--- 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: [] --- # 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