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llama-3.1-spam-vs-no-spam-classification – AI Model by edvaldomelo | AlphaNeural AI
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llama-3.1-spam-vs-no-spam-classification
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peft
safetensors
adapter
lora
transformers
meta-llama/Llama-3.1-8B-Instruct
llama3.1
us
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llama-3.1-spam-vs-no-spam-classification
This model is a fine-tuned version of
meta-llama/Llama-3.1-8B-Instruct
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.1263
F1: 0.9926
Precision: 1.0
Recall: 0.9854
Auc: 0.9993
Accuracy: 0.9965
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: 0.000347
train_batch_size: 22
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 0.011187
num_epochs: 5
Training results
Framework versions
PEFT 0.17.1
Transformers 4.57.1
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.22.1
