--- title: Ring-lite canonical_url: "https://www.modelscope.cn/models/inclusionAI/Ring-lite" md_url: "https://www.modelscope.cn/models/inclusionAI/Ring-lite.md" repository: inclusionAI/Ring-lite 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: 299 stars: 3 --- # Ring-lite > Ring-lite - inclusionAI 在 ModelScope 开源的模型。Ring-lite is a lightweight, fully open-sourced MoE (Mixture of Experts) LLM designed for complex reasoning tasks. It is built upon the publicly available Ling-lite-1.5 model, which has 16.8B parameters with 2.75B… inclusionAI/Ring-lite 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 - **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**: 299 - **Stars**: 3 - **Last updated**: 2025-12-30 Source: https://www.modelscope.cn/models/inclusionAI/Ring-lite --- # Ring-lite

🤗 Hugging Face

## Introduction Ring-lite is a lightweight, fully open-sourced MoE (Mixture of Experts) LLM designed for complex reasoning tasks. It is built upon the publicly available [Ling-lite-1.5](https://huggingface.co/inclusionAI/Ling-lite-1.5) model, which has 16.8B parameters with 2.75B activated parameters.. We use a joint training pipeline combining knowledge distillation with reinforcement learning, achieving performance comparable to state-of-the-art (SOTA) small-size reasoning models on challenging benchmarks (AIME, LiveCodeBench, and GPQA-Diamond) while activating only one-third of their parameters. ## News [20250704] Ring-lite-0704: we update Ring-lite model, which supports two distinct reasoning modes: "**thinking on**" and "**thinking off**". ## Model Downloads

| **Model** | **#Total Params** | **#Activated Params** | **Context Length** | **Download** | | :----------------: | :---------------: | :-------------------: | :----------------: | :----------: | | 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-0704 and Ring-lite-0616, we evaluate the two models on a broader range of reasoning and general-purpose benchmarks, including instruction following, function calling, and creative writing. | **Dataset** | **Ring-lite-0616** | **Ring-lite-0704** | | :---------: | :----------------: | :----------------: | | AIME 2024 | 76.6 | 79.0 | | AIME 2025 | 69.1 | 69.5 | | LiveCodeBench | 60.7 | 61.4 | | Codeforces (percentile) | 86.5 | 88.0 | | GPQA Diamond | 61.1 | 63.2 | | C-Eval | 59.0 | 65.4 | | MMLU-Pro | 60.0 | 63.0 | | ArenaHard | 27.8 | 62.7 | | IF-Eval | 51.6 | 54.3 | | BFCL_Live | 60.1 | 66.8 | | Creative Writing | 6.7 | 60.2 | More details are reported in our [technical report](https://arxiv.org/abs/2506.14731). ## Quickstart ### 🤗 Hugging Face Transformers The newly updated **Ring-lite** model now supports two distinct reasoning modes: "**thinking on**" and "**thinking off**". These modes are controlled by the `enable_thinking` parameter in the `tokenizer.apply_chat_template()` function. * When `enable_thinking` is set to `True` (or omitted), the model operates in "**thinking on**" mode, where it generates and outputs the internal reasoning process. * When `enable_thinking` is explicitly set to `False`, the model runs in "**thinking off**" mode, skipping the reasoning step entirely and directly producing the final answer. This feature allows users to choose between detailed reasoning and concise output based on their specific needs. Here is a code snippet to show you how to use the chat model with `transformers`: #### Thinking on ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "inclusionAI/Ring-lite" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) 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] ``` #### Thinking off ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "inclusionAI/Ring-lite" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) 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=False ) 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] ``` ## Dataset The training data of Ring-lite is release at [Ring-lite-sft-data](https://huggingface.co/datasets/inclusionAI/Ring-lite-sft-data) and [Ring-lite-rl-data](https://huggingface.co/datasets/inclusionAI/Ring-lite-rl-data). ## Code The training code will be released soon. ## 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/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}, } ```