Instructions to use bhushanrocks/supportpal-dialoGPT-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bhushanrocks/supportpal-dialoGPT-v3 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="bhushanrocks/supportpal-dialoGPT-v3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bhushanrocks/supportpal-dialoGPT-v3")
model = AutoModelForCausalLM.from_pretrained("bhushanrocks/supportpal-dialoGPT-v3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bhushanrocks/supportpal-dialoGPT-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bhushanrocks/supportpal-dialoGPT-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bhushanrocks/supportpal-dialoGPT-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bhushanrocks/supportpal-dialoGPT-v3

SGLang

How to use bhushanrocks/supportpal-dialoGPT-v3 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "bhushanrocks/supportpal-dialoGPT-v3" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bhushanrocks/supportpal-dialoGPT-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "bhushanrocks/supportpal-dialoGPT-v3" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bhushanrocks/supportpal-dialoGPT-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bhushanrocks/supportpal-dialoGPT-v3 with Docker Model Runner:
```
docker model run hf.co/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)

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Safetensors

Model size

0.4B params

Tensor type

F32

Model tree for bhushanrocks/supportpal-dialoGPT-v3

Base model

microsoft/DialoGPT-medium

Finetuned

(94)

this model

bhushanrocks
/

supportpal-dialoGPT-v3