Update README.md

README.md

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 ---
 license: mit
+language:
+- en
+metrics:
+- f1
+tags:
+- medical
 ---
+# Introduction
+
+MentaLLaMA-33B-lora 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. This model is finetuned based on the Vicuna-33B foundation model and the full IMHI instruction tuning data,
+but tuned with LoRA due to limited computational resources.
+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.
+
+# 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 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-33B-lora, the MentaLLaMA project includes another model: MentaLLaMA-chat-7B, 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.
+  
+- **MentaLLaMA-chat-7B**: This model is finetuned based on the Meta LLaMA2-chat-7B 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.
+
+## Usage
+
+You can use the MentaLLaMA-33B-lora model in your Python project with the Hugging Face Transformers library. Here is a simple example of how to load the model:
+
+Since our model is based on the Vicuna-33B foundation model, you need to first download the Vicuna-33B model [here](https://huggingface.co/lmsys/vicuna-33b-v1.3), 
+and put it under the `./Vicuna-33B` dir.
+
+```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-33B-lora is licensed under MIT. For more details, please see the MIT file.
+
+## Citation
+
+If you use MentaLLaMA-33B-lora 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}
+}
+```