---
license: mit
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
datasets:
- scitldr
base_model: microsoft/phi-1_5
model-index:
- name: Phi-1.5-Summarization-LoRA
  results: []
pipeline_tag: summarization
---

<!-- 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-1.5 Summarization (LoRA)

This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co./microsoft/phi-1_5) on the scitldr dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6242

## 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: 0.001
- 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

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6014        | 0.25  | 500  | 2.6496          |
| 2.5756        | 0.5   | 1000 | 2.6445          |
| 2.5945        | 0.75  | 1500 | 2.6291          |
| 2.5251        | 1.0   | 2000 | 2.6133          |
| 2.3196        | 1.26  | 2500 | 2.6370          |
| 2.2953        | 1.51  | 3000 | 2.6325          |
| 2.452         | 1.76  | 3500 | 2.6242          |


### Framework versions

- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2