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---
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
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