---
license: mit
base_model: microsoft/Phi-3-mini-4k-instruct
tags:
- generated_from_trainer
model-index:
- name: Phi0503HMA8
  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. -->

# Phi0503HMA8

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: 0.0686

## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 4.3865        | 0.09  | 10   | 1.2753          |
| 0.5539        | 0.18  | 20   | 0.2766          |
| 0.5947        | 0.27  | 30   | 0.2953          |
| 0.2568        | 0.36  | 40   | 0.2363          |
| 0.2491        | 0.45  | 50   | 0.2147          |
| 0.2056        | 0.54  | 60   | 0.2216          |
| 0.1891        | 0.63  | 70   | 0.1671          |
| 0.1675        | 0.73  | 80   | 0.1412          |
| 0.1048        | 0.82  | 90   | 0.0875          |
| 0.0832        | 0.91  | 100  | 0.0893          |
| 0.1           | 1.0   | 110  | 0.0979          |
| 0.0777        | 1.09  | 120  | 0.0755          |
| 0.0726        | 1.18  | 130  | 0.0886          |
| 0.1565        | 1.27  | 140  | 0.0863          |
| 0.0881        | 1.36  | 150  | 0.0741          |
| 0.0792        | 1.45  | 160  | 0.0784          |
| 0.0742        | 1.54  | 170  | 0.0716          |
| 0.0673        | 1.63  | 180  | 0.0688          |
| 0.0644        | 1.72  | 190  | 0.0674          |
| 0.0687        | 1.81  | 200  | 0.0684          |
| 0.0644        | 1.9   | 210  | 0.0695          |
| 0.0641        | 1.99  | 220  | 0.0694          |
| 0.039         | 2.08  | 230  | 0.0703          |
| 0.0375        | 2.18  | 240  | 0.0849          |
| 0.0345        | 2.27  | 250  | 0.0772          |
| 0.0324        | 2.36  | 260  | 0.0694          |
| 0.0386        | 2.45  | 270  | 0.0736          |
| 0.0336        | 2.54  | 280  | 0.0731          |
| 0.0321        | 2.63  | 290  | 0.0704          |
| 0.0365        | 2.72  | 300  | 0.0705          |
| 0.0394        | 2.81  | 310  | 0.0697          |
| 0.0357        | 2.9   | 320  | 0.0687          |
| 0.0379        | 2.99  | 330  | 0.0686          |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.0