README.md

---
library_name: peft
license: mit
base_model: microsoft/Phi-3.5-mini-instruct
tags:
- generated_from_trainer
datasets:
- scitldr
model-index:
- name: Phi-3.5-Mini-Instruct-Summarization-QLoRa
  results: []
pipeline_tag: summarization
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Phi-3.5-Mini-Instruct-Summarization-QLoRa

This model is a fine-tuned version of [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) on the scitldr dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1376

## Model description

More information needed

## Intended uses & limitations

Summarization

## 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: Use paged_adamw_32bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.0519        | 0.2510 | 500  | 2.1280          |
| 2.0279        | 0.5020 | 1000 | 2.1223          |
| 2.0514        | 0.7530 | 1500 | 2.1131          |
| 2.0313        | 1.0040 | 2000 | 2.1142          |
| 1.8923        | 1.2550 | 2500 | 2.1390          |
| 1.8487        | 1.5060 | 3000 | 2.1375          |
| 1.819         | 1.7570 | 3500 | 2.1376          |


### Framework versions

- PEFT 0.14.0
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0