---
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: []
---

<!-- 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. -->

# Genpro_Llama3-8b

This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/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