---
base_model: microsoft/phi-1_5
library_name: peft
license: mit
tags:
- generated_from_trainer
model-index:
- name: Phi-Medical-QA-LoRA
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Phi-Medical-QA-LoRA

This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9509

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.1312        | 0.1684 | 100  | 2.7874          |
| 2.6198        | 0.3367 | 200  | 2.5280          |
| 2.4484        | 0.5051 | 300  | 2.3807          |
| 2.3069        | 0.6734 | 400  | 2.2949          |
| 2.2195        | 0.8418 | 500  | 2.2151          |
| 2.2088        | 1.0101 | 600  | 2.1664          |
| 2.0887        | 1.1785 | 700  | 2.1273          |
| 2.0318        | 1.3468 | 800  | 2.0870          |
| 2.0239        | 1.5152 | 900  | 2.0519          |
| 1.9709        | 1.6835 | 1000 | 2.0285          |
| 1.9697        | 1.8519 | 1100 | 2.0116          |
| 1.9416        | 2.0202 | 1200 | 1.9881          |
| 1.8815        | 2.1886 | 1300 | 1.9745          |
| 1.8816        | 2.3569 | 1400 | 1.9661          |
| 1.8641        | 2.5253 | 1500 | 1.9577          |
| 1.843         | 2.6936 | 1600 | 1.9526          |
| 1.8638        | 2.8620 | 1700 | 1.9509          |


### Framework versions

- PEFT 0.11.1
- Transformers 4.42.4
- Pytorch 1.13.1+cu117
- Datasets 2.19.2
- Tokenizers 0.19.1