--- title: Ring-lite-2507 canonical_url: "https://www.modelscope.cn/models/inclusionAI/Ring-lite-2507" md_url: "https://www.modelscope.cn/models/inclusionAI/Ring-lite-2507.md" repository: inclusionAI/Ring-lite-2507 chinese_name: Ring-lite-2507 last_updated: 2025-12-30 license: mit pipeline_tag: text-generation tasks: - text-generation model_type: - bailing_moe architectures: - BailingMoeForCausalLM base_model: - inclusionAI/Ling-lite-base-1.5 base_model_relation: finetune parameters: 16.8B tensor_type: - BF16 library_name: - transformer - safetensors - pytorch frameworks: - Pytorch language: - zh - en inference_backends: - "deploy_task text" - "sglang 0.5.2" downloads: 211 stars: 2 --- # Ring-lite-2507 > Ring-lite-2507 - inclusionAI 在 ModelScope 开源的模型。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… inclusionAI/Ring-lite-2507 是 ModelScope 魔搭社区上的 16.8B 参数text-generation模型，采用 mit 许可，基于 inclusionAI/Ling-lite-base-1.5 构建，可用 deploy_task text、sglang 0.5.2 部署。 - **Repository**: inclusionAI/Ring-lite-2507 - **License**: mit - **Tasks**: text-generation - **Parameters**: 16.8B - **Base model**: inclusionAI/Ling-lite-base-1.5 - **Inference backends**: deploy_task text, sglang 0.5.2 - **Downloads**: 211 - **Stars**: 2 - **Last updated**: 2025-12-30 Source: https://www.modelscope.cn/models/inclusionAI/Ring-lite-2507 --- # Ring-lite-2507

🤗 Hugging Face

## 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

| **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) |
## 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.

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}, } ```