Instructions to use GSAI-ML/LLaDA-V with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GSAI-ML/LLaDA-V with Transformers:

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

pipe = pipeline("image-text-to-text", model="GSAI-ML/LLaDA-V", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import LlavaLLaDAModelLM
model = LlavaLLaDAModelLM.from_pretrained("GSAI-ML/LLaDA-V", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use GSAI-ML/LLaDA-V with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GSAI-ML/LLaDA-V"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GSAI-ML/LLaDA-V",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/GSAI-ML/LLaDA-V

SGLang

How to use GSAI-ML/LLaDA-V 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 "GSAI-ML/LLaDA-V" \
    --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": "GSAI-ML/LLaDA-V",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "GSAI-ML/LLaDA-V" \
        --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": "GSAI-ML/LLaDA-V",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use GSAI-ML/LLaDA-V with Docker Model Runner:
```
docker model run hf.co/GSAI-ML/LLaDA-V
```

yyyou commited on Mar 23

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@@ -2,13 +2,15 @@
 pipeline_tag: image-text-to-text
 library_name: transformers
 ---
 # LLaDA-V
-We introduce LLaDA-V, a competitive diffusion-based vision-language model, outperforming other diffusion MLLMs.
 It was presented in the paper [LLaDA-V: Large Language Diffusion Models with Visual Instruction Tuning](https://huggingface.co/papers/2505.16933).
 Project Page: https://ml-gsai.github.io/LLaDA-V-demo/
-Code: https://github.com/ML-GSAI/LLaDA-V

 pipeline_tag: image-text-to-text
 library_name: transformers
 ---
+---
+**Update (March 2026):** We are excited to introduce [LLaDA-o](https://huggingface.co/GSAI-ML/LLaDA-o), the latest model in the LLaDA series. As an effective and length-adaptive omni diffusion model for unified multimodal understanding and generation, LLaDA-o extends the LLaDA line to broader multimodal settings, supporting visual understanding, text-to-image generation, and instruction-based image editing. For more details, please check out the [paper](https://huggingface.co/papers/2603.01068) and [code](https://github.com/ML-GSAI/LLaDA-o).
+---
 # LLaDA-V
+We introduce LLaDA-V, a competitive diffusion-based vision-language model that outperforms other diffusion MLLMs.
 It was presented in the paper [LLaDA-V: Large Language Diffusion Models with Visual Instruction Tuning](https://huggingface.co/papers/2505.16933).
 Project Page: https://ml-gsai.github.io/LLaDA-V-demo/
+Code: https://github.com/ML-GSAI/LLaDA-V