Update README.md

README.md

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----
-license: apache-2.0
----
+---
+base_model: microsoft/DialoGPT-medium
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- conversational-ai
+- finance
+- fintech
+- investment-banking
+- banking
+- lora
+- financial-advisor
+- portfolio-management
+- risk-assessment
+- financial-analysis
+- trading
+- markets
+- wealth-management
+- compliance
+- regulatory
+- client-advisory
+- chatbot
+- nlp
+language:
+- en
+license: mit
+datasets:
+- gbharti/finance-alpaca
+metrics:
+- perplexity
+- bleu
+widget:
+- text: "<|user|> What are the key risks in equity trading? <|bot|>"
+  example_title: "Risk Assessment Query"
+- text: "<|user|> Explain portfolio diversification strategies <|bot|>"  
+  example_title: "Investment Strategy"
+- text: "<|user|> How do interest rates affect bond prices? <|bot|>"
+  example_title: "Market Analysis"
+---
+
+# 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.