Instructions to use SJTU-DENG-Lab/MBD-Code-SDAR-8B-Chat-b4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SJTU-DENG-Lab/MBD-Code-SDAR-8B-Chat-b4 with Transformers:

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

pipe = pipeline("text-generation", model="SJTU-DENG-Lab/MBD-Code-SDAR-8B-Chat-b4", 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-SDAR-8B-Chat-b4", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SJTU-DENG-Lab/MBD-Code-SDAR-8B-Chat-b4 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-SDAR-8B-Chat-b4"
# 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-SDAR-8B-Chat-b4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SJTU-DENG-Lab/MBD-Code-SDAR-8B-Chat-b4

SGLang

How to use SJTU-DENG-Lab/MBD-Code-SDAR-8B-Chat-b4 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-SDAR-8B-Chat-b4" \
    --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-SDAR-8B-Chat-b4",
		"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-SDAR-8B-Chat-b4" \
        --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-SDAR-8B-Chat-b4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SJTU-DENG-Lab/MBD-Code-SDAR-8B-Chat-b4 with Docker Model Runner:
```
docker model run hf.co/SJTU-DENG-Lab/MBD-Code-SDAR-8B-Chat-b4
```

MBD-Code-SDAR-8B-Chat-b4 / README.md

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metadata

license: mit
library_name: transformers
pipeline_tag: text-generation

Multi-Block Diffusion Language Models (MBD-LMs)

Model weights of MBD-LMs.

Project Page: sjtu-deng-lab.github.io/mbd-lms
GitHub Repository (Training & Configs): SJTU-DENG-Lab/mbd-lms
Inference Engine (Diffulex): SJTU-DENG-Lab/Diffulex

Introduction

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

MBD-LMs are 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, matching practical MultiBD inference states.

Citation

If you find this work useful, please cite:

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