality.gguf with your preferred quantization tier (Quality / Balanced / Compact). Add --mmproj mmproj-Nex-N2-mini.F16.gguf for vision. Recommended sampling: temperature 0.7, top_p 0.95, top_k 40, min_p 0.

📋 Original Model Benchmarks

Benchmark	Score	Category
BrowseComp	74.1	Agent
SWE-Bench Verified	74.4	Coding
Terminal-Bench 2.1	60.7	Coding
GPQA Diamond	82.6	Reasoning
IFEval	89.1	Instruction

From the original Nex-N2-mini model card (BF16, full precision). Abliteration does not significantly change benchmark scores.

SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF

Huihui-Nex-N2-mini

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