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)

# pip install -U transformers accelerate
# 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=256)
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
```

Add community evaluation results

by SaylorTwift HF Staff - opened Sep 14

base: refs/heads/main

←

from: refs/pr/3

Discussion Files changed

+38

-0

YAML Metadata Error:Invalid content in Eval Result file .eval_results/Nex-N2.5-Pro.yaml

Check out the documentation for more information.

Show details

Task ID "toolathlon_verified" does not match any task in dataset "hkust-nlp/Toolathlon". Available: none

Files changed (1) hide show

.eval_results/Nex-N2.5-Pro.yaml +38 -0

.eval_results/Nex-N2.5-Pro.yaml ADDED Viewed

	@@ -0,0 +1,38 @@

+- dataset:
+    id: harborframework/terminal-bench-2.1
+    task_id: terminalbench_2_1
+  value: 82.7
+  date: "2026-09-11"
+  source:
+    url: https://huggingface.co/nex-agi/Nex-N2.5-Pro
+    name: "Nex-N2.5 model card"
+  notes: "NexAU harness."
+- dataset:
+    id: ScaleAI/SWE-bench_Pro
+    task_id: SWE_Bench_Pro
+  value: 61.2
+  date: "2026-09-11"
+  source:
+    url: https://huggingface.co/nex-agi/Nex-N2.5-Pro
+    name: "Nex-N2.5 model card"
+  notes: "NexAU harness."
+- dataset:
+    id: datacurve/deep-swe
+    task_id: deep_swe
+  value: 55.8
+  date: "2026-09-11"
+  source:
+    url: https://huggingface.co/nex-agi/Nex-N2.5-Pro
+    name: "Nex-N2.5 model card"
+  notes: "NexAU harness."
+- dataset:
+    id: hkust-nlp/Toolathlon
+    task_id: toolathlon_verified
+  value: 68.5
+  date: "2026-09-11"
+  source:
+    url: https://huggingface.co/nex-agi/Nex-N2.5-Pro
+    name: "Nex-N2.5 model card"