README.md

---
license: mit
language:
- zh
- en
base_model:
- inclusionAI/Ling-lite-base-1.5
---
# Ring-lite-2507

<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>
<p>

## Introduction

We present **Ring-lite-2507**, an upgraded version of our previously released lightweight reasoning model, **Ring-lite**. Building upon 16.8B Mixture-of-Experts (MoE)-based large language model with 2.75B activated parameters, Ring-lite-2507 further pushes its reasoning ability to an advanced level, meanwhile, it demonstrates superior performance on a comprehensive range of LLM benchmarks, including general text understanding, alignment, coding, logical and agentic tasks. Thanks to our innovative and robust reinforcement learning training pipeline, Ring-lite-2507 distinguished itself from latest public dense models under 10B parameters by showing competitive performance across various tasks while activating only 1/3 of their parameter size.  


## Model Downloads

<div align="center">

|     **Model**      | **#Total Params** | **#Activated Params** | **Context Length** | **Download** |
| :----------------: | :---------------: | :-------------------: | :----------------: | :----------: |
| Ring-lite-2507 |       16.8B       |         2.75B         |        128K         |      [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ring-lite-2507) |
| Ring-lite |       16.8B       |         2.75B         |        128K         |      [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ring-lite) |

</div>

## Evaluation
For a comprehensive evaluation of the quality of our reasoning models, we implemented automatic benchmarks to assess their performance including math, code and science.

<p align="center">
    <img src="https://mdn.alipayobjects.com/huamei_qa8qxu/afts/img/A*4FXmTLeqUWMAAAAARqAAAAgAemJ7AQ/original" width="1000"/>
<p>

To compare the performance of Ring-lite-2507 and Ring-lite, we evaluate the two models on a broader range of reasoning and general-purpose benchmarks, including knowledge understanding, math, coding, reasoning & agentic and alignment. 

### Knowledge Understanding

| **Benchmark**   | **Ring-lite-2507** | **Ring-lite-2506** | **Qwen3-8B-Thinking** 
| :-------------: | :---------------: | :-----------: | :-------------------: | 
| MMLU-Pro (EM)         | 72.50	    | 63.44	    | **72.56** | 
| GPQA-Diamond (Pass@1) | **69.35**	    | 63.51	    | 62.00 | 
| SuperGPQA (EM)        | 40.05	    | 13.97	    | **40.36** | 
| Phybench (Pass@1)     | 28.51	    | **29.19**    | 22.14 |    


### Math

| **Benchmark**   | **Ring-lite-2507** | **Ring-lite-2506** | **Qwen3-8B-Thinking** 
| :-------------: | :---------------: | :-----------: | :-------------------: | 
| MATH-500 (Pass@1)             |   **97.95**	|   96.80	|   97.30       |
| CNMO 2024 (Pass@1)            |   75.09	|   **77.26**	|   74.57       |
| AIME 2024 (Pass@1)            |   **79.79**	|   79.00	|   74.90       |
| AIME 2025 (Pass@1)            |   **72.92**	|   69.50	|   67.19       |
| LiveMathBench (Pass@1)        |   83.37	|   **85.08**	|   81.90       |
| TheoremQA (Pass@1)            |   70.00	|   **70.19**	|   68.81       |
| OlympiadBench (math) (Pass@1) |   80.64	|   **82.86**	|   80.20       |

### Coding 

| **Benchmark**   | **Ring-lite-2507** | **Ring-lite-2506** | **Qwen3-8B-Thinking** 
| :-------------: | :---------------: | :-----------: | :-------------------: |
| LiveCodeBench(2408-2505) (Pass@1)     |**60.35**	    |   59.53   |	55.12   |
| Codeforces(Percentile) (Pass@1)       |**1830**	    |   1673    |	1580    |
| Codeforces(Rating)                    |**92.16**	    |   88.00   |	79.44   |

### Reasoning \& Agentic

| **Benchmark**   | **Ring-lite-2507** | **Ring-lite-2506** | **Qwen3-8B-Thinking** 
| :-------------: | :---------------: | :-----------: | :-------------------: | 
| DROP (zero-shot F1)    |   **89.27**  	  | 60.21   |	87.13   |
| BBH (EM)               |   **88.65**	  | 50.84   |	87.30   |
| ARCPrize (Pass@1)      |   **19.00**	  | 3.12    |	3.88    |
| MuSR (EM)              |   **77.19**	  | 66.77   |	76.92   |
| BFCL_Live (Pass@1)     |   74.81	  | 66.76   |	**75.99**   |

### Alignment

| **Benchmark**   | **Ring-lite-2507** | **Ring-lite-2506** | **Qwen3-8B-Thinking** 
| :-------------: | :---------------: | :-----------: | :-------------------: | 
| IFEval (Prompt Strict)    |   84.66   |   54.34   |	**85.40**   |
| AlignBench v1.1(gpt-4.1)  |   **80.90**   |	69.60   |	74.70   |
| FoFo (gpt-4-turbo)        |   **85.02**	|   67.81   |	81.93   |
| ArenaHard (gpt-4.1)       |   **88.85**	|   56.12   |	86.14   |



### Blog
More details are reported in our [blog](https://inclusionai.github.io/blog/ring-lite-2507/).

## Quickstart

### 🤗 Hugging Face Transformers

Here is a code snippet to show you how to use the chat model with `transformers`:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "inclusionAI/Ring-lite-2507"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Give me a short introduction to large language models."
messages = [
    {"role": "system", "content": "You are Ring, an assistant created by inclusionAI"},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=8192
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```


## Deployment
Please refer to [GitHub](https://github.com/inclusionAI/Ring/blob/main/README.md)

## License
This code repository is licensed under [the MIT License](https://huggingface.co/inclusionAI/Ring-lite-2507/blob/main/LICENSE).

## Citation
```
@misc{ringteam2025ringlitescalablereasoningc3postabilized,
      title={Ring-lite: Scalable Reasoning via C3PO-Stabilized Reinforcement Learning for LLMs}, 
      author={Ling Team},
      year={2025},
      eprint={2506.14731},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2506.14731}, 
}
```