Ring-lite-2506

👁 Image

🤗 Hugging Face

Introduction

Ring-lite-2506 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 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.

Model Downloads

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.

👁 Image

More details are reported in our technical report.

Quickstart

🤗 Hugging Face Transformers

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

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "inclusionAI/Ring-lite-2506"

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
)
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-2506 is release at Ring-lite-sft-data and Ring-lite-rl-data.

Deployment

Please refer to GitHub

License

This code repository is licensed under the MIT 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}, 
}

Base model