Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

nvidia
/
Nemotron-Labs-Diffusion-8B-Base

Text Generation
Transformers
Safetensors
PyTorch
nemotron_labs_diffusion
feature-extraction
nvidia
conversational
custom_code
Model card Files Files and versions
xet
Community
1

Instructions to use nvidia/Nemotron-Labs-Diffusion-8B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use nvidia/Nemotron-Labs-Diffusion-8B-Base with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="nvidia/Nemotron-Labs-Diffusion-8B-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-8B-Base", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use nvidia/Nemotron-Labs-Diffusion-8B-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-8B-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-8B-Base",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/nvidia/Nemotron-Labs-Diffusion-8B-Base
  • SGLang

    How to use nvidia/Nemotron-Labs-Diffusion-8B-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-8B-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-8B-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-8B-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-8B-Base",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Docker Model Runner

    How to use nvidia/Nemotron-Labs-Diffusion-8B-Base with Docker Model Runner:

    docker model run hf.co/nvidia/Nemotron-Labs-Diffusion-8B-Base
Nemotron-Labs-Diffusion-8B-Base
17 GB
Ctrl+K
Ctrl+K
  • 4 contributors
History: 30 commits
abhgarg's picture
abhgarg
Clean up rope params; ensure transformers 4.55/5.0 compatibility
a4574ae verified 5 days ago
  • .gitattributes
    1.57 kB
    Upload tokenizer 4 months ago
  • README.md
    1.43 kB
    Update README.md 4 months ago
  • chat_template.jinja
    397 Bytes
    Upload tokenizer 17 days ago
  • chat_utils.py
    12.9 kB
    Upload model 17 days ago
  • config.json
    1.75 kB
    Clean up rope params; ensure transformers 4.55/5.0 compatibility 5 days ago
  • configuration_ministral_dlm.py
    11.6 kB
    Clean up rope params; ensure transformers 4.55/5.0 compatibility 5 days ago
  • generation_config.json
    133 Bytes
    Upload model 4 months ago
  • model.safetensors
    17 GB
    xet
    Upload model 4 months ago
  • modeling_ministral.py
    23.4 kB
    Clean up rope params; ensure transformers 4.55/5.0 compatibility 5 days ago
  • modeling_ministral_dlm.py
    81 kB
    Clean up rope params; ensure transformers 4.55/5.0 compatibility 5 days ago
  • special_tokens_map.json
    414 Bytes
    Upload tokenizer 4 months ago
  • tokenizer.json
    17.1 MB
    xet
    Upload tokenizer 4 months ago
  • tokenizer_config.json
    177 kB
    Upload tokenizer 17 days ago