---
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-LoRA
  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-LoRA

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: 1.7121

## 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: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.94          | 0.0953 | 100  | 1.8918          |
| 1.8113        | 0.1907 | 200  | 1.7877          |
| 1.7578        | 0.2860 | 300  | 1.7701          |
| 1.756         | 0.3813 | 400  | 1.7637          |
| 1.7632        | 0.4766 | 500  | 1.7582          |
| 1.7477        | 0.5720 | 600  | 1.7542          |
| 1.7605        | 0.6673 | 700  | 1.7508          |
| 1.7312        | 0.7626 | 800  | 1.7482          |
| 1.7315        | 0.8580 | 900  | 1.7439          |
| 1.7148        | 0.9533 | 1000 | 1.7414          |
| 1.7263        | 1.0486 | 1100 | 1.7385          |
| 1.7184        | 1.1439 | 1200 | 1.7361          |
| 1.7187        | 1.2393 | 1300 | 1.7336          |
| 1.7231        | 1.3346 | 1400 | 1.7313          |
| 1.7433        | 1.4299 | 1500 | 1.7290          |
| 1.6962        | 1.5253 | 1600 | 1.7268          |
| 1.7136        | 1.6206 | 1700 | 1.7253          |
| 1.6969        | 1.7159 | 1800 | 1.7236          |
| 1.7028        | 1.8112 | 1900 | 1.7217          |
| 1.7066        | 1.9066 | 2000 | 1.7200          |
| 1.7123        | 2.0019 | 2100 | 1.7191          |
| 1.7005        | 2.0972 | 2200 | 1.7178          |
| 1.7052        | 2.1926 | 2300 | 1.7168          |
| 1.6946        | 2.2879 | 2400 | 1.7160          |
| 1.6728        | 2.3832 | 2500 | 1.7150          |
| 1.7033        | 2.4786 | 2600 | 1.7144          |
| 1.6893        | 2.5739 | 2700 | 1.7136          |
| 1.7206        | 2.6692 | 2800 | 1.7129          |
| 1.6747        | 2.7645 | 2900 | 1.7126          |
| 1.6981        | 2.8599 | 3000 | 1.7123          |
| 1.6928        | 2.9552 | 3100 | 1.7121          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.43.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1