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README.md

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 license: apache-2.0
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+# MindGLM: A Fine-tuned Language Model for Chinese Psychological Counseling
+1. Introduction
+MindGLM is a large language model fine-tuned and aligned for the task of psychological counseling in Chinese. Developed from the foundational model ChatGLM2-6B, MindGLM is designed to resonate with human preferences in psychological inquiries, offering a reliable and safe tool for digital psychological counseling.
+
+2. Key Features
+Fine-tuned for Counseling: MindGLM has been meticulously trained to understand and respond to psychological inquiries, ensuring empathetic and accurate responses.
+
+Aligned with Human Preferences: The model underwent a rigorous alignment process, ensuring its responses are in line with human values and preferences in the realm of psychological counseling.
+
+High Performance: MindGLM has demonstrated superior performance in both quantitative and qualitative evaluations, making it a leading choice for digital psychological interventions.
+
+4. Usage
+To use MindGLM with the Hugging Face Transformers library:
+
+'''
+    python
+    Copy code
+    from transformers import AutoTokenizer, AutoModelForCausalLM
+    
+    tokenizer = AutoTokenizer.from_pretrained("ZhangCNN/MindGLM")
+    model = AutoModelForCausalLM.from_pretrained("ZhangCNN/MindGLM")
+    
+    input_text = "Your input text here"
+    input_ids = tokenizer.encode(input_text, return_tensors="pt")
+    output = model.generate(input_ids)
+    decoded_output = tokenizer.decode(output[0], skip_special_tokens=True)
+    print(decoded_output)
+'''
+
+5. Training Data
+MindGLM was trained using a combination of open-source datasets and self-constructed datasets, ensuring a comprehensive understanding of psychological counseling scenarios. The datasets include SmileConv, comparison_data_v1, psychology-RLAIF, rm_labelled_180, and rm_gpt_375.
+
+6. Training Process
+The model underwent a three-phase training approach:
+
+Supervised Fine-tuning: Using the ChatGLM2-6B foundational model, MindGLM was fine-tuned with a dedicated dataset for psychological counseling.
+
+Reward Model Training: A reward model was trained to evaluate and score the responses of the fine-tuned model.
+
+Reinforcement Learning: The model was further aligned using the PPO (Proximal Policy Optimization) algorithm to ensure its responses align with human preferences.
+
+7. Limitations
+While MindGLM is a powerful tool, users should be aware of its limitations:
+
+It is designed for psychological counseling but should not replace professional medical advice or interventions.
+
+The model's responses are based on the training data, and while it's aligned with human preferences, it might not always provide the most appropriate response.
+
+8. License
+Please refer to the licensing terms of the datasets used for training. Usage of MindGLM should be in compliance with these licenses.license: apache-2.0