# Model Card for Phi-Med-V1

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Microsoft Phi2 Finetuned on Medical Text Data

## Model Details

### Model Description

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


## Uses

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Testing the effectivness of Finetuning SLMs 

### Direct Use

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Not Allowed as this is for research only




## Bias, Risks, and Limitations

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Model can still Halucinate.


## Training Details

### Training Data

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MedText Dataset from HuggingFace

### Training Procedure 

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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]



{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["medical"], "datasets": ["BI55/MedText"], "pipeline_tag": "text-generation"}
transformers safetensors phi-msft text-generation medical custom_code en dataset:BI55/MedText license:apache-2.0 autotrain_compatible endpoints_compatible region:us