Create README.md

# 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

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### 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

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### 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)

README.md

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+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