This model is a fine-tuned version of PowerInfer/SmallThinker-3B-Preview on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0417

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
gradient_accumulation_steps: 8
total_train_batch_size: 64
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine_with_restarts
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
0.3572	0.3458	30	0.0929
0.0809	0.6916	60	0.0417

Framework versions

Transformers 4.50.3
Pytorch 2.5.1+cu124
Datasets 3.5.0
Tokenizers 0.21.1

yangwooko/smartmind-cyberone-20250401-test

smartmind-cyberone-20250401-test

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

yangwooko/
smartmind-cyberone-20250401-test