---
license: mit
base_model: microsoft/Phi-3-mini-4k-instruct
tags:
- generated_from_trainer
model-index:
- name: Phi0503HMA2
  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. -->

# Phi0503HMA2

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: 0.1630

## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 80
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.4067        | 0.09  | 10   | 0.9597          |
| 0.5121        | 0.18  | 20   | 0.4807          |
| 0.3541        | 0.27  | 30   | 0.2436          |
| 0.2345        | 0.36  | 40   | 0.2271          |
| 0.2398        | 0.45  | 50   | 0.2915          |
| 0.2538        | 0.54  | 60   | 0.2847          |
| 0.216         | 0.63  | 70   | 0.2622          |
| 0.247         | 0.73  | 80   | 0.2132          |
| 0.2135        | 0.82  | 90   | 0.2269          |
| 0.2383        | 0.91  | 100  | 0.2018          |
| 0.1876        | 1.0   | 110  | 0.1702          |
| 0.1708        | 1.09  | 120  | 0.1679          |
| 0.1662        | 1.18  | 130  | 0.1660          |
| 0.1802        | 1.27  | 140  | 0.1703          |
| 0.1759        | 1.36  | 150  | 0.1664          |
| 0.1622        | 1.45  | 160  | 0.1666          |
| 0.1654        | 1.54  | 170  | 0.1636          |
| 0.1648        | 1.63  | 180  | 0.1627          |
| 0.1656        | 1.72  | 190  | 0.1691          |
| 0.1667        | 1.81  | 200  | 0.1640          |
| 0.166         | 1.9   | 210  | 0.1633          |
| 0.1628        | 1.99  | 220  | 0.1643          |
| 0.1638        | 2.08  | 230  | 0.1628          |
| 0.1604        | 2.18  | 240  | 0.1625          |
| 0.1599        | 2.27  | 250  | 0.1631          |
| 0.163         | 2.36  | 260  | 0.1638          |
| 0.1611        | 2.45  | 270  | 0.1634          |
| 0.1615        | 2.54  | 280  | 0.1635          |
| 0.1616        | 2.63  | 290  | 0.1637          |
| 0.1625        | 2.72  | 300  | 0.1633          |
| 0.1626        | 2.81  | 310  | 0.1631          |
| 0.1619        | 2.9   | 320  | 0.1630          |
| 0.1659        | 2.99  | 330  | 0.1630          |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1