9ec6b30287bf672083c695b0e5d8a199

This model is a fine-tuned version of meta-llama/Llama-3.2-1B on the nyu-mll/glue [wnli] dataset. It achieves the following results on the evaluation set:

• Loss: 11.2991
• Data Size: 0.25
• Epoch Runtime: 6.4510
• Accuracy: 0.5469
• F1 Macro: 0.3535
• Rouge1: 0.5469
• Rouge2: 0.0
• Rougel: 0.5469
• Rougelsum: 0.5469

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	2.9798	0	1.1505	0.5938	0.5901	0.5938	0.0	0.5938	0.5938
No log	1	19	24.6430	0.0078	0.9676	0.5625	0.36	0.5625	0.0	0.5625	0.5625
No log	2	38	5.4570	0.0156	2.4870	0.4531	0.3347	0.4531	0.0	0.4531	0.4531
No log	3	57	20.5002	0.0312	3.9600	0.5625	0.36	0.5625	0.0	0.5625	0.5625
No log	4	76	23.6446	0.0625	4.9573	0.4375	0.3043	0.4375	0.0	0.4375	0.4375
No log	5	95	51.5391	0.125	6.1106	0.4375	0.3043	0.4375	0.0	0.4375	0.4375
2.4509	6	114	11.2991	0.25	6.4510	0.5469	0.3535	0.5469	0.0	0.5469	0.5469

Framework versions

• Transformers 4.57.0
• Pytorch 2.8.0+cu128
• Datasets 4.3.0
• Tokenizers 0.22.1