Instructions to use inclusionAI/LLaDA2.1-flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inclusionAI/LLaDA2.1-flash with Transformers:

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

pipe = pipeline("text-generation", model="inclusionAI/LLaDA2.1-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/LLaDA2.1-flash", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use inclusionAI/LLaDA2.1-flash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inclusionAI/LLaDA2.1-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/LLaDA2.1-flash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/inclusionAI/LLaDA2.1-flash

SGLang

How to use inclusionAI/LLaDA2.1-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/LLaDA2.1-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/LLaDA2.1-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/LLaDA2.1-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/LLaDA2.1-flash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use inclusionAI/LLaDA2.1-flash with Docker Model Runner:
```
docker model run hf.co/inclusionAI/LLaDA2.1-flash
```

utdawn commited on Feb 12

Commit

408f916

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1 Parent(s): afa23bf

Update README.md

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README.md +4 -2

README.md CHANGED Viewed

@@ -18,7 +18,9 @@ tags:
 <div align="center">
   <img src="https://mdn.alipayobjects.com/huamei_qa8qxu/afts/img/A*biwvQpCmKjEAAAAAULAAAAgAemJ7AQ/original" width="800" />
 </div>
 ---
 <table>
 <thead>
 <tr>
@@ -113,7 +115,7 @@ tags:
 <td align="left">MuSR</td>
 <td align="center">79.15</td>
 <td align="center">82.72</td>
-<td align="center">82.72 | 1.70</td>
 <td align="center">80.10 | 2.90</td>
 <td align="center">79.84 | 1.85</td>
 </tr>
@@ -137,7 +139,7 @@ tags:
 <td align="left">PIQA</td>
 <td align="center">91.57</td>
 <td align="center">91.95</td>
-<td align="center">96.50 | 1.43</td>
 <td align="center">92.44 | 2.38</td>
 <td align="center">92.17 | 1.44</td>
 </tr>

 <div align="center">
   <img src="https://mdn.alipayobjects.com/huamei_qa8qxu/afts/img/A*biwvQpCmKjEAAAAAULAAAAgAemJ7AQ/original" width="800" />
 </div>
 ---
 <table>
 <thead>
 <tr>
 <td align="left">MuSR</td>
 <td align="center">79.15</td>
 <td align="center">82.72</td>
+<td align="center">80.48 | 1.70</td>
 <td align="center">80.10 | 2.90</td>
 <td align="center">79.84 | 1.85</td>
 </tr>
 <td align="left">PIQA</td>
 <td align="center">91.57</td>
 <td align="center">91.95</td>
+<td align="center">92.76 | 1.43</td>
 <td align="center">92.44 | 2.38</td>
 <td align="center">92.17 | 1.44</td>
 </tr>