This model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct on the saiteki-kai/BeaverTails-it dataset. It achieves the following results on the evaluation set:

Loss: 0.0781
Accuracy: 0.6964
Macro F1: 0.6445
Macro Precision: 0.7365
Macro Recall: 0.5970
Micro F1: 0.7539
Micro Precision: 0.8036
Micro Recall: 0.7100
Flagged/accuracy: 0.8560
Flagged/precision: 0.9050
Flagged/recall: 0.8283
Flagged/f1: 0.8649

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: 2.8362564501611134e-07
train_batch_size: 16
eval_batch_size: 32
seed: 42
distributed_type: multi-GPU
num_devices: 4
total_train_batch_size: 64
total_eval_batch_size: 128
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Macro F1	Macro Precision	Macro Recall	Micro F1	Micro Precision	Micro Recall	Flagged/accuracy	Flagged/precision	Flagged/recall	Flagged/f1
0.0688	1.0	8454	0.0799	0.6891	0.6367	0.7276	0.5931	0.7464	0.8015	0.6984	0.8491	0.8948	0.8260	0.8590
0.0745	2.0	16908	0.0777	0.6956	0.6295	0.7647	0.5680	0.7503	0.8171	0.6935	0.8532	0.9108	0.8160	0.8608
0.06	3.0	25362	0.0781	0.6965	0.6444	0.7361	0.5968	0.7539	0.8035	0.7100	0.8561	0.9050	0.8284	0.8650

Framework versions

Transformers 4.51.3
Pytorch 2.7.0+cu118
Datasets 3.5.1
Tokenizers 0.21.1

saiteki-kai/QA-Llama-3.1

QA-Llama-3.1-4155

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

saiteki-kai/
QA-Llama-3.1