Overview

Model Description

• Base Model: Mistral 8x7B model- Instruct
• Model type: Transformer-based decoder-only language model
• Language(s) (NLP): English

Training Dataset

• Dataset Size: 4.8 million research papers and 10,000 medical books.
• Data Diversity: Includes a wide range of medical fields, ensuring comprehensive coverage of medical knowledge.
• Preprocessing:
• Books: We collected 10,000 textbooks from various sources such as the open-library, university libraries, and reputable publishers, covering a wide range of medical specialties. For preprocessing, we extracted text content from PDF files, then performed data cleaning through de-duplication and content filtering. This involved removing extraneous elements such as URLs, author lists, superfluous information, document contents, references, and citations.
• Papers: Academic papers are a valuable knowledge resource due to their high-quality, cutting-edge medical information. We started with the S2ORC (Lo et al. 2020) dataset, which contains 81.1 million English-language academic papers. From this, we selected biomedical-related papers based on the presence of corresponding PubMed Central (PMC) IDs. This resulted in approximately 4.8 million biomedical papers, totaling over 75 billion tokens.

Model Sources [optional]

• Repository: https://huggingface.co/SNOWTEAM/medico-mistral
• Paper [optional]:
• Demo [optional]:

How to Get Started with the Model

Training Details

Training Hyperparameters

• Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

Evaluation

Testing Data, Factors & Metrics

Testing Data

Factors

Metrics

Results

Summary

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