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---
license: apache-2.0
datasets:
- BI55/MedText
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- medical
---
# 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]
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