---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: gpt2-alpaca-instruction-fine-tuning-qlora
  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. -->

# gpt2-alpaca-instruction-fine-tuning-qlora

This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8887

## 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: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 0.02  | 50   | 2.4375          |
| No log        | 0.04  | 100  | 2.375           |
| No log        | 0.06  | 150  | 2.2637          |
| No log        | 0.08  | 200  | 2.1855          |
| No log        | 0.1   | 250  | 2.1094          |
| No log        | 0.12  | 300  | 2.0840          |
| No log        | 0.14  | 350  | 2.0977          |
| No log        | 0.16  | 400  | 2.0488          |
| No log        | 0.18  | 450  | 2.0332          |
| No log        | 0.2   | 500  | 2.0312          |
| No log        | 0.22  | 550  | 2.0352          |
| No log        | 0.24  | 600  | 2.0430          |
| No log        | 0.26  | 650  | 2.0117          |
| No log        | 0.28  | 700  | 2.0117          |
| No log        | 0.3   | 750  | 2.0059          |
| No log        | 0.32  | 800  | 1.9961          |
| No log        | 0.34  | 850  | 1.9551          |
| No log        | 0.36  | 900  | 1.9463          |
| No log        | 0.38  | 950  | 1.9854          |
| 2.4218        | 0.4   | 1000 | 1.9883          |
| 2.4218        | 0.42  | 1050 | 1.9766          |
| 2.4218        | 0.44  | 1100 | 1.9424          |
| 2.4218        | 0.46  | 1150 | 1.9727          |
| 2.4218        | 0.48  | 1200 | 1.9473          |
| 2.4218        | 0.5   | 1250 | 1.9580          |
| 2.4218        | 0.52  | 1300 | 1.9404          |
| 2.4218        | 0.54  | 1350 | 1.9287          |
| 2.4218        | 0.56  | 1400 | 1.9473          |
| 2.4218        | 0.58  | 1450 | 1.9209          |
| 2.4218        | 0.6   | 1500 | 1.9219          |
| 2.4218        | 0.62  | 1550 | 1.9336          |
| 2.4218        | 0.64  | 1600 | 1.9287          |
| 2.4218        | 0.66  | 1650 | 1.9082          |
| 2.4218        | 0.68  | 1700 | 1.9219          |
| 2.4218        | 0.7   | 1750 | 1.8994          |
| 2.4218        | 0.72  | 1800 | 1.9092          |
| 2.4218        | 0.74  | 1850 | 1.8877          |
| 2.4218        | 0.76  | 1900 | 1.8994          |
| 2.4218        | 0.78  | 1950 | 1.8955          |
| 2.1818        | 0.8   | 2000 | 1.8896          |
| 2.1818        | 0.82  | 2050 | 1.8867          |
| 2.1818        | 0.84  | 2100 | 1.8857          |
| 2.1818        | 0.86  | 2150 | 1.8916          |
| 2.1818        | 0.88  | 2200 | 1.8857          |
| 2.1818        | 0.9   | 2250 | 1.8916          |
| 2.1818        | 0.92  | 2300 | 1.8926          |
| 2.1818        | 0.94  | 2350 | 1.8896          |
| 2.1818        | 0.96  | 2400 | 1.8887          |
| 2.1818        | 0.98  | 2450 | 1.8887          |
| 2.1818        | 1.0   | 2500 | 1.8887          |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3