Instructions to use SJTU-DENG-Lab/MBD-Code-LLaDA2-mini-DMax-16B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SJTU-DENG-Lab/MBD-Code-LLaDA2-mini-DMax-16B with Transformers:

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

pipe = pipeline("text-generation", model="SJTU-DENG-Lab/MBD-Code-LLaDA2-mini-DMax-16B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("SJTU-DENG-Lab/MBD-Code-LLaDA2-mini-DMax-16B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SJTU-DENG-Lab/MBD-Code-LLaDA2-mini-DMax-16B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SJTU-DENG-Lab/MBD-Code-LLaDA2-mini-DMax-16B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SJTU-DENG-Lab/MBD-Code-LLaDA2-mini-DMax-16B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SJTU-DENG-Lab/MBD-Code-LLaDA2-mini-DMax-16B

SGLang

How to use SJTU-DENG-Lab/MBD-Code-LLaDA2-mini-DMax-16B 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 "SJTU-DENG-Lab/MBD-Code-LLaDA2-mini-DMax-16B" \
    --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": "SJTU-DENG-Lab/MBD-Code-LLaDA2-mini-DMax-16B",
		"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 "SJTU-DENG-Lab/MBD-Code-LLaDA2-mini-DMax-16B" \
        --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": "SJTU-DENG-Lab/MBD-Code-LLaDA2-mini-DMax-16B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SJTU-DENG-Lab/MBD-Code-LLaDA2-mini-DMax-16B with Docker Model Runner:
```
docker model run hf.co/SJTU-DENG-Lab/MBD-Code-LLaDA2-mini-DMax-16B
```

MBD-Code-LLaDA2-mini-DMax-16B / README.md

DrewJin0827

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metadata

license: mit
library_name: transformers
pipeline_tag: text-generation

Multi-Block Diffusion Language Models (MBD-LMs)

This repository contains the model weights for Multi-Block Diffusion Language Models (MBD-LMs), presented in the paper Multi-Block Diffusion Language Models.

Project Page: sjtu-deng-lab.github.io/mbd-lms
GitHub Repository: SJTU-DENG-Lab/mbd-lms

Introduction

Block Diffusion Language Models (BD-LMs) improve diffusion-based text generation with KV caching and flexible-length generation. MBD-LMs extend them from Single-Block Diffusion (SingleBD) to Multi-Block Diffusion (MultiBD), where a running-set of consecutive blocks is decoded concurrently for inter-block parallelism.

This model is obtained by post-training BD-LMs with Multi-block Teacher Forcing (MultiTF), which integrates teacher forcing and diffusion forcing by training on bounded noise-groups conditioned on clean prefixes.

For setup guidelines, training configurations, and optimized inference engine setups, please refer to the official repository and the Diffulex engine.

Citation

@article{jin2026multiblock,
  title={Multi-Block Diffusion Language Models},
  author={Yijie Jin and Jiajun Xu and Yuxuan Liu and Chenkai Xu and Yi Tu and Jiajun Li and Dandan Tu and Xiaohui Yan and Kai Yu and Pengfei Liu and Zhijie Deng},
  journal={arXiv preprint arXiv:2606.29215},
  year={2026}
}