Mentallama Chat 7B Hf

🚀 MentaLLaMA-chat-7B

MentaLLaMA-chat-7B is an open - source large language model for interpretable mental health analysis with instruction - following capability, finetuned on Meta LLaMA2 - chat - 7B and IMHI data.

🚀 Quick Start

You can use the MentaLLaMA-chat-7B model in your Python project with the Hugging Face Transformers library. Here is a simple example of how to load the model:

Basic Usage

from transformers import LlamaTokenizer, LlamaForCausalLM
tokenizer = LlamaTokenizer.from_pretrained('klyang/MentaLLaMA-chat-7B')
model = LlamaForCausalLM.from_pretrained('klyang/MentaLLaMA-chat-7B', 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.

✨ Features

• Interpretable Mental Health Analysis: MentaLLaMA-chat-7B is designed to make complex mental health analysis for various mental health conditions and give reliable explanations for each of its predictions.
• Instruction - Following Capability: Finetuned on high - quality natural language instructions, it can follow instructions to perform downstream tasks.
• High - Quality Performance: Comprehensive evaluation on the IMHI benchmark with 20K test samples shows that it approaches state - of - the - art discriminative methods in correctness and generates high - quality explanations.

📚 Documentation

Introduction

MentaLLaMA-chat-7B is part of the MentaLLaMA project, the first open - source large language model (LLM) series for interpretable mental health analysis with instruction - following capability. This model is finetuned based on the Meta LLaMA2 - chat - 7B foundation model and the full IMHI instruction tuning data. It is fine - tuned on the IMHI dataset with 75K high - quality natural language instructions to boost its performance in downstream tasks.

Ethical Consideration

Although experiments on MentaLLaMA show promising performance on interpretable mental health analysis, we stress that all predicted results and generated explanations should only be used for non - clinical research, and the help - seeker should get assistance from professional psychiatrists or clinical practitioners. In addition, recent studies have indicated LLMs may introduce some potential bias, such as gender gaps. Meanwhile, some incorrect prediction results, inappropriate explanations, and over - generalization also illustrate the potential risks of current LLMs. Therefore, there are still many challenges in applying the model to real - scenario mental health monitoring systems.

Other Models in MentaLLaMA

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

• MentaLLaMA - chat - 13B: This model is finetuned based on the Meta LLaMA2 - chat - 13B foundation model and the full IMHI instruction tuning data. The training data covers 10 mental health analysis tasks.
• MentalBART: This model is finetuned based on the BART - large foundation model and the full IMHI - completion data. The training data covers 10 mental health analysis tasks. This model doesn't have instruction - following ability but is more lightweight and performs well in interpretable mental health analysis in a completion - based manner.
• MentalT5: This model is finetuned based on the T5 - large foundation model and the full IMHI - completion data. The training data covers 10 mental health analysis tasks. This model doesn't have instruction - following ability but is more lightweight and performs well in interpretable mental health analysis in a completion - based manner.

📄 License

MentaLLaMA-chat-7B is licensed under MIT. For more details, please see the MIT file.

📚 Citation

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

@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}
}

⚠️ Important Note

All predicted results and generated explanations of MentaLLaMA should only be used for non - clinical research, and help - seekers should get assistance from professional psychiatrists or clinical practitioners. Also, current LLMs may introduce potential bias and have other risks.

💡 Usage Tip

When using the MentaLLaMA-chat-7B model, make sure your Python environment has the Hugging Face Transformers library installed. You can use the device_map='auto' argument to take advantage of GPU acceleration if available.