tensorboard generated_from_trainer dataset:glue base_model:meta-llama/Llama-2-7b-hf region:us
---
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: []
---

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

This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6354
- Accuracy: 0.8701
- F1: 0.9062

## 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: 1.2800000000000003e-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: 40

### 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 | 0.8456   | 0.8904 | 0.3982          |
| 0.2799        | 14.0  | 3220 | 0.8456   | 0.8881 | 0.3873          |
| 0.2799        | 15.0  | 3450 | 0.8529   | 0.8940 | 0.3939          |
| 0.2511        | 16.0  | 3680 | 0.8431   | 0.8877 | 0.4018          |
| 0.2511        | 17.0  | 3910 | 0.8529   | 0.8947 | 0.3969          |
| 0.2371        | 18.0  | 4140 | 0.8456   | 0.8912 | 0.3963          |
| 0.2371        | 19.0  | 4370 | 0.8578   | 0.8964 | 0.3865          |
| 0.2211        | 20.0  | 4600 | 0.8505   | 0.8928 | 0.4165          |
| 0.2211        | 21.0  | 4830 | 0.8456   | 0.8901 | 0.4070          |
| 0.2136        | 22.0  | 5060 | 0.8578   | 0.8972 | 0.4090          |
| 0.2136        | 23.0  | 5290 | 0.8578   | 0.8961 | 0.4328          |
| 0.1774        | 24.0  | 5520 | 0.8382   | 0.8791 | 0.4602          |
| 0.1774        | 25.0  | 5750 | 0.8627   | 0.9018 | 0.4551          |
| 0.1774        | 26.0  | 5980 | 0.8505   | 0.8920 | 0.4677          |
| 0.1521        | 27.0  | 6210 | 0.8578   | 0.8953 | 0.4854          |
| 0.1521        | 28.0  | 6440 | 0.8505   | 0.8932 | 0.5064          |
| 0.134         | 29.0  | 6670 | 0.8603   | 0.8988 | 0.4971          |
| 0.134         | 30.0  | 6900 | 0.8676   | 0.9046 | 0.4717          |
| 0.1298        | 31.0  | 7130 | 0.5216   | 0.8652 | 0.8998          |
| 0.1298        | 32.0  | 7360 | 0.5339   | 0.8578 | 0.8979          |
| 0.1233        | 33.0  | 7590 | 0.5533   | 0.8627 | 0.8993          |
| 0.1233        | 34.0  | 7820 | 0.5526   | 0.875  | 0.9084          |
| 0.1094        | 35.0  | 8050 | 0.6027   | 0.8725 | 0.9068          |
| 0.1094        | 36.0  | 8280 | 0.6441   | 0.8652 | 0.9037          |
| 0.0906        | 37.0  | 8510 | 0.6289   | 0.8554 | 0.8929          |
| 0.0906        | 38.0  | 8740 | 0.6213   | 0.8676 | 0.9039          |
| 0.0906        | 39.0  | 8970 | 0.6585   | 0.8603 | 0.8977          |
| 0.0842        | 40.0  | 9200 | 0.6354   | 0.8701 | 0.9062          |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
