DialoGPT-Quantitative-Risk-Analysis-Expert

Fine-tuned DialoGPT-medium for advanced quantitative risk analysis, financial risk modeling, and comprehensive risk management consultations.

Overview

• Base Model: microsoft/DialoGPT-medium (355M parameters)
• Fine-tuning Method: LoRA (4-bit quantization)
• Dataset: Financial risk analysis dataset (800 expert-level samples)
• Training: 3 epochs with optimized hyperparameters

Key Features

• Advanced quantitative risk modeling and analysis
• Value at Risk (VaR) and Expected Shortfall calculations
• Portfolio risk assessment and optimization
• Market risk, credit risk, and operational risk evaluation
• Volatility modeling and stress testing scenarios
• Risk metric interpretation and regulatory compliance

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("sweatSmile/DialoGPT-Quantitative-Risk-Analysis-Expert")
tokenizer = AutoTokenizer.from_pretrained("sweatSmile/DialoGPT-Quantitative-Risk-Analysis-Expert")

# Quantitative risk analysis example
prompt = "<|user|> As a quantitative risk analyst, please analyze: How do we calculate risk-adjusted returns for a multi-asset portfolio under different market scenarios? <|bot|>"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=250, pad_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Applications

• Risk management department consultations
• Hedge fund risk assessment and monitoring
• Investment bank risk modeling and analysis
• Portfolio risk optimization and stress testing
• Regulatory compliance and risk reporting
• Quantitative research and model validation

Training Details

• LoRA rank: 8, alpha: 16
• 4-bit NF4 quantization with bfloat16 precision
• Learning rate: 1e-4 with cosine scheduling
• Batch size: 8, Max length: 400 tokens
• 3 epochs on curated financial risk analysis dataset

Specialized for sophisticated quantitative risk analysis and modeling in institutional finance environments.