Instructions to use morikomorizz/Nex-N2-Pro-MTP-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use morikomorizz/Nex-N2-Pro-MTP-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="morikomorizz/Nex-N2-Pro-MTP-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("morikomorizz/Nex-N2-Pro-MTP-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use morikomorizz/Nex-N2-Pro-MTP-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 morikomorizz/Nex-N2-Pro-MTP-GGUF:IQ2_XS
# Run inference directly in the terminal:
llama cli -hf morikomorizz/Nex-N2-Pro-MTP-GGUF:IQ2_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf morikomorizz/Nex-N2-Pro-MTP-GGUF:IQ2_XS
# Run inference directly in the terminal:
llama cli -hf morikomorizz/Nex-N2-Pro-MTP-GGUF:IQ2_XS

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 morikomorizz/Nex-N2-Pro-MTP-GGUF:IQ2_XS
# Run inference directly in the terminal:
./llama-cli -hf morikomorizz/Nex-N2-Pro-MTP-GGUF:IQ2_XS

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 morikomorizz/Nex-N2-Pro-MTP-GGUF:IQ2_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf morikomorizz/Nex-N2-Pro-MTP-GGUF:IQ2_XS

Use Docker

docker model run hf.co/morikomorizz/Nex-N2-Pro-MTP-GGUF:IQ2_XS

LM Studio
Jan

vLLM

How to use morikomorizz/Nex-N2-Pro-MTP-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "morikomorizz/Nex-N2-Pro-MTP-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": "morikomorizz/Nex-N2-Pro-MTP-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/morikomorizz/Nex-N2-Pro-MTP-GGUF:IQ2_XS

SGLang

How to use morikomorizz/Nex-N2-Pro-MTP-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 "morikomorizz/Nex-N2-Pro-MTP-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": "morikomorizz/Nex-N2-Pro-MTP-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "morikomorizz/Nex-N2-Pro-MTP-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": "morikomorizz/Nex-N2-Pro-MTP-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use morikomorizz/Nex-N2-Pro-MTP-GGUF with Ollama:
```
ollama run hf.co/morikomorizz/Nex-N2-Pro-MTP-GGUF:IQ2_XS
```
Unsloth Desktop

How to use morikomorizz/Nex-N2-Pro-MTP-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf morikomorizz/Nex-N2-Pro-MTP-GGUF:IQ2_XS

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": "morikomorizz/Nex-N2-Pro-MTP-GGUF:IQ2_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use morikomorizz/Nex-N2-Pro-MTP-GGUF with Docker Model Runner:
```
docker model run hf.co/morikomorizz/Nex-N2-Pro-MTP-GGUF:IQ2_XS
```

Lemonade

How to use morikomorizz/Nex-N2-Pro-MTP-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull morikomorizz/Nex-N2-Pro-MTP-GGUF:IQ2_XS

Run and chat with the model

lemonade run user.Nex-N2-Pro-MTP-GGUF-IQ2_XS

List all available models

lemonade list

Hermes Agent

How to use morikomorizz/Nex-N2-Pro-MTP-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 morikomorizz/Nex-N2-Pro-MTP-GGUF:IQ2_XS

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 morikomorizz/Nex-N2-Pro-MTP-GGUF:IQ2_XS

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use morikomorizz/Nex-N2-Pro-MTP-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf morikomorizz/Nex-N2-Pro-MTP-GGUF:IQ2_XS

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 "morikomorizz/Nex-N2-Pro-MTP-GGUF:IQ2_XS" \
  --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"

Nex-N2-Pro-MTP-GGUF

File size: 4,849 Bytes

---
license: apache-2.0
base_model:
- nex-agi/Nex-N2-Pro
pipeline_tag: text-generation
library_name: transformers
tags:
- qwen3_5_moe
- image-text-to-text
- conversational
- gguf
- moe
- agent
---

# Nex-N2-Pro-GGUF

## Overview
This repository contains the **GGUF** quantized files for **[nex-agi/Nex-N2-Pro](https://huggingface.co/nex-agi/Nex-N2-Pro)**. 
- **Original Model:** [nex-agi/Nex-N2-Pro](https://huggingface.co/nex-agi/Nex-N2-Pro)
- **Architecture:** Qwen3.5-397B-A17B
- **License:** Apache 2.0
- **MTP Support:** MTP Donor-[unsloth/Qwen3.5-397B-A17B-MTP-GGUF](https://huggingface.co/unsloth/Qwen3.5-397B-A17B-MTP-GGUF)

| Quant Type | Size | Description |
| :--- | :--- | :--- |
| **IQ1+** | 100 GB | Mixed Precision for Better Quality |
| **IQ2_XS** | 142 GB | Mixed Precision for Better Quality |
| **Q2_K** | 158 GB | Standar llama.cpp quantization |

----

<div align="left">
<img src="./figures/NEX_logo.svg" width="20%"/>
</div>

**An agentic model with Agentic Thinking.**

Today, we are officially releasing and open-sourcing our next-generation model, **Nex-N2** — an agent model built for real-world productivity scenarios. With first-tier coding and agentic capabilities, Nex-N2 keeps driving complex, long-horizon tasks forward in real environments to deliver stable, end-to-end results.

