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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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Model type: Medical Q&A
Language(s) (NLP): English
License: Apache License 2.0
Finetuned from model [optional]: LLaMA 2 7B

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Uses

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure

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Training Hyperparameters

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Evaluation

Testing Data, Factors & Metrics

Testing Data

This model is fine tuned on this dataset: https://huggingface.co/datasets/natsume-shokogami/medqa, which is based on https://huggingface.co/datasets/openlifescienceai/medmcqa

Factors

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Metrics

Metric used: BLEU, ROUGE

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Results

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Summary

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: NVIDIA Tesla P100 (Kaggle's)
Hours used: 15 (though only 2 hours for actual trianing, the rest is debugging)
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Carbon Emitted: 2.1 kg CO2 approximated https://mlco2.github.io/impact/#publish

Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

Kaggle's hardware

Software

Base model: LLaMA 2 7B Dataset: openlifescienceai/medmcqa Fine-tuning: QLoRA

Citation [optional]

https://www.datacamp.com/tutorial/fine-tuning-llama-2

BibTeX:

@InProceedings{pmlr-v174-pal22a, title = {MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering}, author = {Pal, Ankit and Umapathi, Logesh Kumar and Sankarasubbu, Malaikannan}, booktitle = {Proceedings of the Conference on Health, Inference, and Learning}, pages = {248--260}, year = {2022}, editor = {Flores, Gerardo and Chen, George H and Pollard, Tom and Ho, Joyce C and Naumann, Tristan}, volume = {174}, series = {Proceedings of Machine Learning Research}, month = {07--08 Apr}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v174/pal22a/pal22a.pdf}, url = {https://proceedings.mlr.press/v174/pal22a.html}, abstract = {This paper introduces MedMCQA, a new large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions. More than 194k high-quality AIIMS & NEET PG entrance exam MCQs covering 2.4k healthcare topics and 21 medical subjects are collected with an average token length of 12.77 and high topical diversity. Each sample contains a question, correct answer(s), and other options which requires a deeper language understanding as it tests the 10+ reasoning abilities of a model across a wide range of medical subjects & topics. A detailed explanation of the solution, along with the above information, is provided in this study.} }

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