---
license: mit
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: microsoft/Phi-3-mini-128k-instruct
datasets:
- scitldr
model-index:
- name: Summarization-Phi-3
  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. -->

# Summarization-Phi-3

This model is a fine-tuned version of [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) on the scitldr dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1554

## 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: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.0689        | 0.2510 | 500  | 2.1439          |
| 2.0455        | 0.5020 | 1000 | 2.1388          |
| 2.0665        | 0.7530 | 1500 | 2.1349          |
| 2.0481        | 1.0040 | 2000 | 2.1308          |
| 1.9051        | 1.2550 | 2500 | 2.1573          |
| 1.8524        | 1.5060 | 3000 | 2.1588          |
| 1.8247        | 1.7570 | 3500 | 2.1554          |


### Framework versions

- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1