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

Libraries

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
```

YongganFu commited on Jan 18

Commit

4cabc4d

verified ·

1 Parent(s): 6294f2a

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Files changed (1) hide show

modeling_ministral_dlm.py +2 -2

modeling_ministral_dlm.py CHANGED Viewed

@@ -518,7 +518,7 @@ class MinistralDiffEncoderModel(Ministral3PreTrainedModel, GenerationMixin):
         if labels is not None and self.config.dlm_paradigm != 'autoregressive':
             if masked_indices is not None:
-                assert p_mask is not None
                 if loss_mask is not None:
                     masked_indices[loss_mask == 0] = 0
@@ -565,7 +565,7 @@ class MinistralDiffEncoderModel(Ministral3PreTrainedModel, GenerationMixin):
         logits = self.diffusion_head(enc_out.last_hidden_state)  # (batch, len_B, vocab)
         causal_logits = None
         if labels is not None and self.config.dlm_paradigm in ['block_diff', 'sbd_block_diff']:
             if self.config.dlm_paradigm == 'sbd_block_diff':
                 causal_logits = logits[:, input_ids_len:]

         if labels is not None and self.config.dlm_paradigm != 'autoregressive':
             if masked_indices is not None:
+                # assert p_mask is not None
                 if loss_mask is not None:
                     masked_indices[loss_mask == 0] = 0
         logits = self.diffusion_head(enc_out.last_hidden_state)  # (batch, len_B, vocab)
         causal_logits = None
         if labels is not None and self.config.dlm_paradigm in ['block_diff', 'sbd_block_diff']:
             if self.config.dlm_paradigm == 'sbd_block_diff':
                 causal_logits = logits[:, input_ids_len:]