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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: Qwen/Qwen2.5-7B-Instruct |
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tags: |
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- llama-factory |
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- verl |
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- grpo-training |
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model-index: |
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- name: Psyche-R1 |
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results: [] |
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language: |
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- zh |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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<div style="display: flex; align-items: center;"> |
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<img src="logo.png" alt="Psyche-R1 logo" style="height: 2em; margin-right: 10px;"> |
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<h1 style="margin: 0;">Psyche-R1</h1> |
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</div> |
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We propose the first Chinese psychological reasoning LLM that unifies empathy, expertise, and reasoning. |
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This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on our proposed dataset encompassing psychological questions paired with detailed rationales, and empathetic single-turn dialogues. |
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We conduct a hybrid training strategy, including SFT and GRPO training. We will present detailed training hyperparameters later. |
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It achieves comparable performance to DeepSeek-R1 on several psychology benchmarks, including psychology counselor examination benchmark (PCEB) proposed by [Hu et al. (2024)](https://ieeexplore.ieee.org/abstract/document/10772313), and CPsyExam test set proposed by [Zhao et al. (2024)](https://aclanthology.org/anthology-files/anthology-files/pdf/coling/2025.coling-main.745.pdf). It also demonstates better performance in empathy on [SoulChat2.0 test set (Xie et al. 2025)](https://aclanthology.org/2025.acl-long.55.pdf). |
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## Training procedure |
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### SFT Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 8 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 256 |
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- total_eval_batch_size: 8 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 2.0 |
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### GRPO Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-06 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 8 |
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- total_train_batch_size: 128 |
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- total_eval_batch_size: 8 |
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- ppo_mini_batch_size: 32 |
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- ppo_micro_batch_size_per_gpu: 20 |
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- kl_loss_coef: 0.001 |
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- lr_scheduler_warmup_steps: 10 |
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- num_epochs: 2.0 |
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## Usage |
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For quick start, please see [MindIntLab-HFUT/Psyche-R1](https://github.com/MindIntLab-HFUT/Psyche-R1) on GitHub. |
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## Citation |
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If this work is helpful, please kindly cite as: |
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```bibtex |
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@article{dai2025psyche, |
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title={Psyche-R1: Towards Reliable Psychological LLMs through Unified Empathy, Expertise, and Reasoning}, |
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author={Dai, Chongyuan and Hu, Jinpeng and Shi, Hongchang and Li, Zhuo and Yang, Xun and Wang, Meng}, |
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journal={arXiv preprint arXiv:2508.10848}, |
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year={2025} |
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} |
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``` |
