phi-med-v1

Model Card for Phi-Med-V1

<!-- Provide a quick summary of what the model is/does. -->

Microsoft Phi2 Finetuned on Medical Text Data

Model Details

Model Description

<!-- Provide a longer summary of what this model is. -->

• Developed by: [JJ]
• Model type: [SLM]
• Finetuned from model: [microsoft/Phi-2]

Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

Testing the effectivness of Finetuning SLMs

Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

Not Allowed as this is for research only

Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

Model can still Halucinate.

Training Details

Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

MedText Dataset from HuggingFace

Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

SFT using HF Transformers

Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

• Hardware Type: A10 GPU VMs [2x24GB A10]

• Hours used: [3]

• Cloud Provider: [Azure]

• Compute Region: [North Europe (Dublin)]

• Experiments were conducted using Azure in region northeurope, which has a carbon efficiency of 0.62 kgCO$_2$eq/kWh. A cumulative of 100 hours of computation was performed on hardware of type A10 (TDP of 350W).

• Total emissions are estimated to be 21.7 kgCO$_2$eq of which 100 percents were directly offset by the cloud provider.

• Estimations were conducted using the [https://mlco2.github.io/impact#compute][MachineLearning Impact calculator]

Technical Specifications [optional]

Compute Infrastructure

[Azure]

Hardware

[NV72ads A10 GPU VMs]

Software

[Axolotl]

Model Card Authors [optional]

[JJ]

Model Card Contact

[JJ]