Over the past year, a paradigm shift led by Vibe Coding and Harness Engineering has been redefining the limits of LLM agents. From dialogue, to reasoning, to agents that execute long-horizon tasks with environmental feedback, the tasks models must handle keep growing harder, the contexts longer, and the environments more realistic. The core of next-generation model competition is no longer *whether a model can think*, but whether it can reliably and efficiently turn thinking into actions that are executable, verifiable, and iterable.

Rather than treating reasoning, tool use, and environment execution as separate capabilities, Nex-N2 unifies them through an **Agentic Thinking** framework that connects requirement understanding, task planning, code implementation, environmental feedback, evaluation and debugging, and continuous iteration into a single closed loop. The framework has two parts:

- **Adaptive Thinking** lets the model decide on its own when to think and how deeply — executing simple actions quickly while reasoning thoroughly on critical decisions.
- **Coherent Thinking** carries one consistent reasoning paradigm across general reasoning and diverse agentic tasks, staying consistent across tasks and modalities to enable stable capability transfer.

Across real agentic workflows — agentic coding, deep research, tool calling, and terminal execution — Nex-N2 reaches first-tier performance, with substantial gains over the previous-generation Nex-N1 on multiple authoritative benchmarks. In real productivity scenarios such as OpenClaw one-person-company workflows, end-to-end game development, and web and multimodal generation, it likewise demonstrates outstanding usability, robustness, and stability.

---

## Performance

| Benchmark | **Nex-N2-mini** | **Nex-N2-Pro** | GPT-5.5 | Opus 4.7 | Kimi-K2.6 | GLM-5.1 | MiniMax M3 | DeepSeek-V4-Pro |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| **Agent** |  |  |  |  |  |  |  |  |
| BrowseComp | 74.1 | 83.7 | 84.4 | 79.8 | 83.2 | 79.3 | 83.5 | 83.4 |
| GDPval | 1402 | 1585 | 1769 | 1753 | 1481 | 1535 | - | 1554 |
| Toolathlon | 33.3 | 51.9 | 55.6 | 52.8 | 50.0 | 40.7 | - | 51.8 |
| WildClawBench | 47.7 | 53.5 | 58.2 | 62.2 | - | 48.2 | - | 43.7 |
| WideSearch | 62.0 | 75.6 | - | - | 80.8 | - | - | - |
| TAU3 | 65.9 | 71.1 | - | - | - | 70.6 | - | - |
| **Coding & SWE** |  |  |  |  |  |  |  |  |
| SWE-Bench Pro | 50.2 | 58.8 | 58.6 | 64.3 | 58.6 | 58.4 | 59.0 | 55.4 |
| Terminal-Bench 2.1 | 60.7 | 75.3 | 83.4 | 69.7 | - | 58.7 | 66.0 | 72.0 |
| DeepSWE | 8.0 | 33.6 | 70 | 54 | 24 | 18 | - | 8 |
| SWE-Bench Verified | 74.4 | 80.8 | 82.9 | 87.6 | 80.2 | - | 80.5 | 80.6 |
| SWE Atlas QnA | 31.5 | 37.9 | 45.4 | 45.2 | - | - | 37.9 | - |
| SWE Atlas RF | 30.0 | 32.9 | 44.8 | 48.6 | - | - | - | - |
| SWE Atlas TW | 23.3 | 40.0 | 42.6 | 38.2 | - | - | 30.8 | - |
| **General & Reasoning** |  |  |  |  |  |  |  |  |
| GPQA Diamond | 82.6 | 90.7 | 93.6 | 94.2 | 90.5 | 86.2 | - | 90.1 |
| IFEval | 89.1 | 94.0 | - | - | 94.5 | 94.5 | - | 91.9 |
| Apex | 9.4 | 36.5 | - | - | 24.0 | 11.5 | - | 38.3 |

![Nex-N2 Benchmark Overview](./figures/Nex-N2-Benchmark-white.png)

---

## How to Use
These GGUF files are fully compatible with [llama.cpp](https://github.com/ggml-org/llama.cpp) and popular graphical interfaces like **LM Studio**, **Ollama**.

### Example using `llama.cpp` CLI:
```bash
./llama-cli -m nex-n2-pro-Q2_K-00001-of-00023.gguf \
  -p "Hello, how are you?" \
  -sys "You are a helpful AI" \
  -n 4096 \
  -c 8192