---
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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/esawtooth-rohit-jain/learning/runs/9hs3unns)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/esawtooth-rohit-jain/learning/runs/3vivu1t4)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/esawtooth-rohit-jain/learning/runs/qnf9h94c)
# 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: 0.5586

## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- 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: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.7527        | 0.1131 | 250  | 0.6002          |
| 0.5924        | 0.2262 | 500  | 0.5809          |
| 0.5811        | 0.3393 | 750  | 0.5759          |
| 0.5827        | 0.4524 | 1000 | 0.5717          |
| 0.5767        | 0.5655 | 1250 | 0.5704          |
| 0.5711        | 0.6787 | 1500 | 0.5678          |
| 0.5691        | 0.7918 | 1750 | 0.5672          |
| 0.5635        | 0.9049 | 2000 | 0.5654          |
| 0.5712        | 1.0180 | 2250 | 0.5650          |
| 0.5611        | 1.1311 | 2500 | 0.5647          |
| 0.555         | 1.2442 | 2750 | 0.5631          |
| 0.5505        | 1.3573 | 3000 | 0.5628          |
| 0.5657        | 1.4704 | 3250 | 0.5624          |
| 0.563         | 1.5835 | 3500 | 0.5617          |
| 0.5577        | 1.6966 | 3750 | 0.5614          |
| 0.5578        | 1.8098 | 4000 | 0.5603          |
| 0.5552        | 1.9229 | 4250 | 0.5604          |
| 0.5514        | 2.0360 | 4500 | 0.5600          |
| 0.5473        | 2.1491 | 4750 | 0.5603          |
| 0.5573        | 2.2622 | 5000 | 0.5596          |
| 0.5423        | 2.3753 | 5250 | 0.5599          |
| 0.5579        | 2.4884 | 5500 | 0.5595          |
| 0.5403        | 2.6015 | 5750 | 0.5591          |
| 0.5475        | 2.7146 | 6000 | 0.5593          |
| 0.5477        | 2.8277 | 6250 | 0.5590          |
| 0.5438        | 2.9408 | 6500 | 0.5586          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.43.1
- Pytorch 2.4.0a0+3bcc3cddb5.nv24.07
- Datasets 2.20.0
- Tokenizers 0.19.1