metadata

base_model: meta-llama/Llama-2-7b-hf
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - accuracy
  - f1
model-index:
  - name: Llama-2-7b-hf-finetuned-mrpc-v0.4
    results: []

Llama-2-7b-hf-finetuned-mrpc-v0.4

This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on the glue dataset. It achieves the following results on the evaluation set:

Loss: 0.4018
Accuracy: 0.8431
F1: 0.8877

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: 1.6000000000000003e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 16

Training results

Training Loss	Epoch	Step	Accuracy	F1	Validation Loss
No log	1.0	230	0.6446	0.7695	0.6542
No log	2.0	460	0.6912	0.7968	0.5938
0.6489	3.0	690	0.7230	0.8151	0.5694
0.6489	4.0	920	0.7230	0.8138	0.5503
0.5299	5.0	1150	0.7402	0.8251	0.5492
0.5299	6.0	1380	0.7794	0.8432	0.4880
0.4687	7.0	1610	0.8064	0.8663	0.4559
0.4687	8.0	1840	0.8186	0.875	0.4298
0.374	9.0	2070	0.8284	0.8818	0.4210
0.374	10.0	2300	0.8456	0.8916	0.3953
0.3096	11.0	2530	0.8431	0.8897	0.4074
0.3096	12.0	2760	0.8407	0.8862	0.4030
0.3096	13.0	2990	0.3982	0.8456	0.8904
0.2799	14.0	3220	0.3873	0.8456	0.8881
0.2799	15.0	3450	0.3939	0.8529	0.8940
0.2511	16.0	3680	0.4018	0.8431	0.8877

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3