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
Changed chat_template to remove alternating check
Browse filesSigned-off-by: Daniel Egert <degert@nvidia.com>
- chat_template.jinja +0 -10
chat_template.jinja
CHANGED
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@@ -33,16 +33,6 @@
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{%- set tools_definition = '[AVAILABLE_TOOLS]' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %}
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{{- tools_definition }}
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{%- endif %}
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{#- Checks for alternating user/assistant messages. #}
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{%- set ns = namespace(index=0) %}
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{%- for message in loop_messages %}
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{%- if message.role == 'user' or (message.role == 'assistant' and (message.tool_calls is not defined or message.tool_calls is none or message.tool_calls | length == 0)) %}
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{%- if (message['role'] == 'user') != (ns.index % 2 == 0) %}
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{{- raise_exception('After the optional system message, conversation roles must alternate user and assistant roles except for tool calls and results.') }}
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{%- endif %}
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{%- set ns.index = ns.index + 1 %}
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{%- endif %}
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{%- endfor %}
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{#- Handle conversation messages. #}
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{%- for message in loop_messages %}
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{#- User messages supports text content or text and image chunks. #}
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{%- set tools_definition = '[AVAILABLE_TOOLS]' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %}
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{{- tools_definition }}
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{%- endif %}
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{#- Handle conversation messages. #}
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{%- for message in loop_messages %}
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{#- User messages supports text content or text and image chunks. #}
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