Ephraimmm/customer-service
Ephraimmm/customer-service is a 4.3 billion parameter Gemma-3-4B instruction-tuned model developed by Ephraimmm, specifically fine-tuned for Nigerian bank customer service interactions. It leverages a 32768 token context length and is optimized to reflect Nigerian tone and phrasing patterns, including common Nigerian English expressions and code-switching. This model excels at handling financial services customer support conversations and loan application inquiries within a Nigerian banking context.
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Overview
This model, developed by Ephraimmm, is a 4.3 billion parameter Gemma-3-4B instruction-tuned model specifically fine-tuned for customer service conversations within a Nigerian bank context. It was trained using LoRA fine-tuning via Unsloth and Hugging Face's TRL library, building upon the unsloth/gemma-3-4b-it-unsloth-bnb-4bit base model. The fine-tuning data incorporates Nigerian customer-service interactions, enabling the model to generate responses with a distinct Nigerian tone and phrasing patterns, including common Nigerian English expressions and code-switching.
Key Capabilities
- Domain-Specific Customer Service: Optimized for financial services customer support, particularly for Nigerian banks.
- Culturally Adapted Responses: Generates text reflecting Nigerian tone, phrasing, and common linguistic patterns.
- Loan Application Assistance: Capable of handling inquiries related to loan application status and general assistance.
Intended Use Cases
- Financial Services Customer Support: Ideal for chat-based customer interaction in Nigerian banking scenarios.
- Loan Inquiry Handling: Specifically designed to assist with loan application processes and status checks.
Limitations
- Domain-Specific: Not intended as a general-purpose chat model; performance is optimized for its narrow use case.
- No Formal Benchmarks: Lacks published evaluation metrics, accuracy figures, or benchmark scores.
- Production Review Required: Outputs should be reviewed before deployment in compliance-sensitive financial services environments.