LoRA Fine-tuning for Domain Expertise Challenge

Global AI models lack understanding of African legal systems, medical conditions, engineering standards, and agricultural practices, making them unreliable for professional use. A lawyer in Rwanda cannot trust AI for local law, a doctor in Ethiopia gets incorrect diagnostic suggestions, and engineers receive recommendations violating local building codes. Organizations need specialized models but lack the resources for traditional fine-tuning. Developers must demonstrate LoRA fine-tuning on consumer hardware to create domain-expert models for law, medicine, engineering, or agriculture, showing how any organization can build specialized AI without expensive infrastructure.

The LoRA Fine-tuning for Domain Expertise Challenge invites developers to demonstrate how Low-Rank Adaptation can create specialized AI models for African professional domains using only consumer-grade hardware. This challenge addresses the need for domain-specific AI that understands local contexts, regulations, and practices.

Participants will fine-tune small language models using LoRA techniques for specific domains such as law (understanding local legal systems), medicine (recognizing regional health conditions), engineering (local construction standards), or agriculture (indigenous farming practices). The fine-tuning process must be achievable on consumer GPUs or even CPUs, making it accessible to organizations without expensive infrastructure.

Successful solutions will demonstrate significant performance improvements in domain-specific tasks, maintain general capabilities while adding specialization, document the fine-tuning process for replication, and provide benchmarks showing expertise acquisition. The system should include data preparation pipelines, training scripts, and evaluation metrics specific to the chosen domain.

We particularly value solutions that address uniquely African domain knowledge often missing from global models, create models for local languages and dialects, demonstrate cost-effective fine-tuning workflows, and provide clear guides for non-ML experts to replicate the process. The project should democratize AI customization, enabling organizations to create their own specialized models.