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

Libraries

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

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

pipe = pipeline("image-text-to-text", model="SC117/Nex-N2-mini-APEX-GGUF")

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use SC117/Nex-N2-mini-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/Nex-N2-mini-APEX-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf SC117/Nex-N2-mini-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/Nex-N2-mini-APEX-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf SC117/Nex-N2-mini-APEX-GGUF:BF16

Build from source code

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

Use Docker

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

Llama new
LM Studio
Jan

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SC117/Nex-N2-mini-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/Nex-N2-mini-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/Nex-N2-mini-APEX-GGUF:BF16

SGLang

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

How to use SC117/Nex-N2-mini-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/Nex-N2-mini-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/Nex-N2-mini-APEX-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

How to use SC117/Nex-N2-mini-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/Nex-N2-mini-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/Nex-N2-mini-APEX-GGUF:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use SC117/Nex-N2-mini-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/Nex-N2-mini-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/Nex-N2-mini-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

Nex-N2-mini

📖 中文文档

Agentic Vision MoE — APEX Quantized GGUF

⚡ Thinking Mode Requires Nex's Patched llama.cpp

👉 Using stock llama.cpp (without Nex's patch)? → SC117/Nex-N2-mini-template-fix-APEX-GGUF — works out of the box, no --chat-template-file needed

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 --reasoning-format.

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.

💡 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
`Nex-N2-mini.BF16.gguf`	64.6 GB	16.0	Full precision reference
`Nex-N2-mini-APEX-Quality.gguf`	21.3 GB	5.23	Highest quality, best accuracy
`Nex-N2-mini-APEX-Balanced.gguf`	23.6 GB	5.85	Best all-rounder, recommended
`Nex-N2-mini-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)

🧠 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 (see above)

🚀 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 Nex-N2-mini-APEX-Quality.gguf \ -ngl 99 -ncmoe 19 -c 32768 \ --host 0.0.0.0 --port 8081

Replace Nex-N2-mini-APEX-Quality.gguf with your preferred quantization tier. 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).

Using with Stock llama.cpp

If you cannot use Nex's patched llama.cpp, a template-fixed version is available at SC117/Nex-N2-mini-template-fix-APEX-GGUF. These GGUFs have a modified chat_template.jinja embedded so that --reasoning-format works on stock llama.cpp without --chat-template-file.

⚠️ The Nex team explicitly advises against modifying the chat template — the model was trained strictly on the original template, and deviating from the training-time format may degrade output quality. See discussion #3. Use the template-fixed version only if you have no alternative, and be aware of the potential quality trade-off.