---
base_model: microsoft/Phi-3-mini-128k-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-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3525

## 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: 1
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.3351        | 0.17  | 500  | 0.3755          |
| 0.3312        | 0.34  | 1000 | 0.3644          |
| 0.3079        | 0.51  | 1500 | 0.3597          |
| 0.3195        | 0.68  | 2000 | 0.3577          |
| 0.3218        | 0.85  | 2500 | 0.3557          |
| 0.3034        | 1.02  | 3000 | 0.3553          |
| 0.296         | 1.19  | 3500 | 0.3543          |
| 0.3175        | 1.36  | 4000 | 0.3539          |
| 0.3257        | 1.53  | 4500 | 0.3533          |
| 0.3263        | 1.7   | 5000 | 0.3526          |
| 0.3209        | 1.87  | 5500 | 0.3522          |
| 0.3221        | 2.04  | 6000 | 0.3528          |
| 0.2927        | 2.21  | 6500 | 0.3526          |
| 0.2922        | 2.38  | 7000 | 0.3527          |
| 0.2968        | 2.55  | 7500 | 0.3525          |
| 0.2968        | 2.72  | 8000 | 0.3526          |
| 0.3094        | 2.89  | 8500 | 0.3525          |


### Framework versions

- PEFT 0.8.2
- Transformers 4.38.0
- Pytorch 2.2.1
- Datasets 2.17.0
- Tokenizers 0.15.2