Instructions to use d3LLM/d3LLM_Dream with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use d3LLM/d3LLM_Dream with Transformers:

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

pipe = pipeline("text-generation", model="d3LLM/d3LLM_Dream", 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_Dream", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use d3LLM/d3LLM_Dream with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "d3LLM/d3LLM_Dream"
# 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_Dream",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/d3LLM/d3LLM_Dream

SGLang

How to use d3LLM/d3LLM_Dream 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_Dream" \
    --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_Dream",
		"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_Dream" \
        --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_Dream",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use d3LLM/d3LLM_Dream with Docker Model Runner:
```
docker model run hf.co/d3LLM/d3LLM_Dream
```

d3LLM_Dream / README.md

d3LLM-model

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metadata

datasets:
  - d3LLM/trajectory_data_dream_32
pipeline_tag: text-generation
library_name: transformers
license: apache-2.0
base_model: Dream-org/Dream-v0-Instruct-7B
tags:
  - diffusion
  - text-generation
  - fast-inference
  - d3llm

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

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

📄 Paper: arXiv:2601.07568
👉 Code repo: https://github.com/hao-ai-lab/d3LLM
🌐 Blog: https://hao-ai-lab.github.io/blogs/text-diffusion/
🕹️ Demo: https://d3llm-team.github.io/

Model Description

d3LLM-Dream is an ultra-fast diffusion language model that achieves high generation speed while maintaining competitive performance. It strikes a balance between accuracy and parallelism by using pseudo-trajectory distillation during training and entropy-based multi-block decoding during inference.

Key Features

🚀 High throughput: 4.5× faster than autoregressive models (Qwen-2.5-7B) on H100 GPU, 2.5× faster on A100 GPU. Achieves 235.34 tokens/s on H100 on GSM8K-CoT.
📊 High AUP: Optimized for Accuracy Under Parallelism across benchmarks.
🔧 Specialized: Optimized for coding and math reasoning tasks.

Usage

For more chat examples and evaluation scripts, visit the official repository.

Citation

@inproceedings{ICML'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},
    booktitle = {Proceedings of the 43rd International Conference on Machine Learning (ICML)},
    pages = {to appear},
    year = {2026}
}