Instructions to use luxopes/LuxAI-1-Nemo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use luxopes/LuxAI-1-Nemo with Transformers:

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

pipe = pipeline("text-generation", model="luxopes/LuxAI-1-Nemo")
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("luxopes/LuxAI-1-Nemo")
model = AutoModelForMultimodalLM.from_pretrained("luxopes/LuxAI-1-Nemo", 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 luxopes/LuxAI-1-Nemo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "luxopes/LuxAI-1-Nemo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "luxopes/LuxAI-1-Nemo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/luxopes/LuxAI-1-Nemo

SGLang

How to use luxopes/LuxAI-1-Nemo 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 "luxopes/LuxAI-1-Nemo" \
    --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": "luxopes/LuxAI-1-Nemo",
		"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 "luxopes/LuxAI-1-Nemo" \
        --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": "luxopes/LuxAI-1-Nemo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use luxopes/LuxAI-1-Nemo with Docker Model Runner:
```
docker model run hf.co/luxopes/LuxAI-1-Nemo
```

LuxAI-1-Nemo / DATA_ATTRIBUTION.md

luxopes

LuxAI 1 Nemo: saved BF16 LoRA training result

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Third-party data and attribution

This is a practical attribution index, not legal advice. Review the upstream terms for the intended distribution and deployment. Exact repository revisions and realized counts are recorded in manifest.json.

Partition	Upstream	Selected terms / policy
`open_code_instruct`	`nvidia/OpenCodeInstruct`	CC BY 4.0; perfect executed-test rows only
`ling_coder`	`inclusionAI/Ling-Coder-SFT`	Apache 2.0
`code_fim`, `czech_code`	Derived from the two code sources	Parent terms and lineage retained
`open_code_reasoning`	`nvidia/OpenCodeReasoning-2` + `codeparrot/apps`	Attribution retained; APPS train prompts are MIT
`open_swe`	`nvidia/Open-SWE-Traces` v1.0	CC BY 4.0 plus per-row permissive repository license
`terminal`	`nvidia/Nemotron-Terminal-Corpus`	CC BY 4.0
`chat`	`nvidia/Nemotron-SFT-Instruction-Following-Chat-v3`	CC BY 4.0 and ODC-By
`eu_czech`	`openeurollm/EU-Instruct-Synthetic`, `cs`	Apache 2.0
`muri_czech`	`akoksal/muri-it-language-split`, `ces`	Apache 2.0; original subdataset retained per row
`math`	`nvidia/Nemotron-SFT-Math-v4`	Rows explicitly marked CC BY 4.0 only
`science`	`nvidia/OpenScienceReasoning-2`	CC BY 4.0
`agentic`	`nvidia/Nemotron-SFT-Agentic-v2`	CC BY 4.0; card also identifies Apache 2.0/MIT inputs
`structured`	`nvidia/Nemotron-Instruction-Following-Chat-v1`	CC BY 4.0 rows only
`safety`	`nvidia/Nemotron-SFT-Safety-v1`	CC BY 4.0 rows only
`identity`	Locally generated	LuxAI-generated

Keep this file and manifest.json with redistributed copies. Upstream dataset URLs are https://huggingface.co/datasets/<repo> for the repository names above.