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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

d3LLM: Ultra-Fast Diffusion LLM using Pseudo-Trajectory Distillation 🚀

This repository contains d3LLM-LLaDA, an ultra-fast diffusion language model presented in the paper d3LLM: Ultra-Fast Diffusion LLM using Pseudo-Trajectory Distillation.

📄 Paper: arXiv:2601.07568
💻 Code: GitHub - hao-ai-lab/d3LLM
🌐 Blog: Ultra-Fast Diffusion LLMs
🕹️ Demo: d3LLM Demo

Model Description

d3LLM-LLaDA is an ultra-fast diffusion language model that strikes a balance between accuracy and parallelism. It uses pseudo-trajectory distillation to teach the model which tokens can be decoded confidently at early steps, and employs an entropy-based multi-block decoding mechanism with KV-cache refresh during inference.

Key Features

🚀 High throughput: 5.0× faster than autoregressive models (Qwen-2.5-7B-it) on H100 GPU and 3.5× faster on A100 GPU.
📊 High AUP: Achieves high Accuracy Under Parallelism scores across benchmarks.
🔧 Task Optimization: Specifically optimized for coding and math reasoning tasks.

Installation

To use this model, it is recommended to clone the official repository and install the required dependencies:

# Clone the repository
git clone https://github.com/hao-ai-lab/d3LLM.git
cd d3LLM

# Install dependencies
pip install -r requirements.txt

Citation

If you find d3LLM useful for your research, please cite the following work:

@article{arxiv'26:d3llm,
  title   = {d3LLM: Ultra-Fast Diffusion LLM using Pseudo-Trajectory Distillation},
  author  = {Yu-Yang Qian and Junda Su and Lanxiang Hu and Peiyuan Zhang and Zhijie Deng and Peng Zhao and Hao Zhang},
  journal = {ArXiv preprint},
  volume  = {arXiv:2601.07568},
  year    = {2026}
}

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for d3LLM/d3LLM_LLaDA

Base model

GSAI-ML/LLaDA-8B-Instruct

Finetuned

(28)

this model

Quantizations

2 models

Dataset used to train d3LLM/d3LLM_LLaDA

Paper for d3LLM/d3LLM_LLaDA

d3LLM: Ultra-Fast Diffusion LLM using Pseudo-Trajectory Distillation

Paper • 2601.07568 • Published Jan 12 • 1