---
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-QLoRA
  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-QLoRA

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: 1.0514

## 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: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- 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: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.2314        | 0.0273 | 100  | 2.1230          |
| 1.5709        | 0.0546 | 200  | 1.5079          |
| 1.4201        | 0.0820 | 300  | 1.4112          |
| 1.3689        | 0.1093 | 400  | 1.3759          |
| 1.3444        | 0.1366 | 500  | 1.3509          |
| 1.3287        | 0.1639 | 600  | 1.3273          |
| 1.3019        | 0.1912 | 700  | 1.3038          |
| 1.2713        | 0.2185 | 800  | 1.2827          |
| 1.2596        | 0.2459 | 900  | 1.2630          |
| 1.2433        | 0.2732 | 1000 | 1.2459          |
| 1.2233        | 0.3005 | 1100 | 1.2310          |
| 1.2197        | 0.3278 | 1200 | 1.2177          |
| 1.2033        | 0.3551 | 1300 | 1.2055          |
| 1.1902        | 0.3825 | 1400 | 1.1963          |
| 1.1927        | 0.4098 | 1500 | 1.1876          |
| 1.1771        | 0.4371 | 1600 | 1.1791          |
| 1.1615        | 0.4644 | 1700 | 1.1702          |
| 1.1655        | 0.4917 | 1800 | 1.1641          |
| 1.1725        | 0.5191 | 1900 | 1.1585          |
| 1.1348        | 0.5464 | 2000 | 1.1522          |
| 1.1429        | 0.5737 | 2100 | 1.1464          |
| 1.141         | 0.6010 | 2200 | 1.1413          |
| 1.1458        | 0.6283 | 2300 | 1.1362          |
| 1.1268        | 0.6556 | 2400 | 1.1314          |
| 1.1218        | 0.6830 | 2500 | 1.1272          |
| 1.1277        | 0.7103 | 2600 | 1.1226          |
| 1.1092        | 0.7376 | 2700 | 1.1198          |
| 1.1282        | 0.7649 | 2800 | 1.1156          |
| 1.1027        | 0.7922 | 2900 | 1.1116          |
| 1.0951        | 0.8196 | 3000 | 1.1084          |
| 1.1001        | 0.8469 | 3100 | 1.1057          |
| 1.1027        | 0.8742 | 3200 | 1.1021          |
| 1.0989        | 0.9015 | 3300 | 1.0987          |
| 1.0917        | 0.9288 | 3400 | 1.0966          |
| 1.0832        | 0.9562 | 3500 | 1.0939          |
| 1.1074        | 0.9835 | 3600 | 1.0915          |
| 1.0692        | 1.0108 | 3700 | 1.0891          |
| 1.0868        | 1.0381 | 3800 | 1.0872          |
| 1.079         | 1.0654 | 3900 | 1.0855          |
| 1.0844        | 1.0927 | 4000 | 1.0831          |
| 1.0779        | 1.1201 | 4100 | 1.0819          |
| 1.0737        | 1.1474 | 4200 | 1.0797          |
| 1.0651        | 1.1747 | 4300 | 1.0775          |
| 1.0656        | 1.2020 | 4400 | 1.0764          |
| 1.0592        | 1.2293 | 4500 | 1.0739          |
| 1.07          | 1.2567 | 4600 | 1.0729          |
| 1.068         | 1.2840 | 4700 | 1.0719          |
| 1.0623        | 1.3113 | 4800 | 1.0701          |
| 1.0622        | 1.3386 | 4900 | 1.0691          |
| 1.0579        | 1.3659 | 5000 | 1.0678          |
| 1.0652        | 1.3933 | 5100 | 1.0667          |
| 1.0655        | 1.4206 | 5200 | 1.0654          |
| 1.0619        | 1.4479 | 5300 | 1.0642          |
| 1.0521        | 1.4752 | 5400 | 1.0635          |
| 1.0563        | 1.5025 | 5500 | 1.0625          |
| 1.0554        | 1.5298 | 5600 | 1.0611          |
| 1.0577        | 1.5572 | 5700 | 1.0599          |
| 1.0427        | 1.5845 | 5800 | 1.0590          |
| 1.0489        | 1.6118 | 5900 | 1.0583          |
| 1.0444        | 1.6391 | 6000 | 1.0578          |
| 1.0573        | 1.6664 | 6100 | 1.0562          |
| 1.0494        | 1.6938 | 6200 | 1.0555          |
| 1.0355        | 1.7211 | 6300 | 1.0551          |
| 1.0531        | 1.7484 | 6400 | 1.0544          |
| 1.0542        | 1.7757 | 6500 | 1.0540          |
| 1.0324        | 1.8030 | 6600 | 1.0535          |
| 1.0497        | 1.8304 | 6700 | 1.0532          |
| 1.0415        | 1.8577 | 6800 | 1.0529          |
| 1.0414        | 1.8850 | 6900 | 1.0522          |
| 1.0588        | 1.9123 | 7000 | 1.0520          |
| 1.0347        | 1.9396 | 7100 | 1.0519          |
| 1.0346        | 1.9669 | 7200 | 1.0516          |
| 1.043         | 1.9943 | 7300 | 1.0514          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.44.0
- Pytorch 2.0.1+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1