---
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: 0.8384

## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 1.4537        | 0.2052 | 100  | 1.2281          |
| 1.0408        | 0.4105 | 200  | 0.9405          |
| 0.915         | 0.6157 | 300  | 0.9088          |
| 0.9159        | 0.8209 | 400  | 0.8933          |
| 0.8925        | 1.0262 | 500  | 0.8809          |
| 0.8837        | 1.2314 | 600  | 0.8712          |
| 0.8753        | 1.4366 | 700  | 0.8604          |
| 0.8701        | 1.6419 | 800  | 0.8537          |
| 0.8755        | 1.8471 | 900  | 0.8498          |
| 0.8603        | 2.0523 | 1000 | 0.8460          |
| 0.8669        | 2.2576 | 1100 | 0.8434          |
| 0.8558        | 2.4628 | 1200 | 0.8410          |
| 0.8482        | 2.6680 | 1300 | 0.8395          |
| 0.844         | 2.8733 | 1400 | 0.8384          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1