Instructions to use nex-agi/Nex-N2.5-Pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nex-agi/Nex-N2.5-Pro with Transformers:

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

pipe = pipeline("text-generation", model="nex-agi/Nex-N2.5-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-N2.5-Pro")
model = AutoModelForMultimodalLM.from_pretrained("nex-agi/Nex-N2.5-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-N2.5-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-N2.5-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-N2.5-Pro",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nex-agi/Nex-N2.5-Pro

SGLang

How to use nex-agi/Nex-N2.5-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-N2.5-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-N2.5-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-N2.5-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-N2.5-Pro",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nex-agi/Nex-N2.5-Pro with Docker Model Runner:
```
docker model run hf.co/nex-agi/Nex-N2.5-Pro
```

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README.md +1 -1
figures/Nex-N2.5-Benchmark-white.png +2 -2

README.md CHANGED Viewed

@@ -99,7 +99,7 @@ The tables below compare **Nex-N2.5-mini**, **Nex-N2.5-Pro**, and **Nex-N2.5-Max
     <tr><td>Toolathlon Verified</td><td align="center">54.6</td><td align="center">68.5</td><td align="center">74.7</td><td align="center"><b>76.5</b></td><td align="center">74.9</td><td align="center"><b>76.5</b></td><td align="center">73.0</td><td align="center">74.1</td><td align="center">72.5</td></tr>
   </tbody>
   <tbody>
-    <tr><td>GDPval-AA v2</td><td align="center">1446</td><td align="center">1628</td><td align="center">1713</td><td align="center"><b>1861</b></td><td align="center">1748</td><td align="center">1668</td><td align="center">1769</td><td align="center">1580</td><td align="center">1630</td></tr>
   </tbody>
   <tbody>
     <tr><td>Job Bench</td><td align="center">28.5</td><td align="center">41.4</td><td align="center">53.6</td><td align="center"><b>65.7</b></td><td align="center">45.4</td><td align="center">52.9</td><td align="center">58.2</td><td align="center">54.1</td><td align="center">53.4</td></tr>

     <tr><td>Toolathlon Verified</td><td align="center">54.6</td><td align="center">68.5</td><td align="center">74.7</td><td align="center"><b>76.5</b></td><td align="center">74.9</td><td align="center"><b>76.5</b></td><td align="center">73.0</td><td align="center">74.1</td><td align="center">72.5</td></tr>
   </tbody>
   <tbody>
+    <tr><td>GDPval-AA v2</td><td align="center">1446</td><td align="center">1628</td><td align="center">1713</td><td align="center"><b>1831</b></td><td align="center">1711</td><td align="center">1675</td><td align="center">1763</td><td align="center">1580</td><td align="center">1717</td></tr>
   </tbody>
   <tbody>
     <tr><td>Job Bench</td><td align="center">28.5</td><td align="center">41.4</td><td align="center">53.6</td><td align="center"><b>65.7</b></td><td align="center">45.4</td><td align="center">52.9</td><td align="center">58.2</td><td align="center">54.1</td><td align="center">53.4</td></tr>

figures/Nex-N2.5-Benchmark-white.png CHANGED Viewed

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