metadata
license: llama3
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: meta-llama/Meta-Llama-3-8B
model-index:
- name: Genpro_Llama3-8b
results: []
Genpro_Llama3-8b
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5784
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: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.3266 | 0.0634 | 100 | 1.3260 |
| 1.123 | 0.1267 | 200 | 1.1090 |
| 1.0242 | 0.1901 | 300 | 1.0121 |
| 1.0228 | 0.2535 | 400 | 0.9520 |
| 0.9834 | 0.3169 | 500 | 0.9037 |
| 0.9726 | 0.3802 | 600 | 0.8456 |
| 0.9003 | 0.4436 | 700 | 0.8270 |
| 0.8862 | 0.5070 | 800 | 0.7967 |
| 0.7788 | 0.5703 | 900 | 0.7715 |
| 0.831 | 0.6337 | 1000 | 0.7528 |
| 0.7875 | 0.6971 | 1100 | 0.7319 |
| 0.8284 | 0.7605 | 1200 | 0.7097 |
| 0.7387 | 0.8238 | 1300 | 0.6927 |
| 0.7573 | 0.8872 | 1400 | 0.6735 |
| 0.7744 | 0.9506 | 1500 | 0.6668 |
| 0.5684 | 1.0139 | 1600 | 0.6487 |
| 0.5606 | 1.0773 | 1700 | 0.6378 |
| 0.5268 | 1.1407 | 1800 | 0.6363 |
| 0.5727 | 1.2041 | 1900 | 0.6269 |
| 0.5456 | 1.2674 | 2000 | 0.6196 |
| 0.5174 | 1.3308 | 2100 | 0.6146 |
| 0.499 | 1.3942 | 2200 | 0.6055 |
| 0.5831 | 1.4575 | 2300 | 0.5984 |
| 0.4884 | 1.5209 | 2400 | 0.5952 |
| 0.5538 | 1.5843 | 2500 | 0.5829 |
| 0.5302 | 1.6477 | 2600 | 0.5805 |
| 0.5506 | 1.7110 | 2700 | 0.5758 |
| 0.5509 | 1.7744 | 2800 | 0.5708 |
| 0.5249 | 1.8378 | 2900 | 0.5597 |
| 0.5249 | 1.9011 | 3000 | 0.5601 |
| 0.4597 | 1.9645 | 3100 | 0.5585 |
| 0.383 | 2.0279 | 3200 | 0.5643 |
| 0.4115 | 2.0913 | 3300 | 0.5666 |
| 0.3928 | 2.1546 | 3400 | 0.5737 |
| 0.4634 | 2.2180 | 3500 | 0.5587 |
| 0.4093 | 2.2814 | 3600 | 0.5615 |
| 0.3724 | 2.3447 | 3700 | 0.5529 |
| 0.3846 | 2.4081 | 3800 | 0.5604 |
| 0.4206 | 2.4715 | 3900 | 0.5539 |
| 0.4803 | 2.5349 | 4000 | 0.5422 |
| 0.4319 | 2.5982 | 4100 | 0.5452 |
| 0.3762 | 2.6616 | 4200 | 0.5523 |
| 0.4472 | 2.7250 | 4300 | 0.5319 |
| 0.4048 | 2.7883 | 4400 | 0.5370 |
| 0.4227 | 2.8517 | 4500 | 0.5401 |
| 0.4407 | 2.9151 | 4600 | 0.5294 |
| 0.3998 | 2.9785 | 4700 | 0.5282 |
| 0.336 | 3.0418 | 4800 | 0.5504 |
| 0.3022 | 3.1052 | 4900 | 0.5608 |
| 0.3323 | 3.1686 | 5000 | 0.5584 |
| 0.3306 | 3.2319 | 5100 | 0.5560 |
| 0.3557 | 3.2953 | 5200 | 0.5478 |
| 0.3475 | 3.3587 | 5300 | 0.5656 |
| 0.3515 | 3.4221 | 5400 | 0.5520 |
| 0.3236 | 3.4854 | 5500 | 0.5479 |
| 0.3886 | 3.5488 | 5600 | 0.5436 |
| 0.339 | 3.6122 | 5700 | 0.5408 |
| 0.3509 | 3.6755 | 5800 | 0.5499 |
| 0.3651 | 3.7389 | 5900 | 0.5447 |
| 0.3707 | 3.8023 | 6000 | 0.5340 |
| 0.3122 | 3.8657 | 6100 | 0.5360 |
| 0.3613 | 3.9290 | 6200 | 0.5326 |
| 0.364 | 3.9924 | 6300 | 0.5315 |
| 0.2418 | 4.0558 | 6400 | 0.5719 |
| 0.2349 | 4.1191 | 6500 | 0.5686 |
| 0.2366 | 4.1825 | 6600 | 0.5750 |
| 0.2433 | 4.2459 | 6700 | 0.5739 |
| 0.2566 | 4.3093 | 6800 | 0.5664 |
| 0.2524 | 4.3726 | 6900 | 0.5798 |
| 0.2667 | 4.4360 | 7000 | 0.5570 |
| 0.2528 | 4.4994 | 7100 | 0.5573 |
| 0.2348 | 4.5627 | 7200 | 0.5723 |
| 0.2629 | 4.6261 | 7300 | 0.5742 |
| 0.2705 | 4.6895 | 7400 | 0.5743 |
| 0.2893 | 4.7529 | 7500 | 0.5560 |
| 0.2371 | 4.8162 | 7600 | 0.5652 |
| 0.287 | 4.8796 | 7700 | 0.5436 |
| 0.2725 | 4.9430 | 7800 | 0.5784 |
Framework versions
- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1