DialoGPT-Financial-Wealth-Management-Advisor

Fine-tuned DialoGPT-small for financial advisory conversations, wealth management guidance, and comprehensive investment consultation services.

Overview

• Base Model: microsoft/DialoGPT-small (117M parameters)
• Fine-tuning Method: LoRA (4-bit quantization)
• Dataset: Financial Q&A dataset (1K expert-level samples)
• Training: 3 epochs with optimized hyperparameters

Key Features

• Comprehensive financial advisory consultations
• Investment portfolio analysis and recommendations
• Risk assessment and management strategies
• Tax planning and wealth optimization advice
• Retirement and financial planning guidance
• Client-focused conversational interface

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("sweatSmile/DialoGPT-Financial-Wealth-Management-Advisor")
tokenizer = AutoTokenizer.from_pretrained("sweatSmile/DialoGPT-Financial-Wealth-Management-Advisor")

# Financial advisory consultation example
prompt = "<|user|> As my financial advisor, please help me understand: How do foreign currency fluctuations affect my international investments? <|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

• Wealth management client consultations
• Investment advisory services automation
• Financial planning and retirement guidance
• Private banking client support
• Robo-advisor conversation engines
• Financial education and client onboarding

Training Details

• LoRA rank: 8, alpha: 16
• 4-bit NF4 quantization with fp16 precision
• Learning rate: 2e-4 with linear scheduling
• Batch size: 8, Max length: 320 tokens
• 3 epochs on curated financial advisory dataset

Optimized for sophisticated wealth management and investment advisory conversations in professional financial services environments.