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 25, 2025

Commit

c2e6723

verified ·

1 Parent(s): e43dd0e

Update README.md

Browse files

Files changed (1) hide show

README.md +4 -4

README.md CHANGED Viewed

@@ -8,7 +8,7 @@ tags:
 - text_generation
 ---
 DA2.0-flash-preview
-**LLaDA2-flash-preview** is a diffusion language model featuring a 100BA6B Mixture-of-Experts (MoE) architecture. As an enhanced, instruction-tuned iteration of the LLaDA2.0 series, it is optimized for practical applications.
 <div align="center">
   <img src="https://mdn.alipayobjects.com/huamei_qa8qxu/afts/img/A*kLORSaRfSK8AAAAAgIAAAAgAemJ7AQ/original" width="800" />
@@ -65,8 +65,8 @@ Fully open-source with commitment to transparency. We plan to release a **leadin
 ## 📦 Model Variants
 | Model ID | Description | Hugging Face Link |
 | --- | --- | --- |
-| `inclusionAI/LLaDA2-mini-preview` | Instruction-tuned model, ready for downstream applications. | [🤗 Model Card](https://huggingface.co/inclusionAI/LLaDA2.0-mini-preview) |
-| `inclusionAI/LLaDA2-flash-preview` | Instruction-tuned model, ready for downstream applications. | [🤗 Model Card](https://huggingface.co/inclusionAI/LLaDA2.0-flash-preview) |
 ---
@@ -93,7 +93,7 @@ import torch.nn.functional as F
 from transformers import AutoModelForCausalLM
 from transformers import AutoTokenizer
-model_path = "/path/to/LLaDA2-mini-preview"
 device = "auto"
 model = AutoModelForCausalLM.from_pretrained(
     model_path, trust_remote_code=True, device_map=device

 - text_generation
 ---
 DA2.0-flash-preview
+**LLaDA2.0-flash-preview** is a diffusion language model featuring a 100BA6B Mixture-of-Experts (MoE) architecture. As an enhanced, instruction-tuned iteration of the LLaDA2.0 series, it is optimized for practical applications.
 <div align="center">
   <img src="https://mdn.alipayobjects.com/huamei_qa8qxu/afts/img/A*kLORSaRfSK8AAAAAgIAAAAgAemJ7AQ/original" width="800" />
 ## 📦 Model Variants
 | Model ID | Description | Hugging Face Link |
 | --- | --- | --- |
+| `inclusionAI/LLaDA2.0-mini-preview` | Instruction-tuned model, ready for downstream applications. | [🤗 Model Card](https://huggingface.co/inclusionAI/LLaDA2.0-mini-preview) |
+| `inclusionAI/LLaDA2.0-flash-preview` | Instruction-tuned model, ready for downstream applications. | [🤗 Model Card](https://huggingface.co/inclusionAI/LLaDA2.0-flash-preview) |
 ---
 from transformers import AutoModelForCausalLM
 from transformers import AutoTokenizer
+model_path = "/path/to/LLaDA2.0-mini-preview"
 device = "auto"
 model = AutoModelForCausalLM.from_pretrained(
     model_path, trust_remote_code=True, device_map=device