Instructions to use inclusionAI/LLaDA2.2-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inclusionAI/LLaDA2.2-mini with Transformers:

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

pipe = pipeline("text-generation", model="inclusionAI/LLaDA2.2-mini", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("inclusionAI/LLaDA2.2-mini", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use inclusionAI/LLaDA2.2-mini with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inclusionAI/LLaDA2.2-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/LLaDA2.2-mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/inclusionAI/LLaDA2.2-mini

SGLang

How to use inclusionAI/LLaDA2.2-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/LLaDA2.2-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/LLaDA2.2-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/LLaDA2.2-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/LLaDA2.2-mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use inclusionAI/LLaDA2.2-mini with Docker Model Runner:
```
docker model run hf.co/inclusionAI/LLaDA2.2-mini
```

Bin2bin commited on 12 days ago

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1 Parent(s): 038922e

Update Readme.md (#3)

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@@ -155,6 +155,47 @@ If you are in mainland China, we strongly recommend accessing our models via
 ---
 ## 🌐 License
 This project is licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).

 ---
+## Deployment
+### SGLang
+You can serve LLaDA2.2-mini through an OpenAI-compatible API using SGLang.
+Install SGLang from the latest `main` branch:
+```bash
+git clone https://github.com/sgl-project/sglang.git
+cd sglang
+pip install -e "python"
+```
+For environment requirements, see the
+[SGLang installation guide](https://docs.sglang.io/docs/get-started/install).
+Launch the server with the following command. This example uses eight NVIDIA GPUs:
+```bash
+python3 -m sglang.launch_server \
+  --model-path inclusionAI/LLaDA2.2-mini \
+  --trust-remote-code \
+  --dllm-algorithm JointThresholdInDel \
+  --moe-runner-backend triton \
+  --tp-size 8 \
+  --context-length 131072 \
+  --max-running-requests 8 \
+  --host 0.0.0.0 \
+  --port 30000
+```
+Adjust `--tp-size` and `--max-running-requests` according to your hardware
+and available GPU memory.
+`JointThresholdInDel` currently uses greedy decoding, with default settings
+of `block_size=32`, `threshold=0.5`, `edit_threshold=0.0`, and
+`max_post_edit_steps=16`.
+---
 ## 🌐 License
 This project is licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).