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

Libraries

How to use nex-agi/Nex-N2-Pro with Transformers:

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

pipe = pipeline("text-generation", model="nex-agi/Nex-N2-Pro")
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 AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("nex-agi/Nex-N2-Pro")
model = AutoModelForMultimodalLM.from_pretrained("nex-agi/Nex-N2-Pro", device_map="auto")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nex-agi/Nex-N2-Pro with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nex-agi/Nex-N2-Pro"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nex-agi/Nex-N2-Pro",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nex-agi/Nex-N2-Pro

SGLang

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

Docker Model Runner
How to use nex-agi/Nex-N2-Pro with Docker Model Runner:
```
docker model run hf.co/nex-agi/Nex-N2-Pro
```

Update README.md

#16

by zech7ay - opened 10 days ago

base: refs/heads/main

←

from: refs/pr/16

Discussion Files changed

+16

-3

Files changed (1) hide show

README.md +16 -3

README.md CHANGED Viewed

@@ -1,7 +1,7 @@
 ---
 license: apache-2.0
 pipeline_tag: text-generation
-library_name: transformers
 ---
 <div align="center">
@@ -12,10 +12,11 @@ library_name: transformers
 <div align="center">
 🤗 <a href="https://hf.co/collections/nex-agi/nex-n2"><b>Model</b></a>&nbsp&nbsp | &nbsp&nbsp
-🔀 <a href="https://openrouter.ai/nex-agi/Nex-N2-Pro:free"><b>OpenRouter (Enjoy two weeks free starting June 9!)</b></a>&nbsp&nbsp | &nbsp&nbsp
 💻 <a href="https://github.com/nex-agi/Nex-N2"><b>Github</b></a>&nbsp&nbsp | &nbsp&nbsp
 🧭 <a href="https://www.modelscope.cn/collections/nex-agi/Nex-N2"><b>ModelScope</b></a>&nbsp&nbsp | &nbsp&nbsp
-🚀 <a href="https://nex-agi.com"><b>Nex-AGI</b></a>
 </div>
 # Nex-N2
@@ -189,3 +190,15 @@ Nex-series models emit explicit reasoning traces. Add the `--reasoning-parser qw
 ```bash
 python -m sglang.launch_server --model-path /path/to/your/model --tool-call-parser qwen3_coder --reasoning-parser qwen3
 ```

 ---
+library_name: transformers
 license: apache-2.0
 pipeline_tag: text-generation
 ---
 <div align="center">
 <div align="center">
 🤗 <a href="https://hf.co/collections/nex-agi/nex-n2"><b>Model</b></a>&nbsp&nbsp | &nbsp&nbsp
+🔀 <a href="https://openrouter.ai/nex-agi/Nex-N2-Pro"><b>OpenRouter</b></a>&nbsp&nbsp | &nbsp&nbsp
 💻 <a href="https://github.com/nex-agi/Nex-N2"><b>Github</b></a>&nbsp&nbsp | &nbsp&nbsp
 🧭 <a href="https://www.modelscope.cn/collections/nex-agi/Nex-N2"><b>ModelScope</b></a>&nbsp&nbsp | &nbsp&nbsp
+🚀 <a href="https://nex-agi.com"><b>Nex-AGI</b></a>&nbsp&nbsp | &nbsp&nbsp
+📖 <a href="https://huggingface.co/papers/2607.14186"><b>Paper</b></a>
 </div>
 # Nex-N2
 ```bash
 python -m sglang.launch_server --model-path /path/to/your/model --tool-call-parser qwen3_coder --reasoning-parser qwen3
 ```
+## Citation
+If you find Nex-N2 models helpful, please consider citing the NexForge paper:
+```bibtex
+@article{zhao2026nexforge,
+  title={NexForge: Scaling Executable Agent Tasks via Requirement-First Synthesis},
+  author={Zhao, Jiarong and Lei, Zhikai and Xi, Zhiheng Bag and Zheng, Rui and Yan, Hang and Zhou, Jie and Chen, Qin and He, Liang},
+  journal={arXiv preprint arXiv:2607.14186},
+  year={2026}
+}