Instructions to use SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF:BF16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF:BF16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF:BF16

Use Docker

docker model run hf.co/SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF:BF16

LM Studio
Jan

vLLM

How to use SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF:BF16

SGLang

How to use SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF with Ollama:
```
ollama run hf.co/SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF:BF16
```
Unsloth Desktop

How to use SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF:BF16

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF with Docker Model Runner:
```
docker model run hf.co/SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF:BF16
```

Lemonade

How to use SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF:BF16

Run and chat with the model

lemonade run user.Huihui-Nex-N2-mini-abliterated-APEX-GGUF-BF16

List all available models

lemonade list

Hermes Agent

How to use SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF:BF16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF:BF16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "SC117/Huihui-Nex-N2-mini-abliterated-APEX-GGUF:BF16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

APEX Vision Agentic Abliterated

Huihui-Nex-N2-mini

📖 中文文档

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