Instructions to use inclusionAI/LLaDA2.1-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inclusionAI/LLaDA2.1-mini with Transformers:

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

pipe = pipeline("text-generation", model="inclusionAI/LLaDA2.1-mini", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("inclusionAI/LLaDA2.1-mini", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use inclusionAI/LLaDA2.1-mini with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inclusionAI/LLaDA2.1-mini"
# 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/LLaDA2.1-mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/inclusionAI/LLaDA2.1-mini

SGLang

How to use inclusionAI/LLaDA2.1-mini 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/LLaDA2.1-mini" \
    --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/LLaDA2.1-mini",
		"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/LLaDA2.1-mini" \
        --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/LLaDA2.1-mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use inclusionAI/LLaDA2.1-mini with Docker Model Runner:
```
docker model run hf.co/inclusionAI/LLaDA2.1-mini
```

About model.generate and KV cache

by quaternior - opened 11 days ago

Discussion

quaternior

11 days ago

Hello, thanks for your impressive work, I really enjoy this project and model.

I have some questions about this model. This example code shows model.generate for convenience reproduction. But it seems that it doesn't use KV cache for decode-like phase. And also, for efficient deployment, I saw that there are some choices such as dInfer (for efficient inference framework) and SGLang (for efficient serving framework). Is it right?

Again, thanks for your amazing work!

utdawn

inclusionAI org 10 days ago

Thank you for your kind words and for your interest in our project!

you are correct that the provided model.generate script does not currently support KV caching. This specific script is intended to be a minimal, easy-to-read demonstration of the LLaDA 2.1 decoding algorithm rather than a performance-optimized implementation.

For high-performance deployment and production-level serving, we highly recommend using SGLang. It is well-optimized for our model. You can deploy LLaDA 2.1 using the following command:

python3 -m sglang.launch_server \
      --model-path inclusionAI/LLaDA2.1-flash \
      --dllm-algorithm JointThreshold \
      --tp-size 4 \
      --trust-remote-code \
      --mem-fraction-static 0.8 \
      --max-running-requests 1 \
      --attention-backend flashinfer

I hope this helps!

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