transformers safetensors text-generation-inference unsloth llama trl question-answering en dataset:oMarquess/nahara-dataset-2010n license:apache-2.0 endpoints_compatible region:us
---
base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
datasets:
- oMarquess/nahara-dataset-2010n
language:
- en
license: apache-2.0
pipeline_tag: question-answering
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---

# 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
