| --- |
| license: mit |
| language: |
| - zh |
| - en |
| base_model: |
| - inclusionAI/Ling-lite-base-1.5 |
| pipeline_tag: text-generation |
| library_name: transformers |
| --- |
| # 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}, |
| } |
| ``` |
