---
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-QLoRA
  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-QLoRA

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: 0.5761

## 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: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.1336        | 0.1809 | 100  | 0.6788          |
| 0.6283        | 0.3618 | 200  | 0.6030          |
| 0.5944        | 0.5427 | 300  | 0.5931          |
| 0.5953        | 0.7237 | 400  | 0.5879          |
| 0.5793        | 0.9046 | 500  | 0.5852          |
| 0.5908        | 1.0855 | 600  | 0.5832          |
| 0.5717        | 1.2664 | 700  | 0.5812          |
| 0.5748        | 1.4473 | 800  | 0.5802          |
| 0.5876        | 1.6282 | 900  | 0.5787          |
| 0.5725        | 1.8091 | 1000 | 0.5778          |
| 0.5749        | 1.9900 | 1100 | 0.5772          |
| 0.5646        | 2.1710 | 1200 | 0.5769          |
| 0.5806        | 2.3519 | 1300 | 0.5764          |
| 0.5679        | 2.5328 | 1400 | 0.5762          |
| 0.5683        | 2.7137 | 1500 | 0.5761          |
| 0.5715        | 2.8946 | 1600 | 0.5761          |


### Framework versions

- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.1.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1