Text Generation
Transformers
Safetensors
PyTorch
nemotron_labs_diffusion
feature-extraction
nvidia
conversational
custom_code
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
Commit History
Upload tokenizer 85eb9d3 verified
Upload model f318bfe verified
Trying new settings for tokenizer_config.json to hopefully fix issues d42bc62
Update chat_template.jinja 0a9534a verified
Changed chat_template to remove alternating check 0820ac4
Update chat_template.jinja abf48b3 verified
Update config.json 8200ec0 verified
Upload model 15597b8 verified
Upload model f9e0c41 verified
set default causal_context=True 262d402
Overriding the old function doesn't work, reverting to old approach 33b2954
Trying to force transformers to use the older causal mask 456e96b
Made some potential fixes for DSA, need to test if they work ff07748
Upload model 4cabc4d verified
Upload model 6294f2a verified
Removed p_mask assert for compatibility with nemo-rl bad4dec
Delete model.safetensors.index.json b4fbb98 verified
Delete model-00002-of-00004.safetensors 987c652 verified
Delete model-00001-of-00004.safetensors 57e8939 verified
Delete model-00003-of-00004.safetensors c4c4881 verified
Delete model-00004-of-00004.safetensors e93dcde verified
Upload model 378400a verified
Upload tokenizer 1c298a2 verified
Upload model dfcb979 verified
Added custom MinistralDiffOutputWithPast return type and skip_loss functionality 418d9e4
trias702 commited on