oMarquess/nahara-dataset-model

Nahara Dataset Model

• —Developed by: Redeemer Salami Okekale, BMS
• —License: apache-2.0
• —Finetuned from model: unsloth/meta-llama-3.1-8b-bnb-4bit
• —Training Loss: 1.181600

Model Description: The nahara-dataset-model is a fine-tuned version of Meta's LLaMA series, specifically optimized for low-precision (4-bit) operations to enhance efficiency in both memory usage and computational resources. It was fine-tuned on the Nahara dataset and achieved a training loss of 1.181600, ensuring strong performance on medical data.

• —Model Type: Transformer-based Language Model
• —Size: 8 billion parameters
• —Precision: 4-bit quantization using bnb (bits and bytes), improving memory efficiency and making the model suitable for resource-constrained environments.

Intended Use: This model serves as a highly adaptable AI copilot for medical professionals, ideal for providing real-time recommendations and decision support. It can assist with:

• —Medical diagnostics and treatment suggestions
• —Summarization of clinical data
• —Generation of medical reports and documentation
• —Assistance with medical coding and research data preparation

Performance:

• —Training Loss: 1.181600
• —Fine-tuning Data: Medical and clinical datasets enhanced through data augmentation techniques to handle sparsity and variability, making it applicable across various healthcare contexts.

Applications: The nahara-dataset-model is suited for:

• —Clinical decision support systems
• —AI copilots for medical professionals
• —Research data analysis and augmentation
• —Medical record summarization and automated report generation

Limitations and Considerations:

• —The model is trained on medical data but may not encompass all clinical expertise nuances. It should be used to augment decision-making, not replace professional judgment.
• —Ethical considerations, including data privacy and bias in healthcare applications, must be strictly followed.
• —While efficiency is boosted by quantizing to 4-bit, there may be trade-offs in performance for complex tasks compared to higher precision models.

Future Improvements: The model will undergo further optimization and refinement in Phase 2, including expanding the dataset, improving real-world adaptability, and fine-tuning the AI copilot for specific medical specializations.

Contributors:

• —Emmanuel Akomanin Asiamah, PhD
• —Elli Banini
• —Felix Coker
• —Philip Attram, BMS
• —Schandorf Osam-Frimpong, MD
• —Daniel Mawuenyega Gohoho
• —Vitus Amenorpe
• —Aaron Kofi Gayi
• —Julius Richard Ogbey
• —Cherryln Asiwome Ahiable
• —Ama Quashie
• —Andrew Kojo Mensah-Onumah
• —Edith Zikpi
• —Azumah Benson, MD