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Model: klyang/MentaLLaMA-chat-7B-hf
Source: Original Platform

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README.md

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+---
+license: mit
+language:
+- en
+metrics:
+- f1
+tags:
+- medical
+---
+
+# Introduction
+
+MentaLLaMA-chat-7B 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 Meta LLaMA2-chat-7B foundation model and the full IMHI instruction tuning data.
+The model is expected to make complex mental health analysis 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-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.
+
+## Usage
+
+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:
+
+```python
+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.
+
+## 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:
+
+```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}
+}
+```

config.json

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+{
+  "_name_or_path": "./llama2-chat-checkpoint-7B-hf",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 4096,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.36.2",
+  "use_cache": false,
+  "vocab_size": 32000
+}

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+  "top_p": 0.9,
+  "transformers_version": "4.36.2"
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