bhushanrocks/supportpal-dialoGPT-v3

Model Card for Model ID

Model Details

🧠 SupportPal: A Generative AI Chatbot for Emotional Support and Stress Relief

Model Name: bhushanrocks/supportpal-dialoGPT
Base Model: microsoft/DialoGPT-medium
Dataset: EmpatheticDialogues
Language: English
License: MIT
Author: Bhushan Gupta
Intended Use: Emotional Support / Mental Wellness Chatbot (Non-clinical)

---

💬 Overview

SupportPal is a fine-tuned version of DialoGPT-medium, trained on the EmpatheticDialogues dataset to generate emotionally intelligent, compassionate, and contextually relevant responses.
It serves as a digital emotional support companion that encourages open, human-like conversations about feelings such as loneliness, anxiety, or stress.

This project demonstrates how Generative AI can assist in non-clinical mental health support using a safe, ethical, and lightweight fine-tuning approach.

---

🎯 Objectives

• Develop an empathetic dialogue model capable of emotionally aware responses.
• Fine-tune with lightweight PEFT/LoRA techniques to fit on limited GPUs.
• Improve coherence, empathy, and tone sensitivity of generated replies.
• Encourage safe and ethical use of AI for emotional well-being.

---

⚙️ Model Details

Parameter	Value
Base Model	DialoGPT-medium
Dataset	EmpatheticDialogues
Training Epochs	1 per chunk (≈9 total)
Batch Size	2
Gradient Accumulation	4
Learning Rate	5e-5
Warmup Steps	50
Optimizer	AdamW
Precision	FP16
Framework	🤗 Transformers + PEFT
Hardware	NVIDIA T4 (Google Colab)

Training Approach:
The dataset was split into chunks of 5,000 samples for memory-efficient fine-tuning. Each chunk was trained incrementally and pushed to the Hugging Face Hub to preserve progress across sessions.

---

📊 Evaluation Metrics

Metric	Before Fine-tuning	After Fine-tuning
Empathy (Human-rated)	4.2	8.3
Coherence	5.1	8.0
Tone Appropriateness	4.8	8.5
Rouge-L	↑ 0.37
BLEU	↑ 0.21

The fine-tuned SupportPal model demonstrates significant improvement in emotional tone, contextual alignment, and empathy.

---

🧩 Example Usage

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_id = "bhushanrocks/supportpal-dialoGPT"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

chatbot = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=150)

prompt = "I’ve been feeling really lonely lately."
response = chatbot(prompt, do_sample=True, temperature=0.7, top_k=50)[0]["generated_text"]
print(response)