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

Loss: 0.1446

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: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 4
total_train_batch_size: 128
total_eval_batch_size: 32
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: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.0607	0.7228	500	0.0733
0.029	1.4452	1000	0.0819
0.0058	2.1677	1500	0.1524
0.005	2.8905	2000	0.1443

Framework versions

Transformers 4.57.1
Pytorch 2.6.0+cu124
Datasets 4.0.0
Tokenizers 0.22.1

FlyPig23/Llama3.2-3B_Paper_Impact_SFT

Llama3.2-3B_Paper_Impact_SFT

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

FlyPig23/
Llama3.2-3B_Paper_Impact_SFT