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

Libraries

How to use inclusionAI/Ring-mini-linear-2.0 with Transformers:

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

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

docker model run hf.co/inclusionAI/Ring-mini-linear-2.0

SGLang

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

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

caizhi1 commited on Sep 25, 2025

Commit

1e2bb30

1 Parent(s): 1351e7b

Update README.md

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@@ -20,22 +20,30 @@ tags:
 ## Introduction
 We are excited to announce the official open-source release of Ring-mini-linear-2.0!
-Building on the success of our Ling 2.0 series, this model continues to leverage a powerful hybrid architecture of linear and standard attention, perfectly balancing high performance with superior efficiency. By integrating our proven MoE design with optimizations like a 1/32 expert activation ratio and MTP layers, Ring-mini-linear achieves the performance of a massive 8 B dense model while activating only 1.4 B parameters.  This model was converted from Ling-mini-base-2.0, further trained on an additional xx B tokens.
 When it comes to benchmarks, Ring-mini-linear-2.0 not only holds its own against standard attention models (like ring-mini-2) but also outperforms other open-source MoE and Dense models in its class on several demanding tasks. Plus, with native support for a 128k long context, it's faster and more precise than ever, especially when handling long-form inputs and outputs.
 <div style="display: flex; justify-content: center;">
   <div style="text-align: center;">
     <img src="https://cdn-uploads.huggingface.co/production/uploads/68d20104a6f8ea66da0cb447/UsAtWWsWB9eXcMxV5iCCa.png" width="600">
-    <p style="margin-top: 8px; font-size: 14px;"><strong>Figure xx:</strong> Hybrid Linear Model Architecture</p>
   </div>
 </div>
 ## Evaluation
 <!-- <img src="https://cdn-uploads.huggingface.co/production/uploads/68d20104a6f8ea66da0cb447/_tjjgBEBlankfrWUY0N9i.png" width="1000"> -->
 <div style="display: flex; justify-content: center;">
   <div style="text-align: center;">
     <img src="https://cdn-uploads.huggingface.co/production/uploads/68d20104a6f8ea66da0cb447/OyvwJdc0qRIJiHM1VJgwO.gif" width="800">
-    <p style="margin-top: 8px; font-size: 14px;"><strong>Figure xx:</strong> demo </p>
   </div>
 </div>
@@ -48,14 +56,14 @@ The results are remarkable. In the prefill stage, Ring-mini-linear-2.0's perform
 <div style="display: flex; justify-content: center; align-items: flex-start; gap: 20px;">
   <div style="text-align: center;">
     <img src="https://cdn-uploads.huggingface.co/production/uploads/68d20104a6f8ea66da0cb447/O9gHLIOCdpWvBbPC6bMM5.webp" width="500">
-    <p style="margin-top: 8px; font-size: 14px;"><strong>Figure xx:</strong> Ring-mini-linear-2.0 prefill throughput</p>
   </div>
   <div style="text-align: center;">
     <p align="center">
       <img src="https://cdn-uploads.huggingface.co/production/uploads/68d20104a6f8ea66da0cb447/AvMTStWFX-Frzv-vOzyr6.webp" width="500">
     </p>
-    <p style="margin-top: 8px; font-size: 14px;"><strong>Figure xx:</strong> Ring-mini-linear-2.0 decode throughput</p>
   </div>
 </div>

 ## Introduction
 We are excited to announce the official open-source release of Ring-mini-linear-2.0!
+Building on the success of our Ling 2.0 series, this model continues to leverage a powerful hybrid architecture of linear and standard attention, perfectly balancing high performance with superior efficiency. By integrating our proven MoE design with optimizations like a 1/32 expert activation ratio and MTP layers, Ring-mini-linear achieves the performance of a 8 B dense model while activating only 1.4 B parameters.  This model was converted from Ling-mini-base-2.0, further trained on an additional 600 B tokens.
 When it comes to benchmarks, Ring-mini-linear-2.0 not only holds its own against standard attention models (like ring-mini-2) but also outperforms other open-source MoE and Dense models in its class on several demanding tasks. Plus, with native support for a 128k long context, it's faster and more precise than ever, especially when handling long-form inputs and outputs.
 <div style="display: flex; justify-content: center;">
   <div style="text-align: center;">
     <img src="https://cdn-uploads.huggingface.co/production/uploads/68d20104a6f8ea66da0cb447/UsAtWWsWB9eXcMxV5iCCa.png" width="600">
+    <p style="margin-top: 8px; font-size: 14px;"><strong>Figure 1:</strong> Hybrid Linear Model Architecture</p>
   </div>
 </div>
 ## Evaluation
 <!-- <img src="https://cdn-uploads.huggingface.co/production/uploads/68d20104a6f8ea66da0cb447/_tjjgBEBlankfrWUY0N9i.png" width="1000"> -->
+<div style="display: flex; justify-content: center;">
+  <div style="text-align: center;">
+    <img src="https://cdn-uploads.huggingface.co/production/uploads/68d20104a6f8ea66da0cb447/_tjjgBEBlankfrWUY0N9i.png" width="800">
+    <p style="margin-top: 8px; font-size: 14px;"><strong>Figure 3:</strong> demo </p>
+  </div>
+</div>
 <div style="display: flex; justify-content: center;">
   <div style="text-align: center;">
     <img src="https://cdn-uploads.huggingface.co/production/uploads/68d20104a6f8ea66da0cb447/OyvwJdc0qRIJiHM1VJgwO.gif" width="800">
+    <p style="margin-top: 8px; font-size: 14px;"><strong>Figure 3:</strong> demo </p>
   </div>
 </div>
 <div style="display: flex; justify-content: center; align-items: flex-start; gap: 20px;">
   <div style="text-align: center;">
     <img src="https://cdn-uploads.huggingface.co/production/uploads/68d20104a6f8ea66da0cb447/O9gHLIOCdpWvBbPC6bMM5.webp" width="500">
+    <p style="margin-top: 8px; font-size: 14px;"><strong>Figure 4:</strong> Ring-mini-linear-2.0 prefill throughput</p>
   </div>
   <div style="text-align: center;">
     <p align="center">
       <img src="https://cdn-uploads.huggingface.co/production/uploads/68d20104a6f8ea66da0cb447/AvMTStWFX-Frzv-vOzyr6.webp" width="500">
     </p>
+    <p style="margin-top: 8px; font-size: 14px;"><strong>Figure 5:</strong> Ring-mini-linear-2.0 decode throughput</p>
   </div>
 </div>