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README.md

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-PsyLlama: A Conversational AI for Mental Health Assessment
+# PsyLlama: A Conversational AI for Mental Health Assessment
 
-Model Name: PsyLlama
-Model Architecture: LLaMA-based model (fine-tuned)
-Model Type: Instruct-tuned, conversational AI model
-Primary Use: Mental health assessment through psychometric analysis
+**Model Name**: `PsyLlama`  
+**Model Architecture**: LLaMA-based model (fine-tuned)  
+**Model Type**: Instruct-tuned, conversational AI model  
+**Primary Use**: Mental health assessment through psychometric analysis
+
+---
+
+### Model Description
+
+**PsyLlama** is a conversational AI model based on LLaMA architecture, fine-tuned for mental health assessments. It is designed to assist healthcare professionals in conducting initial psychometric evaluations and mental health assessments by generating context-aware conversational responses. The model uses structured questions and answers to assess patients' mental states and supports clinical decision-making in telemedicine environments.
+
+**Applications**:
+- Psychometric evaluation
+- Mental health chatbot
+- Symptom analysis for mental health assessment
+
+---
+
+### Model Usage
+
+To use **PsyLlama**, you can load it from Hugging Face using the `transformers` library. Below is a code snippet showing how to initialize and use the model:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the model and tokenizer from Hugging Face
+model_name = "Nevil9/PsyLlama"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Example input
+input_text = "How are you feeling today? Have you been experiencing any anxiety or stress?"
+
+# Tokenize input and generate response
+inputs = tokenizer(input_text, return_tensors="pt")
+output = model.generate(**inputs, max_length=100)
+
+# Decode and print the response
+response = tokenizer.decode(output[0], skip_special_tokens=True)
+print(response)