---
license: mit
language:
- en
metrics:
- f1
tags:
- medical
---

# MentaLLaMA-chat-13B

MentaLLaMA-chat-13B is part of the [MentaLLaMA](https://github.com/SteveKGYang/MentalLLaMA) project, the first open-source large language model (LLM) series for 
interpretable mental health analysis with instruction-following capability.
The model is expected to make complex mental health analyses for various mental health conditions and give reliable explanations for each of its predictions.
It is fine-tuned on the IMHI dataset with 75K high-quality natural language instructions to boost its performance in downstream tasks.
We perform a comprehensive evaluation on the IMHI benchmark with 20K test samples. The result shows that MentalLLaMA approaches state-of-the-art discriminative
methods in correctness and generates high-quality explanations.

## Other Models in MentaLLaMA

In addition to MentaLLaMA-chat-13B, the MentaLLaMA project includes another model: MentaLLaMA-chat-7B, MentalBART, MentalT5.

- **MentaLLaMA-chat-7B**: This model 

- **MentalBART**: This model 

- **MentalT5**: This model 

## Usage

You can use the MentaLLaMA-chat-13B model in your Python project with the Hugging Face Transformers library. Here is a simple example of how to load the model:

```python
from transformers import LlamaTokenizer, LlamaForCausalLM
tokenizer = LlamaTokenizer.from_pretrained('klyang/MentaLLaMA-chat-13B')
model = LlamaForCausalLM.from_pretrained('klyang/MentaLLaMA-chat-13B', device_map='auto')
```

In this example, LlamaTokenizer is used to load the tokenizer, and LlamaForCausalLM is used to load the model. The `device_map='auto'` argument is used to automatically
use the GPU if it's available.

## License

MentaLLaMA-chat-13B is licensed under MIT. For more details, please see the MIT file.

## About

This model is part of the MentaLLaMA project.
For more information, you can visit the [MentaLLaMA](https://github.com/SteveKGYang/MentalLLaMA) project on GitHub.

## Citation

If you use MentaLLaMA-chat-7B in your work, please cite our paper:

```bibtex
@misc{yang2023mentalllama,
      title={MentalLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Models}, 
      author={Kailai Yang and Tianlin Zhang and Ziyan Kuang and Qianqian Xie and Sophia Ananiadou},
      year={2023},
      eprint={2309.13567},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```