---
base_model: meta-llama/Meta-Llama-3.1-8B
datasets:
- scitldr
library_name: peft
license: llama3.1
tags:
- generated_from_trainer
model-index:
- name: Llama-3.1-8B-Summarization-QLoRa
  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. -->

# Llama-3.1-8B-Summarization-QLoRa

This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) on the scitldr dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3813

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

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.1968        | 0.2008 | 200  | 2.2962          |
| 2.2026        | 0.4016 | 400  | 2.3085          |
| 2.205         | 0.6024 | 600  | 2.3048          |
| 2.2028        | 0.8032 | 800  | 2.2968          |
| 2.2001        | 1.0040 | 1000 | 2.2911          |
| 1.7063        | 1.2048 | 1200 | 2.3696          |
| 1.6856        | 1.4056 | 1400 | 2.3756          |
| 1.6556        | 1.6064 | 1600 | 2.3823          |
| 1.6331        | 1.8072 | 1800 | 2.3813          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1