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

Libraries

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

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="nvidia/Nemotron-Labs-Diffusion-VLM-8B", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("nvidia/Nemotron-Labs-Diffusion-VLM-8B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use nvidia/Nemotron-Labs-Diffusion-VLM-8B 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-VLM-8B"
# 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-VLM-8B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/nvidia/Nemotron-Labs-Diffusion-VLM-8B

SGLang

How to use nvidia/Nemotron-Labs-Diffusion-VLM-8B 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-VLM-8B" \
    --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-VLM-8B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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-VLM-8B" \
        --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-VLM-8B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use nvidia/Nemotron-Labs-Diffusion-VLM-8B with Docker Model Runner:
```
docker model run hf.co/nvidia/Nemotron-Labs-Diffusion-VLM-8B
```

Nemotron-Labs-Diffusion-VLM-8B

File size: 2,889 Bytes

c6706ba

{
  "ada_dlm_loss_ratio": null,
  "ada_perm_ratio_global": null,
  "ada_perm_ratio_per_block": null,
  "adaptive_mask_rate": false,
  "always_mask_im_end": false,
  "ar_loss_weight": 1.0,
  "architectures": [
    "NemotronLabsDiffusionVLMModel"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "attn_implementation": null,
  "auto_map": {
    "AutoConfig": "configuration_nemotron_labs_diffusion_vlm.NemotronLabsDiffusionVLMConfig",
    "AutoModel": "modeling_nemotron_labs_diffusion_vlm.NemotronLabsDiffusionVLMModel",
    "AutoModelForCausalLM": "modeling_nemotron_labs_diffusion_vlm.NemotronLabsDiffusionVLMModel"
  },
  "block_size": 32,
  "bos_token_id": 1,
  "complementary_mask": true,
  "diff_loss_weight": 0.5,
  "dlm_arch": "encoder",
  "dlm_loss_weight": 0.5,
  "dlm_paradigm": "bidirectional",
  "dlm_type": "llada",
  "dp_varying_mask_ratio": false,
  "dtype": "bfloat16",
  "enforce_mask": false,
  "eos_token_id": 11,
  "global_loss_avg": true,
  "head_dim": 128,
  "hidden_act": "silu",
  "hidden_size": 4096,
  "im_end_token_id": 11,
  "initializer_range": 0.02,
  "intermediate_size": 14336,
  "mask_token_id": 100,
  "max_position_embeddings": 262144,
  "mlp_bias": false,
  "model_type": "nemotron_labs_diffusion_vlm",
  "multi_sampling": null,
  "multimodal_projector_bias": false,
  "num_ar_layers": 0,
  "num_attention_heads": 32,
  "num_diffusion_layers": 0,
  "num_hidden_layers": 34,
  "num_key_value_heads": 8,
  "num_skip_loss_tokens": 0,
  "pad_token_id": 11,
  "prefix_ratio": 0.8,
  "projector_hidden_act": "gelu",
  "random_length_prob": 0,
  "rms_norm_eps": 1e-05,
  "rope_parameters": {
    "beta_fast": 32.0,
    "beta_slow": 1.0,
    "factor": 16.0,
    "llama_4_scaling_beta": 0.1,
    "mscale": 1.0,
    "mscale_all_dim": 1.0,
    "original_max_position_embeddings": 16384,
    "rope_theta": 1000000.0,
    "rope_type": "yarn",
    "type": "yarn"
  },
  "rope_scaling": {
    "beta_fast": 32.0,
    "beta_slow": 1.0,
    "factor": 16.0,
    "llama_4_scaling_beta": 0.1,
    "mscale": 1.0,
    "mscale_all_dim": 1.0,
    "original_max_position_embeddings": 16384,
    "rope_theta": 1000000.0,
    "rope_type": "yarn",
    "type": "yarn"
  },
  "rope_theta": 1000000.0,
  "sliding_window": null,
  "spatial_merge_size": 2,
  "tie_word_embeddings": false,
  "tok_mask_half_life_ratio": null,
  "transformers_version": "4.57.1",
  "use_cache": false,
  "vision_config": {
    "attention_dropout": 0.0,
    "head_dim": 64,
    "hidden_act": "silu",
    "hidden_size": 1024,
    "image_size": 1540,
    "initializer_range": 0.02,
    "intermediate_size": 4096,
    "model_type": "pixtral",
    "num_attention_heads": 16,
    "num_channels": 3,
    "num_hidden_layers": 24,
    "patch_size": 14,
    "rope_parameters": {
      "rope_theta": 10000.0,
      "rope_type": "default"
    }
  },
  "vision_feature_layer": -1,
  "vocab_size": 131073
}