llama3.2-rank-1-weighted

This model is a fine-tuned version of meta-llama/Llama-3.2-3B-Instruct on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.7097
• Accuracy: 0.8983
• F1: 0.8928
• Recall: 0.8467
• Fpr: 0.05
• Auc: 0.9521
• Mcc: 0.8010

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: 0.0002
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Recall	Fpr	Auc	Mcc
0.6591	1.0	313	0.3143	0.8783	0.8843	0.93	0.1733	0.9521	0.7607
0.5839	2.0	626	0.2627	0.8933	0.8971	0.93	0.1433	0.9602	0.7888
0.5069	3.0	939	0.4950	0.905	0.9009	0.8633	0.0533	0.9651	0.8128
0.3144	4.0	1252	0.5836	0.9	0.8932	0.8367	0.0367	0.9629	0.8065
0.0742	5.0	1565	0.6851	0.8933	0.8853	0.8233	0.0367	0.9648	0.7945
0.0315	6.0	1878	0.7731	0.9067	0.9004	0.8433	0.03	0.9683	0.8199
0.006	7.0	2191	0.9968	0.8933	0.8836	0.81	0.0233	0.9665	0.7978
0.0024	8.0	2504	0.9168	0.8983	0.8897	0.82	0.0233	0.9668	0.8066
0.0003	9.0	2817	0.9211	0.8967	0.8877	0.8167	0.0233	0.9665	0.8037
0.0007	10.0	3130	0.9188	0.9	0.8917	0.8233	0.0233	0.9668	0.8096

Framework versions

• PEFT 0.7.0
• Transformers 4.57.3
• Pytorch 2.9.1+cu128
• Datasets 2.15.0
• Tokenizers 0.22.1