70e1388ecefafc3b75f2d60ab64357d0

This model is a fine-tuned version of deepseek-ai/DeepSeek-R1-Distill-Llama-8B on the nyu-mll/glue [wnli] dataset. It achieves the following results on the evaluation set:

• Loss: 2.8047
• Data Size: 1.0
• Epoch Runtime: 129.4802
• Accuracy: 0.4375
• F1 Macro: 0.3043
• Rouge1: 0.4375
• Rouge2: 0.0
• Rougel: 0.4375
• Rougelsum: 0.4375

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: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• total_train_batch_size: 32
• 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: constant
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Data Size	Epoch Runtime	Accuracy	F1 Macro	Rouge1	Rouge2	Rougel	Rougelsum
No log	0	0	7.6511	0	1.3901	0.4531	0.3720	0.4531	0.0	0.4531	0.4531
No log	1	19	189.25	0.0078	2.2217	0.5625	0.36	0.5625	0.0	0.5625	0.5625
No log	2	38	18.4002	0.0156	15.0158	0.4688	0.3818	0.4688	0.0	0.4688	0.4688
No log	3	57	73.5938	0.0312	25.1872	0.5625	0.36	0.5625	0.0	0.5625	0.5625
No log	4	76	54.5938	0.0625	34.9151	0.5625	0.36	0.5625	0.0	0.5625	0.5625
No log	5	95	3.5075	0.125	47.6844	0.5625	0.36	0.5625	0.0	0.5625	0.5625
3.8963	6	114	4.1026	0.25	53.4379	0.4375	0.3043	0.4375	0.0	0.4375	0.4375
3.8963	7	133	3.5751	0.5	67.9234	0.4375	0.3043	0.4375	0.0	0.4375	0.4375
2.293	8.0	152	2.7422	1.0	93.1096	0.5625	0.36	0.5625	0.0	0.5625	0.5625
2.293	9.0	171	3.3115	1.0	91.7085	0.4375	0.3043	0.4375	0.0	0.4375	0.4375
2.293	10.0	190	2.8148	1.0	100.1277	0.5625	0.36	0.5625	0.0	0.5625	0.5625
2.8762	11.0	209	2.7629	1.0	110.4708	0.5625	0.36	0.5625	0.0	0.5625	0.5625
2.8762	12.0	228	2.8047	1.0	129.4802	0.4375	0.3043	0.4375	0.0	0.4375	0.4375

Framework versions

• Transformers 4.57.0
• Pytorch 2.8.0+cu128
• Datasets 4.0.0
• Tokenizers 0.22.1