Instructions to use nvidia/Efficient-DLM-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nvidia/Efficient-DLM-4B with Transformers:

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

pipe = pipeline("text-generation", model="nvidia/Efficient-DLM-4B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("nvidia/Efficient-DLM-4B", trust_remote_code=True)
model = AutoModel.from_pretrained("nvidia/Efficient-DLM-4B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use nvidia/Efficient-DLM-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nvidia/Efficient-DLM-4B"
# 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/Efficient-DLM-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nvidia/Efficient-DLM-4B

SGLang

How to use nvidia/Efficient-DLM-4B 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/Efficient-DLM-4B" \
    --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/Efficient-DLM-4B",
		"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/Efficient-DLM-4B" \
        --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/Efficient-DLM-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nvidia/Efficient-DLM-4B with Docker Model Runner:
```
docker model run hf.co/nvidia/Efficient-DLM-4B
```

Efficient-DLM-4B

File size: 1,256 Bytes

d28c316

{
  "adaptive_mask_rate": false,
  "architectures": [
    "EfficientDLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "attn_implementation": "sdpa",
  "auto_map": {
    "AutoConfig": "configuration_edlm.EfficientDLMConfig",
    "AutoModel": "modeling_edlm.EfficientDLM"
  },
  "block_size": 32,
  "diff_loss_weight": 1,
  "disable_qk_norm": false,
  "dlm_arch": "encoder",
  "dlm_paradigm": "bidirectional",
  "dlm_type": "llada",
  "enforce_mask": false,
  "head_dim": 128,
  "hidden_act": "silu",
  "hidden_size": 2560,
  "initializer_range": 0.02,
  "intermediate_size": 9728,
  "intl_mask": false,
  "mask_token_id": 151662,
  "max_position_embeddings": 32768,
  "max_window_layers": 28,
  "model_type": "qwen3",
  "multi_sampling": null,
  "num_ar_layers": 0,
  "num_attention_heads": 32,
  "num_diffusion_layers": 0,
  "num_hidden_layers": 36,
  "num_key_value_heads": 8,
  "prefix_ratio": 0.8,
  "random_length_prob": 0,
  "rms_norm_eps": 1e-06,
  "rope_scaling": null,
  "rope_theta": 1000000,
  "sliding_window": null,
  "tie_word_embeddings": false,
  "tok_mask_half_life_ratio": null,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.52.2",
  "use_cache": false,
  "use_sliding_window": false,
  "vocab_size": 151936
}