---
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: 1.7010

## 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: 7

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.1348        | 0.1684 | 100  | 2.7621          |
| 2.6302        | 0.3367 | 200  | 2.5110          |
| 2.4441        | 0.5051 | 300  | 2.3619          |
| 2.3325        | 0.6734 | 400  | 2.2485          |
| 2.2262        | 0.8418 | 500  | 2.1819          |
| 2.1809        | 1.0101 | 600  | 2.1318          |
| 2.0646        | 1.1785 | 700  | 2.0802          |
| 2.0665        | 1.3468 | 800  | 2.0541          |
| 2.0072        | 1.5152 | 900  | 2.0057          |
| 1.9954        | 1.6835 | 1000 | 1.9762          |
| 1.9577        | 1.8519 | 1100 | 1.9554          |
| 1.9263        | 2.0202 | 1200 | 1.9256          |
| 1.8635        | 2.1886 | 1300 | 1.9027          |
| 1.855         | 2.3569 | 1400 | 1.8871          |
| 1.8258        | 2.5253 | 1500 | 1.8750          |
| 1.8269        | 2.6936 | 1600 | 1.8555          |
| 1.8194        | 2.8620 | 1700 | 1.8415          |
| 1.775         | 3.0303 | 1800 | 1.8257          |
| 1.7379        | 3.1987 | 1900 | 1.8175          |
| 1.7384        | 3.3670 | 2000 | 1.8052          |
| 1.74          | 3.5354 | 2100 | 1.7943          |
| 1.7275        | 3.7037 | 2200 | 1.7778          |
| 1.6903        | 3.8721 | 2300 | 1.7680          |
| 1.6908        | 4.0404 | 2400 | 1.7594          |
| 1.6663        | 4.2088 | 2500 | 1.7559          |
| 1.6312        | 4.3771 | 2600 | 1.7457          |
| 1.6412        | 4.5455 | 2700 | 1.7395          |
| 1.6392        | 4.7138 | 2800 | 1.7327          |
| 1.6237        | 4.8822 | 2900 | 1.7260          |
| 1.6138        | 5.0505 | 3000 | 1.7244          |
| 1.5858        | 5.2189 | 3100 | 1.7205          |
| 1.6005        | 5.3872 | 3200 | 1.7163          |
| 1.5662        | 5.5556 | 3300 | 1.7120          |
| 1.5888        | 5.7239 | 3400 | 1.7075          |
| 1.5802        | 5.8923 | 3500 | 1.7068          |
| 1.5659        | 6.0606 | 3600 | 1.7038          |
| 1.5526        | 6.2290 | 3700 | 1.7039          |
| 1.54          | 6.3973 | 3800 | 1.7024          |
| 1.5653        | 6.5657 | 3900 | 1.7018          |
| 1.545         | 6.7340 | 4000 | 1.7012          |
| 1.5455        | 6.9024 | 4100 | 1.7010          |


### Framework versions

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