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
Update chat_template.jinja
Browse files- chat_template.jinja +109 -7
chat_template.jinja
CHANGED
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{#- Default system message if no system prompt is passed. #}
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{%- set default_system_message = '' %}
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{#- Begin of sequence token. #}
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{{- bos_token }}
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{#- Handle system prompt if it exists. #}
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{#- System prompt supports text content or text chunks. #}
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{%- if messages[0]['role'] == 'system' %}
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{{- '[SYSTEM_PROMPT]' -}}
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{%- if messages[0]['content'] is string %}
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{{- messages[0]['content'] -}}
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{%- else %}
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{%- for block in messages[0]['content'] %}
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{%- if block['type'] == 'text' %}
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{{- block['text'] }}
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{%- else %}
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{{- raise_exception('Only text chunks are supported in system message contents.') }}
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{%- endif %}
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{%- endfor %}
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{%- endif %}
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{{- '[/SYSTEM_PROMPT]' -}}
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{%- set loop_messages = messages[1:] %}
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{%- else %}
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{%- set loop_messages = messages %}
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{%- if default_system_message != '' %}
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{{- '[SYSTEM_PROMPT]' + default_system_message + '[/SYSTEM_PROMPT]' }}
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{%- endif %}
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{%- endif %}
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{#- Tools definition #}
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{%- set tools_definition = '' %}
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{%- set has_tools = false %}
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{%- if tools is defined and tools is not none and tools|length > 0 %}
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{%- set has_tools = true %}
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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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{%- if message['role'] == 'user' %}
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{%- if message['content'] is string %}
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{{- '[INST]' + message['content'] + '[/INST]' }}
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{%- elif message['content'] | length > 0 %}
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{{- '[INST]' }}
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{%- if message['content'] | length == 2 %}
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{%- set blocks = message['content'] | sort(attribute='type') %}
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{%- else %}
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{%- set blocks = message['content'] %}
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{%- endif %}
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{%- for block in blocks %}
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{%- if block['type'] == 'text' %}
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{{- block['text'] }}
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{%- elif block['type'] in ['image', 'image_url'] %}
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{{- '[IMG]' }}
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{%- else %}
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{{- raise_exception('Only text, image and image_url chunks are supported in user message content.') }}
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{%- endif %}
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{%- endfor %}
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{{- '[/INST]' }}
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{%- else %}
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{{- raise_exception('User message must have a string or a list of chunks in content') }}
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{%- endif %}
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{#- Assistant messages supports text content or text and image chunks. #}
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{%- elif message['role'] == 'assistant' %}
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{%- if (message['content'] is none or message['content'] == '' or message['content']|length == 0) and (message['tool_calls'] is not defined or message['tool_calls'] is none or message['tool_calls']|length == 0) %}
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{{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }}
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{%- endif %}
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{%- if message['content'] is string %}
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{{- message['content'] }}
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{%- elif message['content'] | length > 0 %}
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{%- for block in message['content'] %}
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{%- if block['type'] == 'text' %}
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{{- block['text'] }}
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{%- else %}
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{{- raise_exception('Only text chunks are supported in assistant message contents.') }}
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{%- endif %}
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{%- endfor %}
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{%- endif %}
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{%- if message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %}
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{%- for tool in message['tool_calls'] %}
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{%- set arguments = tool['function']['arguments'] %}
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{%- if arguments is not string %}
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{%- set arguments = arguments|tojson|safe %}
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{%- elif arguments == '' %}
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{%- set arguments = '{}' %}
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{%- endif %}
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{{- '[TOOL_CALLS]' + tool['function']['name'] + '[ARGS]' + arguments }}
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{%- endfor %}
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{%- endif %}
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{#- End of sequence token for each assistant messages. #}
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{{- eos_token }}
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{#- Tool messages only supports text content. #}
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{%- elif message['role'] == 'tool' %}
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{{- '[TOOL_RESULTS]' + message['content']|string + '[/TOOL_RESULTS]' }}
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{#- Raise exception for unsupported roles. #}
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{%- else %}
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{{- raise_exception('Only user, assistant and tool roles are supported, got ' + message['role'] + '.') }}
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{%- endif %}
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{%- endfor %}
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