This model is optimized for financial and banking domain conversations, including customer support automation, FAQs, and personalized query handling.

It can handle:

Credit card and loan inquiries
Account opening and transaction queries
Policy explanation and chatbot-style interactions

🚀 Model Overview

Property	Value
Base Model	`microsoft/DialoGPT-medium`
Fine-tuned Dataset	Custom Banking & FinTech dialogues
Framework	Hugging Face Transformers
Trained On	Azure GPU VM (NVIDIA T4)
Fine-tuning Epochs	**
Learning Rate	2e-4
Tokenizer	AutoTokenizer
Total Parameters	~355.61M
Trainable Parameters (LoRA)	~**

🧠 Use Cases

Banking chatbots and conversational AI
Automated customer support (loan, account, credit cards)
FinTech knowledge assistants
Smart reply systems for banking helpdesks

🧩 Example Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("techpro-saida/banking-slm-v1")
model = AutoModelForCausalLM.from_pretrained("techpro-saida/banking-slm-v1")

prompt = "Hi, can you tell me my credit card limit?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100, temperature=0.7)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

#(or) use pipeline
from transformers import pipeline

slm = pipeline(
    "text-generation",
    model="./banking-slm-v1",
    tokenizer=tokenizer,
    max_new_tokens=60,
    temperature=0.6,
    top_p=0.9
)

prompt = "How do I reset my net-banking password?"

result = slm(prompt)
print(result[0]["generated_text"])