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

Libraries

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

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

pipe = pipeline("text-generation", model="inclusionAI/LLaDA2.2-flash", 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/LLaDA2.2-flash", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

utdawn commited on 6 days ago

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1 Parent(s): 3adc2d7

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@@ -51,7 +51,7 @@ The following tables compare **LLaDA2.2-flash** and **Ling-2.6-flash** in terms
 | SWE-bench Verified | 519.0 | 303.2 |
 | SWE-bench Pro | 485.3 | 283.4 |
 | SWE-bench Multilingual | 459.5 | 200.6 |
-| $\tau^2$-Bench | 592.8 | 334.9 |
 | BFCL-V4 | 703.82 | 331.5 |
 > **Ling-2.6-flash evaluation setup:** MTP was enabled with 4 draft tokens.

 | SWE-bench Verified | 519.0 | 303.2 |
 | SWE-bench Pro | 485.3 | 283.4 |
 | SWE-bench Multilingual | 459.5 | 200.6 |
+| τ²-Bench | 592.8 | 334.9 |
 | BFCL-V4 | 703.82 | 331.5 |
 > **Ling-2.6-flash evaluation setup:** MTP was enabled with 4 draft tokens.