DialoGPT-FinTech-Investment-Banking-Assistant

Fine-tuned DialoGPT-medium for financial conversations, investment advice, and banking operations using LoRA on finance-specific dataset.

Overview

• Base Model: microsoft/DialoGPT-medium (355M parameters)
• Fine-tuning Method: LoRA (4-bit quantization)
• Dataset: Financial Alpaca dataset (2K finance samples)
• Training: 2 epochs with optimized hyperparameters

Key Features

• Financial market analysis and investment guidance
• Banking operations and regulatory compliance
• Risk assessment and portfolio management
• Conversational interface for financial queries
• Optimized for UK fintech and investment banking

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("sweatSmile/DialoGPT-FinTech-Investment-Banking-Assistant")
tokenizer = AutoTokenizer.from_pretrained("sweatSmile/DialoGPT-FinTech-Investment-Banking-Assistant")

# Financial conversation example
prompt = "<|user|> What are the key risks in equity trading? <|bot|>"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200, pad_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Applications

• Investment banking client advisory
• Fintech customer support automation
• Financial education and training
• Risk management consultation
• Portfolio analysis assistance

Training Details

• LoRA rank: 8, alpha: 16
• 4-bit quantization with fp16 precision
• Learning rate: 2e-4 with linear scheduling
• Batch size: 4, Max length: 512 tokens
• Specialized for financial domain conversations

Built for deployment in financial services and fintech environments.