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---
license: llama3
library_name: peft
tags:
- generated_from_trainer
base_model: meta-llama/Meta-Llama-3-8B
metrics:
- accuracy
- precision
- recall
model-index:
- name: llama3-8B_Fact_U
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. -->
# llama3-8B_Fact_U
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: 1.1146
- Accuracy: 0.7953
- Precision: 0.8252
- Recall: 0.7692
- F1 score: 0.7963
## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 score |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| 0.9272 | 0.2509 | 200 | 1.1877 | 0.5765 | 0.5589 | 0.8801 | 0.6837 |
| 0.7133 | 0.5019 | 400 | 0.7246 | 0.7 | 0.6912 | 0.7647 | 0.7261 |
| 0.6476 | 0.7528 | 600 | 0.6219 | 0.7565 | 0.7945 | 0.7172 | 0.7539 |
| 0.5766 | 1.0038 | 800 | 0.5714 | 0.76 | 0.85 | 0.6538 | 0.7391 |
| 0.4292 | 1.2547 | 1000 | 0.5870 | 0.7588 | 0.7474 | 0.8100 | 0.7774 |
| 0.4047 | 1.5056 | 1200 | 0.6078 | 0.7824 | 0.7694 | 0.8303 | 0.7987 |
| 0.4753 | 1.7566 | 1400 | 0.5654 | 0.7929 | 0.8575 | 0.7217 | 0.7838 |
| 0.3831 | 2.0075 | 1600 | 0.5672 | 0.8012 | 0.8289 | 0.7783 | 0.8028 |
| 0.2666 | 2.2585 | 1800 | 0.6054 | 0.8047 | 0.8416 | 0.7692 | 0.8038 |
| 0.3267 | 2.5094 | 2000 | 0.6620 | 0.7929 | 0.8182 | 0.7738 | 0.7953 |
| 0.2742 | 2.7604 | 2200 | 0.6985 | 0.7941 | 0.8579 | 0.7240 | 0.7853 |
| 0.3125 | 3.0113 | 2400 | 0.6373 | 0.7859 | 0.7955 | 0.7919 | 0.7937 |
| 0.1743 | 3.2622 | 2600 | 0.8362 | 0.8047 | 0.8730 | 0.7308 | 0.7956 |
| 0.1837 | 3.5132 | 2800 | 0.7889 | 0.7965 | 0.8440 | 0.7466 | 0.7923 |
| 0.2013 | 3.7641 | 3000 | 0.8958 | 0.7659 | 0.7425 | 0.8416 | 0.7890 |
| 0.1572 | 4.0151 | 3200 | 0.8662 | 0.8012 | 0.864 | 0.7330 | 0.7931 |
| 0.0993 | 4.2660 | 3400 | 0.8730 | 0.8024 | 0.8477 | 0.7557 | 0.7990 |
| 0.0704 | 4.5169 | 3600 | 1.0571 | 0.8012 | 0.8421 | 0.7602 | 0.7990 |
| 0.0598 | 4.7679 | 3800 | 1.1146 | 0.7953 | 0.8252 | 0.7692 | 0.7963 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.44.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |