Instructions to use inclusionAI/LLaDA-MoE-7B-A1B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inclusionAI/LLaDA-MoE-7B-A1B-Base with Transformers:

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

pipe = pipeline("text-generation", model="inclusionAI/LLaDA-MoE-7B-A1B-Base", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("inclusionAI/LLaDA-MoE-7B-A1B-Base", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use inclusionAI/LLaDA-MoE-7B-A1B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inclusionAI/LLaDA-MoE-7B-A1B-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inclusionAI/LLaDA-MoE-7B-A1B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/inclusionAI/LLaDA-MoE-7B-A1B-Base

SGLang

How to use inclusionAI/LLaDA-MoE-7B-A1B-Base 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 "inclusionAI/LLaDA-MoE-7B-A1B-Base" \
    --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": "inclusionAI/LLaDA-MoE-7B-A1B-Base",
		"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 "inclusionAI/LLaDA-MoE-7B-A1B-Base" \
        --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": "inclusionAI/LLaDA-MoE-7B-A1B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use inclusionAI/LLaDA-MoE-7B-A1B-Base with Docker Model Runner:
```
docker model run hf.co/inclusionAI/LLaDA-MoE-7B-A1B-Base
```

Improve model card with paper, code, project links and pipeline tag

by nielsr HF Staff - opened Oct 21, 2025

base: refs/heads/main

←

from: refs/pr/3

Discussion Files changed

-5

nielsr

Oct 21, 2025

This PR enhances the model card for LLaDA-MoE by adding an explicit link to the foundational paper Large Language Diffusion Models, the project page (https://ml-gsai.github.io/LLaDA-demo/), and the GitHub repository (https://github.com/ML-GSAI/LLaDA).

Additionally, it includes the pipeline_tag: text-generation in the metadata, improving discoverability on the Hugging Face Hub. The existing sample usage for the transformers library confirms its compatibility, and this information is retained.

Improve model card with paper, code, project links and pipeline tag64a200fd

m1ngcheng

inclusionAI org Oct 22, 2025

LGTM

m1ngcheng changed pull request status to merged Oct 22, 2025

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