Instructions to use inclusionAI/Ring-flash-2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inclusionAI/Ring-flash-2.0 with Transformers:

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

pipe = pipeline("text-generation", model="inclusionAI/Ring-flash-2.0", 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/Ring-flash-2.0", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use inclusionAI/Ring-flash-2.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inclusionAI/Ring-flash-2.0"
# 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/Ring-flash-2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/inclusionAI/Ring-flash-2.0

SGLang

How to use inclusionAI/Ring-flash-2.0 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/Ring-flash-2.0" \
    --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/Ring-flash-2.0",
		"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/Ring-flash-2.0" \
        --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/Ring-flash-2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use inclusionAI/Ring-flash-2.0 with Docker Model Runner:
```
docker model run hf.co/inclusionAI/Ring-flash-2.0
```

m1ngcheng

thinkthinking commited on Sep 29, 2025

Commit

b5fd4e7

verified ·

1 Parent(s): 53a6fd9

Update README.md (#3)

Browse files

- Update README.md (9c6cd94fa26a8602a758cf40800cc9baf4bcfc62)

Co-authored-by: Ye Zhenjie <thinkthinking@users.noreply.huggingface.co>

Files changed (1) hide show

README.md +35 -1

README.md CHANGED Viewed

@@ -9,7 +9,7 @@ library_name: transformers
 <p align="center">
     <img src="https://mdn.alipayobjects.com/huamei_qa8qxu/afts/img/A*4QxcQrBlTiAAAAAAQXAAAAgAemJ7AQ/original" width="100"/>
 <p>
-<p align="center">🤗 <a href="https://huggingface.co/inclusionAI">Hugging Face</a>&nbsp&nbsp | &nbsp&nbsp🤖 <a href="https://modelscope.cn/organization/inclusionAI">ModelScope</a></p>
 ## Introduction
@@ -71,6 +71,40 @@ During RL training, we compared directly combining RLVR and RLHF into joint trai
 ## Quickstart
 ### 🤗 Hugging Face Transformers
 Here is a code snippet to show you how to use the chat model with `transformers`:

 <p align="center">
     <img src="https://mdn.alipayobjects.com/huamei_qa8qxu/afts/img/A*4QxcQrBlTiAAAAAAQXAAAAgAemJ7AQ/original" width="100"/>
 <p>
+<p align="center">🤗 <a href="https://huggingface.co/inclusionAI">Hugging Face</a>&nbsp&nbsp | &nbsp&nbsp🤖 <a href="https://modelscope.cn/organization/inclusionAI">ModelScope</a>&nbsp&nbsp | &nbsp&nbsp🚀 <a href="https://zenmux.ai/inclusionai/ring-flash-2.0?utm_source=hf_inclusionAI">Experience Now</a></p>
 ## Introduction
 ## Quickstart
+### 🚀 Try Online
+You can experience Ring-flash-2.0 online at: [ZenMux](https://zenmux.ai/inclusionai/ring-flash-2.0?utm_source=hf_inclusionAI)
+### 🔌 API Usage
+You can also use Ring-flash-2.0 through API calls:
+```python
+from openai import OpenAI
+# 1. Initialize the OpenAI client
+client = OpenAI(
+    # 2. Point the base URL to the ZenMux endpoint
+    base_url="https://zenmux.ai/api/v1",
+    # 3. Replace with the API Key from your ZenMux user console
+    api_key="<your ZENMUX_API_KEY>",
+)
+# 4. Make a request
+completion = client.chat.completions.create(
+    # 5. Specify the model to use in the format "provider/model-name"
+    model="inclusionai/ring-flash-2.0",
+    messages=[
+        {
+            "role": "user",
+            "content": "What is the meaning of life?"
+        }
+    ]
+)
+print(completion.choices[0].message.content)
+```
 ### 🤗 Hugging Face Transformers
 Here is a code snippet to show you how to use the chat model with `transformers`: