---
language: en
tags:
- mental-health
- healthcare
- conversational
- wellness
- SmolLM-135M
- fine-tuned
license: mit
pipeline_tag: text-generation
---

# Wellness-Mini: Mental Health Conversational AI

## Model Description
Wellness-Mini is a fine-tuned version of SmolLM-135M, specifically adapted for mental health conversations and assessments. The model is designed to:
- Provide mental health assessments based on user conversations
- Identify potential indicators of ADHD and Bipolar disorder
- Analyze emotional indicators and stress levels

## Base Model
- Original model: SmolLM-135M (HuggingFaceTB/SmolLM-135M)
- Architecture: Causal Language Model
- Size: 135M parameters

## Training Data
The model was trained on a curated dataset including:
- Reddit posts from mental health communities (ADHD and Bipolar subreddits)
- Professional medical prescriptions and guidelines
- Identity training examples
- Emotional and behavioral indicators

## Features
1. **Mental Health Assessment**
   - Primary condition identification
   - Additional symptom indicators
   - Emotional state analysis

2. **Identity Awareness**
   - Consistently identifies as a Sentinet AI Systems creation
   - Clear AI identity in responses

3. **Clinical Indicators Analysis**
   - Sentiment analysis
   - Anxiety levels
   - Stress indicators
   - Emotional valence

## Usage
``` python
from transformers import AutoModelForCausalLM, AutoTokenizer
Load model and tokenizer
model_name = "ombhojane/wellness-mini"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
Example usage
messages = [{"role": "user", "content": "Who created you?"}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(inputs, max_new_tokens=50)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
```


## Limitations
- This is an AI assistant and not a replacement for professional medical advice
- Should be used as a supplementary tool only
- May not be suitable for emergency situations
- Responses should be verified by healthcare professionals

## Ethical Considerations
- The model should be used responsibly in mental health contexts
- User privacy and data protection should be prioritized
- Clear disclosure of AI nature in healthcare applications

## Creator
This model was developed by Om Bhojane.

## Citation
If you use this model in your research or application, please cite:
```
@misc{wellness-mini,
author = {Om Bhojane},
title = {Wellness-Mini: Mental Health Conversational AI},
year = {2024},
publisher = {HuggingFace},
journal = {HuggingFace Hub},
howpublished = {\url{https://huggingface.co/ombhojane/wellness-mini}}
}

```