---
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: 0.4826

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4

### Training results

| Training Loss | Epoch  | Step  | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 0.8441        | 0.2930 | 1000  | 0.6059          |
| 0.5806        | 0.5859 | 2000  | 0.5601          |
| 0.5509        | 0.8789 | 3000  | 0.5371          |
| 0.5293        | 1.1718 | 4000  | 0.5231          |
| 0.5187        | 1.4648 | 5000  | 0.5121          |
| 0.5066        | 1.7577 | 6000  | 0.5041          |
| 0.501         | 2.0507 | 7000  | 0.4988          |
| 0.4904        | 2.3436 | 8000  | 0.4938          |
| 0.4889        | 2.6366 | 9000  | 0.4903          |
| 0.4871        | 2.9295 | 10000 | 0.4871          |
| 0.4823        | 3.2225 | 11000 | 0.4852          |
| 0.4759        | 3.5155 | 12000 | 0.4837          |
| 0.4756        | 3.8084 | 13000 | 0.4826          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1