Himel09/llama-customer-support-ai-lora

🏦 Banking Chatbot – LoRA Fine-Tuned Model

This model is a LoRA fine-tuned version of a Llama-based model, designed to detect user intent for banking tasks and generate agent-style responses.
It is optimized for tasks such as:

• ✔ Checking balance
• ✔ Last transaction lookup
• ✔ Transaction history
• ✔ Loan eligibility queries
• ✔ Banking account actions
• ✔ Customer service style replies

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🔧 Model Details

• Base Model: Llama-3 / Unsloth version (4-bit quantized during training)
• Fine-Tuning Method: LoRA (Parameter-Efficient Training)
• Frameworks Used:
  • unsloth
  • transformers
  • trl (SFTTrainer)
• Dataset: Custom banking conversational dataset with structured responses
• Purpose: Intent detection + Agent response generation

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📘 Training Format

The model is trained on structured dialogue patterns such as:

Each record includes:

• User query
• Intent label
• Agent response

This format ensures stable predictable output for banking applications.

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