---
base_model: microsoft/Phi-3-mini-4k-instruct
library_name: peft
license: mit
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: phi-3-mini-LoRA-MEDQA-Extended-V3
  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-3-mini-LoRA-MEDQA-Extended-V3

This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6233

## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.7825        | 0.0882 | 200  | 0.6760          |
| 0.6593        | 0.1764 | 400  | 0.6488          |
| 0.6454        | 0.2646 | 600  | 0.6424          |
| 0.6424        | 0.3528 | 800  | 0.6382          |
| 0.6382        | 0.4410 | 1000 | 0.6358          |
| 0.6342        | 0.5292 | 1200 | 0.6340          |
| 0.6355        | 0.6174 | 1400 | 0.6327          |
| 0.6355        | 0.7055 | 1600 | 0.6315          |
| 0.6336        | 0.7937 | 1800 | 0.6307          |
| 0.6321        | 0.8819 | 2000 | 0.6298          |
| 0.6321        | 0.9701 | 2200 | 0.6291          |
| 0.6298        | 1.0583 | 2400 | 0.6286          |
| 0.6285        | 1.1465 | 2600 | 0.6280          |
| 0.628         | 1.2347 | 2800 | 0.6275          |
| 0.6282        | 1.3229 | 3000 | 0.6271          |
| 0.6278        | 1.4111 | 3200 | 0.6267          |
| 0.6257        | 1.4993 | 3400 | 0.6264          |
| 0.6276        | 1.5875 | 3600 | 0.6260          |
| 0.6253        | 1.6757 | 3800 | 0.6256          |
| 0.6253        | 1.7639 | 4000 | 0.6253          |
| 0.6242        | 1.8521 | 4200 | 0.6250          |
| 0.6252        | 1.9402 | 4400 | 0.6247          |
| 0.6239        | 2.0284 | 4600 | 0.6246          |
| 0.6222        | 2.1166 | 4800 | 0.6244          |
| 0.6226        | 2.2048 | 5000 | 0.6242          |
| 0.6219        | 2.2930 | 5200 | 0.6241          |
| 0.6227        | 2.3812 | 5400 | 0.6240          |
| 0.6195        | 2.4694 | 5600 | 0.6239          |
| 0.6219        | 2.5576 | 5800 | 0.6237          |
| 0.6221        | 2.6458 | 6000 | 0.6236          |
| 0.6238        | 2.7340 | 6200 | 0.6235          |
| 0.621         | 2.8222 | 6400 | 0.6234          |
| 0.621         | 2.9104 | 6600 | 0.6234          |
| 0.6222        | 2.9986 | 6800 | 0.6233          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.43.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1