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

Libraries

How to use XReyRobert/Nex-N2-mini-GPTQ-Pro with Transformers:

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

pipe = pipeline("text-generation", model="XReyRobert/Nex-N2-mini-GPTQ-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("XReyRobert/Nex-N2-mini-GPTQ-Pro")
model = AutoModelForMultimodalLM.from_pretrained("XReyRobert/Nex-N2-mini-GPTQ-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 XReyRobert/Nex-N2-mini-GPTQ-Pro with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/XReyRobert/Nex-N2-mini-GPTQ-Pro

SGLang

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

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

XReyRobert commited on Jun 19

Commit

574917e

verified ·

1 Parent(s): 7b10e14

Slim Smoke24 publication card

Browse files

Files changed (1) hide show

README.md +6 -10

README.md CHANGED Viewed

@@ -131,11 +131,11 @@ Completed artifact checks:
 - Index metadata total size matches the local safetensor shards.
 - The remote artifact contains the expected five safetensor shards.
-Terminal-Bench 2.0 Smoke24 result:
-| Run | Score | Success rate | Wall-time | Output tokens | Observed decode | LLM API time | Local efficiency |
-|---|---:|---:|---:|---:|---:|---:|---:|
-| `nex-n2-mini-gptq-pro` | `14/24` | `58.3%` | `314.6m` | `1670.6k` | `140.8 tok/s` | `197.4m` | `0.64x` |
 Smoke24 is a fixed 24-task Terminal-Bench 2.0 comparison corpus, not a full
 Terminal-Bench leaderboard run. In this harness, Nex-N2-mini GPTQ-Pro tied the
@@ -143,13 +143,9 @@ Qwen3.6 27B GPTQ reference on solved tasks but used more wall time and far more
 output tokens. That makes it a useful candidate for further serving and
 generation-control tuning, not an efficiency leader in this specific test.
-Full local report and CSVs are included under:
-- [`benchmarks/terminal-bench-2.0/tb20_smoke24_model_comparison_20260617.md`](benchmarks/terminal-bench-2.0/tb20_smoke24_model_comparison_20260617.md)
-- [`benchmarks/terminal-bench-2.0/tb20_smoke24_model_comparison_20260617.csv`](benchmarks/terminal-bench-2.0/tb20_smoke24_model_comparison_20260617.csv)
-- [`benchmarks/terminal-bench-2.0/tb20_smoke24_local_efficiency_metrics_20260617.csv`](benchmarks/terminal-bench-2.0/tb20_smoke24_local_efficiency_metrics_20260617.csv)
-![Terminal-Bench Smoke24 local efficiency](benchmarks/terminal-bench-2.0/tb20_smoke24_score_llm_cost_per_success_bubble.png)
 ## MTP And Vision Status

 - Index metadata total size matches the local safetensor shards.
 - The remote artifact contains the expected five safetensor shards.
+Terminal-Bench 2.0 Smoke24 result and associated vLLM serving measurements:
+| Run | Score | Success rate | Wall-time | Output tokens | Observed decode | LLM API time |
+|---|---:|---:|---:|---:|---:|---:|
+| `nex-n2-mini-gptq-pro` | `14/24` | `58.3%` | `314.6m` | `1670.6k` | `140.8 tok/s` | `197.4m` |
 Smoke24 is a fixed 24-task Terminal-Bench 2.0 comparison corpus, not a full
 Terminal-Bench leaderboard run. In this harness, Nex-N2-mini GPTQ-Pro tied the
 output tokens. That makes it a useful candidate for further serving and
 generation-control tuning, not an efficiency leader in this specific test.
+Task list and harness shape:
+- [`benchmarks/terminal-bench-2.0/smoke24_task_list_20260616.md`](benchmarks/terminal-bench-2.0/smoke24_task_list_20260616.md)
 ## MTP And Vision Status