llama-160m-boolq

This model is a fine-tuned version of JackFram/llama-160m on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.6795
• Accuracy: 0.5957

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: 16
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.99	73	0.6870	0.5731
No log	1.99	147	0.6825	0.5957
No log	3.0	221	0.6809	0.6012
No log	3.96	292	0.6795	0.5957

Framework versions

• Transformers 4.31.0
• Pytorch 2.0.1+cu117
• Datasets 2.18.0
• Tokenizers 0.13.3