Instructions to use d3LLM/d3LLM_LLaDA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use d3LLM/d3LLM_LLaDA with Transformers:

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

pipe = pipeline("text-generation", model="d3LLM/d3LLM_LLaDA", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("d3LLM/d3LLM_LLaDA", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use d3LLM/d3LLM_LLaDA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "d3LLM/d3LLM_LLaDA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "d3LLM/d3LLM_LLaDA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/d3LLM/d3LLM_LLaDA

SGLang

How to use d3LLM/d3LLM_LLaDA 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 "d3LLM/d3LLM_LLaDA" \
    --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": "d3LLM/d3LLM_LLaDA",
		"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 "d3LLM/d3LLM_LLaDA" \
        --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": "d3LLM/d3LLM_LLaDA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use d3LLM/d3LLM_LLaDA with Docker Model Runner:
```
docker model run hf.co/d3LLM/d3LLM_LLaDA
```

d3LLM-model commited on Dec 1, 2025

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@@ -18,7 +18,7 @@ pipeline_tag: text-generation
 ## Key Features
-- 🚀 High throughput: **5.0× faster** than autoregressive models (Qwen-2.5-7B-it) on H100 GPU, **3.5× faster** on A100 GPU. Achieves **288.73 tokens/s** on H100 (vs 57.32 for AR baseline).
 - 📊 High AUP (Accuracy Under Parallelism) scores across benchmarks
 - 🔧 Optimized for coding and math reasoning tasks

 ## Key Features
+- 🚀 High throughput: **5.0× faster** than autoregressive models (Qwen-2.5-7B-it) on H100 GPU, **3.5× faster** on A100 GPU. Achieves **288.73 tokens/s** on H100 (vs 57.32 for AR baseline) on GSM8K-CoT Dataset.
 - 📊 High AUP (Accuracy Under Parallelism) scores across benchmarks
 - 🔧 Optimized for coding and math reasoning tasks