Instructions to use nvidia/Nemotron-Labs-Diffusion-3B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nvidia/Nemotron-Labs-Diffusion-3B-Base with Transformers:

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

pipe = pipeline("text-generation", model="nvidia/Nemotron-Labs-Diffusion-3B-Base", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("nvidia/Nemotron-Labs-Diffusion-3B-Base", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use nvidia/Nemotron-Labs-Diffusion-3B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nvidia/Nemotron-Labs-Diffusion-3B-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nvidia/Nemotron-Labs-Diffusion-3B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nvidia/Nemotron-Labs-Diffusion-3B-Base

SGLang

How to use nvidia/Nemotron-Labs-Diffusion-3B-Base 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 "nvidia/Nemotron-Labs-Diffusion-3B-Base" \
    --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": "nvidia/Nemotron-Labs-Diffusion-3B-Base",
		"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 "nvidia/Nemotron-Labs-Diffusion-3B-Base" \
        --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": "nvidia/Nemotron-Labs-Diffusion-3B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nvidia/Nemotron-Labs-Diffusion-3B-Base with Docker Model Runner:
```
docker model run hf.co/nvidia/Nemotron-Labs-Diffusion-3B-Base
```

Nemotron-Labs-Diffusion-3B-Base / model_cards /privacy.md

YongganFu

Initial release of Nemotron-Labs-Diffusion-3B-Base

91a60ad 2 days ago

preview code

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	Field \| Response
	:----------------------------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------
	Generatable or reverse engineerable personal data? \| [No]
	Personal data used to create this model? \| [No]
	Was consent obtained for any personal data used? \| [Not Applicable]
	How often is dataset reviewed? \| [During dataset creation, model training, evaluation, and the prerelease phase.]
	Was data from user interactions with the AI model (e.g. user input and prompts) used to train the model? \| [Yes]
	Is there provenance for all datasets used in training? \| Yes
	Does data labeling (annotation, metadata) comply with privacy laws? \| Yes
	Is data compliant with data subject requests for data correction or removal, if such a request was made? \| Not Applicable.
	Applicable Privacy Policy \| https://www.nvidia.com/en-us/about-nvidia/privacy-policy/

	Field \| Response
	:----------------------------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------
	Generatable or reverse engineerable personal data? \| [No]
	Personal data used to create this model? \| [No]
	Was consent obtained for any personal data used? \| [Not Applicable]
	How often is dataset reviewed? \| [During dataset creation, model training, evaluation, and the prerelease phase.]
	Was data from user interactions with the AI model (e.g. user input and prompts) used to train the model? \| [Yes]
	Is there provenance for all datasets used in training? \| Yes
	Does data labeling (annotation, metadata) comply with privacy laws? \| Yes
	Is data compliant with data subject requests for data correction or removal, if such a request was made? \| Not Applicable.
	Applicable Privacy Policy \| https://www.nvidia.com/en-us/about-nvidia/privacy-policy/