---
license: mit
base_model: microsoft/Phi-3-mini-4k-instruct
tags:
- generated_from_trainer
model-index:
- name: PHI30511HMA11H
  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. -->

# PHI30511HMA11H

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: 0.0815

## 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: 100
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.2379        | 0.09  | 10   | 0.5427          |
| 0.2757        | 0.18  | 20   | 0.1660          |
| 0.184         | 0.27  | 30   | 0.1553          |
| 0.1398        | 0.36  | 40   | 0.1268          |
| 0.1257        | 0.45  | 50   | 0.1158          |
| 0.1148        | 0.54  | 60   | 0.0949          |
| 0.0892        | 0.63  | 70   | 0.0841          |
| 0.0765        | 0.73  | 80   | 0.0660          |
| 0.0623        | 0.82  | 90   | 0.0698          |
| 0.0647        | 0.91  | 100  | 0.0660          |
| 0.0677        | 1.0   | 110  | 0.0672          |
| 0.0412        | 1.09  | 120  | 0.0798          |
| 0.0487        | 1.18  | 130  | 0.0708          |
| 0.0557        | 1.27  | 140  | 0.0685          |
| 0.0492        | 1.36  | 150  | 0.0652          |
| 0.05          | 1.45  | 160  | 0.0649          |
| 0.0484        | 1.54  | 170  | 0.0729          |
| 0.0468        | 1.63  | 180  | 0.0687          |
| 0.0433        | 1.72  | 190  | 0.0675          |
| 0.0484        | 1.81  | 200  | 0.0632          |
| 0.0433        | 1.9   | 210  | 0.0675          |
| 0.0452        | 1.99  | 220  | 0.0638          |
| 0.0216        | 2.08  | 230  | 0.0726          |
| 0.0164        | 2.18  | 240  | 0.0921          |
| 0.0159        | 2.27  | 250  | 0.0935          |
| 0.0122        | 2.36  | 260  | 0.0880          |
| 0.0215        | 2.45  | 270  | 0.0807          |
| 0.0134        | 2.54  | 280  | 0.0787          |
| 0.0115        | 2.63  | 290  | 0.0803          |
| 0.0171        | 2.72  | 300  | 0.0814          |
| 0.017         | 2.81  | 310  | 0.0815          |
| 0.0134        | 2.9   | 320  | 0.0814          |
| 0.0124        | 2.99  | 330  | 0.0815          |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.14.1