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--- |
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library_name: transformers |
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license: other |
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base_model: Qwen/Qwen3-8B |
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tags: |
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- llama-factory |
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- full |
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- generated_from_trainer |
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--- |
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**PsyLLM** is a large language model designed for **psychological counseling and mental health dialogue generation**. |
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It integrates **diagnostic reasoning** and **therapeutic reasoning**, following established frameworks such as **DSM/ICD**, and incorporates diverse therapeutic approaches including **CBT**, **ACT**, and **psychodynamic therapy**. |
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The model is trained on the [**OpenR1-Psy**](https://huggingface.co/datasets/GMLHUHE/OpenR1-Psy) dataset ([arXiv:2505.15715](https://arxiv.org/abs/2505.15715)), |
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featuring multi-turn counseling dialogues with explicit reasoning traces that support **clinically informed, empathetic, and interpretable** AI-assisted therapy. |
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The training process is implemented based on the open-source framework [**LLaMA-Factory**](https://github.com/hiyouga/LLaMA-Factory). |
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## 推理示例代码 |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_path = "GMLHUHE/PsyLLM" |
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# load the tokenizer and the model |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_path, |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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# prepare the model input |
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prompt = "I have participated in big group sessions before where I was left to find my own safe place, but it hasn't worked for me." |
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messages = [ |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True, |
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enable_thinking=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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# conduct text completion |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=32768 |
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) |
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output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() |
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# parsing thinking content |
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try: |
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index = len(output_ids) - output_ids[::-1].index(151668) |
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except ValueError: |
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index = 0 |
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thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n") |
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content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") |
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print("PsyLLM thinking content:", thinking_content) |
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print("PsyLLM content:", content) |
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``` |
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--- |
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## 📄 Citation |
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If you use this dataset, please cite: |
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```bibtex |
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@article{hu2025beyond, |
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title={Beyond Empathy: Integrating Diagnostic and Therapeutic Reasoning with Large Language Models for Mental Health Counseling}, |
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author={Hu, He and Zhou, Yucheng and Si, Juzheng and Wang, Qianning and Zhang, Hengheng and Ren, Fuji and Ma, Fei and Cui, Laizhong}, |
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journal={arXiv preprint arXiv:2505.15715}, |
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year={2025} |
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} |
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```` |
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--- |
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## 🧩 License |
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For **research and educational use only.** |
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Please ensure compliance with **ethical and legal standards** in mental health AI research. |
