---
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: 0.8588

## 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: 4
- 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.0606        | 0.1071 | 100  | 1.0032          |
| 0.9107        | 0.2141 | 200  | 0.9256          |
| 0.8783        | 0.3212 | 300  | 0.9081          |
| 0.8761        | 0.4283 | 400  | 0.8986          |
| 0.8651        | 0.5353 | 500  | 0.8920          |
| 0.864         | 0.6424 | 600  | 0.8875          |
| 0.8759        | 0.7495 | 700  | 0.8828          |
| 0.8584        | 0.8565 | 800  | 0.8807          |
| 0.8677        | 0.9636 | 900  | 0.8784          |
| 0.8507        | 1.0707 | 1000 | 0.8757          |
| 0.8499        | 1.1777 | 1100 | 0.8739          |
| 0.8446        | 1.2848 | 1200 | 0.8718          |
| 0.8637        | 1.3919 | 1300 | 0.8712          |
| 0.8238        | 1.4989 | 1400 | 0.8686          |
| 0.8231        | 1.6060 | 1500 | 0.8681          |
| 0.8361        | 1.7131 | 1600 | 0.8661          |
| 0.8319        | 1.8201 | 1700 | 0.8652          |
| 0.8166        | 1.9272 | 1800 | 0.8643          |
| 0.8312        | 2.0343 | 1900 | 0.8634          |
| 0.834         | 2.1413 | 2000 | 0.8625          |
| 0.8362        | 2.2484 | 2100 | 0.8616          |
| 0.8413        | 2.3555 | 2200 | 0.8611          |
| 0.8153        | 2.4625 | 2300 | 0.8605          |
| 0.8235        | 2.5696 | 2400 | 0.8607          |
| 0.7958        | 2.6767 | 2500 | 0.8598          |
| 0.8137        | 2.7837 | 2600 | 0.8593          |
| 0.8162        | 2.8908 | 2700 | 0.8591          |
| 0.8317        | 2.9979 | 2800 | 0.8588          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.43.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1