This model is a fine-tuned version of agentica-org/DeepScaleR-1.5B-Preview on the logic_speculative_32B_T_R1_32B_S_DeepScaleR-1.5B dataset. It achieves the following results on the evaluation set:

Loss: 0.2539

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: 1e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 4
total_train_batch_size: 4
total_eval_batch_size: 4
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
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
0.2382	0.5952	100	0.2488
0.2391	1.1905	200	0.2537
0.2466	1.7857	300	0.2540

Framework versions

Transformers 4.46.1
Pytorch 2.6.0+cu124
Datasets 3.1.0
Tokenizers 0.20.3

cutelemonlili/random_NP88OBdFmIS2E6lW

logic_speculative_32B_T_R1_32B_S_DeepScaleR-1.5B

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

cutelemonlili/
random_NP88OBdFmIS2E6lW