This model is a fine-tuned version of TaiGary/llama3.2_3b_NuminaMath-CoT_100k on the MATH_training_Qwen2.5-32B-Instruct dataset. It achieves the following results on the evaluation set:

Loss: 0.2421

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: 2
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 4
total_train_batch_size: 8
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
0.2962	0.2999	200	0.2787
0.2305	0.5997	400	0.2601
0.2155	0.8996	600	0.2427
0.0912	1.1994	800	0.2540
0.0823	1.4993	1000	0.2448
0.0889	1.7991	1200	0.2425

Framework versions

Transformers 4.46.1
Pytorch 2.5.1+cu124
Datasets 3.1.0
Tokenizers 0.20.3

cutelemonlili/llama3.2_3b_NuminaMath-CoT_100k_MATH_training_Qwen2.5-32B-Instruct

MATH_training_Qwen2.5-32B-Instruct

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

cutelemonlili/
llama3.2_3b_NuminaMath-CoT_100k_MATH_training_Qwen2.5-32B-Instruct