---
license: mit
base_model: microsoft/Phi-3-mini-4k-instruct
tags:
- generated_from_trainer
model-index:
- name: Phi0503HMA17
  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. -->

# Phi0503HMA17

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: 0.0613

## 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: 60
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.8235        | 0.09  | 10   | 0.4394          |
| 0.3037        | 0.18  | 20   | 0.2360          |
| 0.2646        | 0.27  | 30   | 0.2336          |
| 0.2183        | 0.36  | 40   | 0.1923          |
| 0.164         | 0.45  | 50   | 0.2113          |
| 0.2323        | 0.54  | 60   | 0.1157          |
| 0.0954        | 0.63  | 70   | 0.0939          |
| 0.0792        | 0.73  | 80   | 0.0938          |
| 0.0914        | 0.82  | 90   | 0.0814          |
| 0.0757        | 0.91  | 100  | 0.0724          |
| 0.0795        | 1.0   | 110  | 0.0717          |
| 0.0546        | 1.09  | 120  | 0.0677          |
| 0.0535        | 1.18  | 130  | 0.0718          |
| 0.0617        | 1.27  | 140  | 0.0718          |
| 0.0561        | 1.36  | 150  | 0.0765          |
| 0.0632        | 1.45  | 160  | 0.0595          |
| 0.0549        | 1.54  | 170  | 0.0612          |
| 0.0404        | 1.63  | 180  | 0.0521          |
| 0.0353        | 1.72  | 190  | 0.0431          |
| 0.0396        | 1.81  | 200  | 0.0489          |
| 0.0272        | 1.9   | 210  | 0.0543          |
| 0.032         | 1.99  | 220  | 0.0489          |
| 0.0132        | 2.08  | 230  | 0.0512          |
| 0.0101        | 2.18  | 240  | 0.0641          |
| 0.0089        | 2.27  | 250  | 0.0688          |
| 0.0095        | 2.36  | 260  | 0.0623          |
| 0.0072        | 2.45  | 270  | 0.0620          |
| 0.0086        | 2.54  | 280  | 0.0628          |
| 0.0073        | 2.63  | 290  | 0.0624          |
| 0.0068        | 2.72  | 300  | 0.0619          |
| 0.0098        | 2.81  | 310  | 0.0621          |
| 0.0088        | 2.9   | 320  | 0.0618          |
| 0.0074        | 2.99  | 330  | 0.0613          |


### Framework versions

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