GMLHUHE/PsyLLM-4B

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)

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📄 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}
}

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🧩 License

For research and educational use only.

Please ensure compliance with ethical and legal standards in mental health AI research.