Instructions to use inclusionAI/LLaDA2.0-flash-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inclusionAI/LLaDA2.0-flash-preview with Transformers:

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

pipe = pipeline("text-generation", model="inclusionAI/LLaDA2.0-flash-preview", 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/LLaDA2.0-flash-preview", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use inclusionAI/LLaDA2.0-flash-preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inclusionAI/LLaDA2.0-flash-preview"
# 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/LLaDA2.0-flash-preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/inclusionAI/LLaDA2.0-flash-preview

SGLang

How to use inclusionAI/LLaDA2.0-flash-preview 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/LLaDA2.0-flash-preview" \
    --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/LLaDA2.0-flash-preview",
		"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/LLaDA2.0-flash-preview" \
        --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/LLaDA2.0-flash-preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use inclusionAI/LLaDA2.0-flash-preview with Docker Model Runner:
```
docker model run hf.co/inclusionAI/LLaDA2.0-flash-preview
```

lccurious commited on Oct 28, 2025

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@@ -59,7 +59,7 @@ Fully open-source with commitment to transparency. We plan to release a **leadin
 ## 🗺️ What's Next
 + **Supercharged Reasoning with LLaDA 2.0:** LLaDA 2.0 series will be fine-tuned with **Reinforcement Learning**, unlocking a new level of sophisticated reasoning and problem-solving abilities.
-+ **Tools for Innovators:** we will release a **detailed tutorial** and our complete **post-training framework**. Whether you want to master the current model or build your own customized versions, you'll have the tools you need. Stay tuned
 ---

 ## 🗺️ What's Next
 + **Supercharged Reasoning with LLaDA 2.0:** LLaDA 2.0 series will be fine-tuned with **Reinforcement Learning**, unlocking a new level of sophisticated reasoning and problem-solving abilities.
++ **Tools for Innovators:** The model was finetuned on the [VeOmni](https://github.com/ByteDance-Seed/VeOmni) framework using Fully Sharded Data Parallel (FSDP2). We will release a **detailed tutorial** and our complete **post-training framework**. Whether you want to master the current model or build your own customized versions, you'll have the tools you need. Stay tuned
 ---