Instructions to use Zigeng/dParallel-LLaDA-8B-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Zigeng/dParallel-LLaDA-8B-instruct with Transformers:

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

pipe = pipeline("text-generation", model="Zigeng/dParallel-LLaDA-8B-instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Zigeng/dParallel-LLaDA-8B-instruct", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Zigeng/dParallel-LLaDA-8B-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Zigeng/dParallel-LLaDA-8B-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Zigeng/dParallel-LLaDA-8B-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Zigeng/dParallel-LLaDA-8B-instruct

SGLang

How to use Zigeng/dParallel-LLaDA-8B-instruct 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 "Zigeng/dParallel-LLaDA-8B-instruct" \
    --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": "Zigeng/dParallel-LLaDA-8B-instruct",
		"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 "Zigeng/dParallel-LLaDA-8B-instruct" \
        --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": "Zigeng/dParallel-LLaDA-8B-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Zigeng/dParallel-LLaDA-8B-instruct with Docker Model Runner:
```
docker model run hf.co/Zigeng/dParallel-LLaDA-8B-instruct
```

Zigeng commited on Oct 1, 2025

Commit

008b152

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1 Parent(s): 49c94ec

Update README.md

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@@ -10,7 +10,7 @@ license: mit
   <a href="https://opensource.org/license/mit-0">
     <img alt="MIT" src="https://img.shields.io/badge/License-MIT-4E94CE.svg">
   </a>
-  <a href="https://github.com/czg1225/dParallel">
     <img src="https://img.shields.io/badge/Paper-Arxiv-darkred.svg" alt="Paper">
   </a>
   <a href="https://huggingface.co/Zigeng/dParallel-LLaDA-8B-instruct">
@@ -50,7 +50,7 @@ We introduce dParallel, a simple and effective method that unlocks the inherent
   <tbody>
     <tr>
       <td>📄 <strong>Paper</strong></td>
-      <td><a href="https://github.com/czg1225/dParallel">ArXiv-Link</a></td>
     </tr>
     <tr>
       <td>🤖 <strong>Model</strong></td>

   <a href="https://opensource.org/license/mit-0">
     <img alt="MIT" src="https://img.shields.io/badge/License-MIT-4E94CE.svg">
   </a>
+  <a href="https://arxiv.org/pdf/2509.26488">
     <img src="https://img.shields.io/badge/Paper-Arxiv-darkred.svg" alt="Paper">
   </a>
   <a href="https://huggingface.co/Zigeng/dParallel-LLaDA-8B-instruct">
   <tbody>
     <tr>
       <td>📄 <strong>Paper</strong></td>
+      <td><a href="https://arxiv.org/pdf/2509.26488">ArXiv-Link</a></td>
     </tr>
     <tr>
       <td>🤖 <strong>Model</strong></td>