ParaThinker-1.5B is a 1.5 billion parameter language model designed for efficient mathematical reasoning through native parallel thinking. Built upon the DeepSeek-R1-Distill-Qwen-1.5B base model, it introduces specialized training to support up to 8 parallel reasoning paths thinking, leveraging KV-cache reuse via PagedAttention in vLLM. This model is detailed in our paper:
ParaThinker: Native Parallel Thinking as a New
Paradigm to Scale LLM Test-time ComputeThis modelcard aims to be a base template for new models. It has been generated using
this raw template.
ParaThinker-1.5B enhances small-scale LLMs by enabling parallel reasoning paths with minimal latency overhead. It uses special tokens (<think1> ~ <think8>) to boost thought diversity and a summarization template for coherent final answers. The model excels in math reasoning tasks, achieving much higher accuracy than sequential baselines on benchmarks like AIME.
ParaThinker-1.5B is intended for mathematical reasoning tasks, such as solving problems from AIME, AMC, or MATH-500 datasets. It can be used directly with the vLLM-based ParaThinker inference engine to generate diverse reasoning paths and a summarized final answer. See
docs to learn more.
