Hugging Face model: drmasad/HAH-2024-v0.11
Author: drmasad
Created: 2024-04-26 07:20:45
Last modified: 2024-05-13 22:56:18
Pipeline: text-generation
Library: transformers
Likes: 1 | Downloads: 0
Tags: transformers, safetensors, mistral, text-generation, healthcare, diabetes, conversational, en, license:apache-2.0, model-index, text-generation-inference, endpoints_compatible, region:us
Card metadata:
language: en
license: apache-2.0
pipeline_tag: text-generation
tags: healthcare, diabetes
model-description: short-description: HAH 2024 v0.1 is a state-of-the-art language model fine-tuned specifically for generating text based on diabetes-related content. Leveraging a dataset constructed from 3000 open-source review articles, this model provides informative and contextually relevant answers to various queries about diabetes care, research, and therapies.
intended-use: primary-use: HAH 2024 v0.1 is intended to for research purposes only.
secondary-potential-uses: a Prototype for researchers to assess (not to formally use in real life cases) generating educational content for patients and the general public about diabetes care and management., Check the use of adapters to assist researchers in summarizing large volumes of diabetes-related literature.
limitations: While HAH 2024 v0.1 excels at generating contextually appropriate responses, it may occasionally produce outputs that require further verification., The training dataset, being limited to published articles, might not capture all contemporary research or emerging trends in diabetes care.
training-data: description: The training data for HAH 2024 v0.1 consists of 3000 open-source review articles about diabetes, carefully curated to cover a wide range of topics within the field. The dataset was enriched with questions generated through prompting OpenAI GPT-4 to ensure diversity in content and perspectives.
training-procedure: description: HAH 2024 v0.1 was fine-tuned on an A100 GPU using Google Colab. The fine-tuning process was carefully monitored to maintain the model's relevance to diabetes-related content while minimizing biases that might arise from the dataset's specific nature.