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
```

Why not compare it with Qwen3-4B or Qwen3-7B?

by wisamidris7 - opened Oct 13, 2025

Discussion

wisamidris7

Oct 13, 2025

It would be a stronger comparison — but not Qwen2.5-3B.

InclusionAI’s LLaDA-MoE-7B-A1B-Base benchmarks include comparisons against Qwen2.5-3B, a smaller and more lightweight model.
While this shows that LLaDA-MoE-7B offers a clear step up in reasoning and language quality, the comparison doesn’t fully represent how it performs against next-generation models of similar size, such as Qwen3-4B or Qwen3-7B.

A fairer evaluation would involve models closer in scale and architecture — especially since the Qwen3 series brings significant improvements in tokenization, training efficiency, and multi-round dialogue capabilities.

This note is not a critique, but an observation:
👉 Comparing a 7B MoE model to a 3B dense model naturally creates an advantage, and future benchmarks will likely include Qwen3-4B or Qwen3-7B for a more balanced performance analysis.

lccurious

inclusionAI org Oct 24, 2025

Thank you for this insightful observation. We completely agree that a more direct comparison against similarly-sized, next-generation models like Qwen3-4B and Qwen3-7B is essential for a balanced analysis.

This is precisely what we have planned for our forthcoming LLaDA 2.0 series. As a first glimpse, we've just released a mini-preview which you can explore here:

👉 https://huggingface.co/inclusionAI/LLaDA2.0-mini-preview

The comprehensive benchmarks you suggested will be featured in subsequent releases within the LLaDA 2.0 series. For all future updates, we encourage you to follow our main InclusionAI profile. Thanks again for the excellent suggestion, and we hope you'll follow our progress!

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