This model is a fine-tuned version of deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B on the logic_speculative_DeepScaleR_data dataset. It achieves the following results on the evaluation set:

Loss: 0.2754

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: 4

Training results

Training Loss	Epoch	Step	Validation Loss
0.2188	0.5435	100	0.2600
0.2359	1.0870	200	0.2634
0.1853	1.6304	300	0.2632
0.2008	2.1739	400	0.2690
0.2593	2.7174	500	0.2706
0.1758	3.2609	600	0.2746
0.1914	3.8043	700	0.2753

Framework versions

Transformers 4.46.1
Pytorch 2.6.0+cu124
Datasets 3.1.0
Tokenizers 0.20.3

cutelemonlili/random_Rbyt7JTkGt43uQbO

logic_speculative_DeepScaleR_data

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

cutelemonlili/
random_Rbyt7JTkGt43uQbO