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 Settings

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
```

Improve model card with pipeline tag, library name, GitHub link, and additional sections

by nielsr HF Staff - opened Oct 1, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+44

-11

nielsr

Oct 1, 2025

This PR enhances the model card for dParallel-LLaDA-8B-instruct by:

Adding pipeline_tag: text-generation to ensure the model is discoverable in the text generation category.
Adding library_name: transformers to enable the automated "how to use" widget, as the model is compatible with the 🤗 Transformers library.
Including a direct link to the GitHub repository in the introductory badges for easier access to the codebase.
Integrating several useful sections (Updates, Installation, Evaluation, Training, and Acknowledgement) from the GitHub README to provide more complete information for users.

These changes will significantly improve the model's visibility and usability on the Hugging Face Hub.

Improve model card with pipeline tag, library name, GitHub link, and additional sectionse35a935d

Zigeng changed pull request status to merged Oct 1, 2025

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