PsyLLM is a large language model designed for psychological counseling and mental health dialogue generation. 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.
The model is trained on the OpenR1-Psy dataset (arXiv:2505.15715),
featuring multi-turn counseling dialogues with explicit reasoning traces that support clinically informed, empathetic, and interpretable AI-assisted therapy.
The training process is implemented based on the open-source framework LLaMA-Factory. If you find this project helpful, feel free to ⭐ it! PsyLLM
推理示例代码
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "GMLHUHE/PsyLLM-4B"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
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."
messages = [
{"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)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("PsyLLM thinking content:", thinking_content)
print("PsyLLM content:", content)
📄 Citation
If you use this dataset, please cite:
@article{hu2025beyond,
title={Beyond Empathy: Integrating Diagnostic and Therapeutic Reasoning with Large Language Models for Mental Health Counseling},
author={Hu, He and Zhou, Yucheng and Si, Juzheng and Wang, Qianning and Zhang, Hengheng and Ren, Fuji and Ma, Fei and Cui, Laizhong},
journal={arXiv preprint arXiv:2505.15715},
year={2025}
}
🧩 License
For research and educational use only.
Please ensure compliance with ethical and legal standards in mental health AI research.