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

Libraries

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

utdawn commited on Nov 25, 2025

Commit

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@@ -19,29 +19,29 @@ tags:
 ---
 | Benchmark | Ling-mini-2.0 | LLaDA-MoE-7B-A1B-Instruct | LLaDA2.0-mini-preview |
-| :------------------------------ | :-------------: | :-------------------------: | :---------------------: |
-| **Average** | 68.98 | 59.72 | 66.89 |
 | **Knowledge** | | | |
-| MMLU | 78.75 | 67.18 | 72.49 |
-| MMLU-PRO | 56.40 | 44.64 | 49.22 |
-| CMMLU | 77.84 | 64.30 | 67.53 |
-| C-EVAL | 77.85 | 63.93 | 66.54 |
 | **Reasoning** | | | |
-| squad2.0 | 69.14 | 86.81 | 85.61 |
-| drop | 76.35 | 79.77 | 79.49 |
-| korbench | 51.04 | 38.40 | 37.26 |
 | **Coding** | | | |
-| CruxEval-O | 71.12 | 42.38 | 61.88 |
-| mbpp | 81.03 | 70.02 | 77.75 |
-| MultiPL-E | 62.23 | 52.53 | 62.43 |
-| humaneval | 77.44 | 61.59 | 80.49 |
-| Bigcodebench-Full | 35.88 | 20.44 | 30.44 |
 | **Math** | | | |
-| GSM8K | 91.58 | 82.41 | 89.01 |
-| math | 82.22 | 58.68 | 73.50 |
 | **Agent & Alignment** | | | |
-| BFCL_Live | 45.74 | 63.09 | 74.11 |
-| IFEval-strict -prompt | 69.13 | 59.33 | 62.50 |

 ---
 | Benchmark | Ling-mini-2.0 | LLaDA-MoE-7B-A1B-Instruct | LLaDA2.0-mini-preview |
+| :---: | :---: | :---: | :---: |
+| **Average** | 74.60 | 59.72 | 66.89 |
 | **Knowledge** | | | |
+| MMLU | 82.15 | 67.18 | 72.49 |
+| MMLU-PRO | 63.72 | 44.64 | 49.22 |
+| CMMLU | 80.84 | 64.30 | 67.53 |
+| C-EVAL | 82.10 | 63.93 | 66.54 |
 | **Reasoning** | | | |
+| squad2.0 | 75.56 | 86.81 | 85.61 |
+| drop | 78.80 | 79.77 | 79.49 |
+| korbench | 62.72 | 38.40 | 37.26 |
 | **Coding** | | | |
+| CruxEval-O | 76.12 | 42.38 | 61.88 |
+| mbpp | 84.07 | 70.02 | 77.75 |
+| MultiPL-E | 67.09 | 52.53 | 62.43 |
+| humaneval | 85.98 | 61.59 | 80.49 |
+| Bigcodebench-Full | 35.00 | 20.44 | 30.44 |
 | **Math** | | | |
+| GSM8K | 94.62 | 82.41 | 89.01 |
+| math | 94.66 | 58.68 | 73.50 |
 | **Agent & Alignment** | | | |
+| BFCL_Live | 53.98 | 63.09 | 74.11 |
+| IFEval-strict -prompt | 76.16 | 59.33 | 62.50 |