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hurtmongoose/
llama3.2-rslora-rank8-hurtmongoose

Name: llama3.2-rslora-rank8-hurtmongoose
Availability: InStock

meta-llama/Llama-3.2-3B-Instruct

llama3.2

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llama3.2-rslora-rank8-hurtmongoose

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: 0.2682
Accuracy: 0.9067
Mcc: 0.8134
Recall: 0.9033
Fpr: 0.09
Auc: 0.9672

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: 4
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Mcc	Recall	Fpr	Auc
1.1279	1.0	313	0.2493	0.8983	0.7968	0.9067	0.11	0.9664
1.2131	2.0	626	0.3415	0.91	0.8209	0.8867	0.0667	0.9642
0.198	3.0	939	1.0070	0.8883	0.7848	0.8167	0.04	0.9694
0.1382	4.0	1252	1.4709	0.8867	0.7803	0.82	0.0467	0.9659
0.0712	5.0	1565	1.6248	0.885	0.7780	0.8133	0.0433	0.9667

Framework versions

PEFT 0.18.0
Transformers 4.57.3
Pytorch 2.9.1+cu128
Datasets 2.15.0
Tokenizers 0.22.1

hurtmongoose/llama3.2-rslora-rank8-hurtmongoose

llama3.2-rslora-rank8-hurtmongoose

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

hurtmongoose/
llama3.2-rslora-rank8-hurtmongoose