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sweatSmile/
DialoGPT-FinTech-Investment-Banking-Assistant

Name: DialoGPT-FinTech-Investment-Banking-Assistant
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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

python

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.