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---
license: llama3
library_name: peft
tags:
- generated_from_trainer
base_model: meta-llama/Meta-Llama-3-8B
metrics:
- accuracy
model-index:
- name: LLAMA3_8b_LORA_FOR_CLASSIFICATION
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_LORA_FOR_CLASSIFICATION
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: 0.6062
- Balanced Accuracy: 0.86
- Accuracy: 0.86
- Micro F1: 0.86
- Macro F1: 0.8600
- Weighted F1: 0.8600
- Classification Report: precision recall f1-score support
0 0.86 0.85 0.86 200
1 0.86 0.86 0.86 200
accuracy 0.86 400
macro avg 0.86 0.86 0.86 400
weighted avg 0.86 0.86 0.86 400
## 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: 8
- 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 | Accuracy | Balanced Accuracy | Classification Report | Validation Loss | Macro F1 | Micro F1 | Weighted F1 |
|:-------------:|:-----:|:----:|:--------:|:-----------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------:|:--------:|:--------:|:-----------:|
| 0.5306 | 1.0 | 732 | 0.8125 | 0.8125 | precision recall f1-score support
0 0.76 0.92 0.83 200
1 0.90 0.70 0.79 200
accuracy 0.81 400
macro avg 0.83 0.81 0.81 400
weighted avg 0.83 0.81 0.81 400
| 0.4840 | 0.8103 | 0.8125 | 0.8103 |
| 0.4284 | 2.0 | 1464 | 0.4444 | 0.815 | 0.815 | 0.815 | 0.8147 | 0.8147 | precision recall f1-score support
0 0.84 0.78 0.81 200
1 0.79 0.85 0.82 200
accuracy 0.81 400
macro avg 0.82 0.81 0.81 400
weighted avg 0.82 0.81 0.81 400
|
| 0.3809 | 3.0 | 2196 | 0.4513 | 0.8475 | 0.8475 | 0.8475 | 0.8470 | 0.8470 | precision recall f1-score support
0 0.81 0.91 0.86 200
1 0.89 0.79 0.84 200
accuracy 0.85 400
macro avg 0.85 0.85 0.85 400
weighted avg 0.85 0.85 0.85 400
|
| 0.2413 | 4.0 | 2928 | 0.5228 | 0.87 | 0.87 | 0.87 | 0.8700 | 0.8700 | precision recall f1-score support
0 0.87 0.86 0.87 200
1 0.87 0.88 0.87 200
accuracy 0.87 400
macro avg 0.87 0.87 0.87 400
weighted avg 0.87 0.87 0.87 400
|
| 0.1499 | 5.0 | 3660 | 0.6062 | 0.86 | 0.86 | 0.86 | 0.8600 | 0.8600 | precision recall f1-score support
0 0.86 0.85 0.86 200
1 0.86 0.86 0.86 200
accuracy 0.86 400
macro avg 0.86 0.86 0.86 400
weighted avg 0.86 0.86 0.86 400
|
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |