|
--- |
|
license: llama3 |
|
library_name: peft |
|
tags: |
|
- generated_from_trainer |
|
base_model: meta-llama/Meta-Llama-3-8B |
|
metrics: |
|
- accuracy |
|
- precision |
|
- recall |
|
model-index: |
|
- name: llama3-8B_MT |
|
results: [] |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# llama3-8B_MT |
|
|
|
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.6988 |
|
- Accuracy: 0.7817 |
|
- Precision: 0.7394 |
|
- Recall: 0.87 |
|
- F1 score: 0.7994 |
|
|
|
## 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: 0.0001 |
|
- train_batch_size: 16 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 5 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Accuracy | F1 score | Precision | Recall | Validation Loss | |
|
|:-------------:|:-----:|:----:|:--------:|:--------:|:---------:|:------:|:---------------:| |
|
| 0.7787 | 0.5 | 200 | 0.7533 | 0.7605 | 0.7390 | 0.7833 | 0.6041 | |
|
| 0.5489 | 1.0 | 400 | 0.73 | 0.7484 | 0.7006 | 0.8033 | 0.5793 | |
|
| 0.3894 | 1.5 | 600 | 0.6433 | 0.7214 | 0.5919 | 0.9233 | 0.7408 | |
|
| 0.3638 | 2.0 | 800 | 0.7533 | 0.7817 | 0.7011 | 0.8833 | 0.5249 | |
|
| 0.2665 | 2.5 | 1000 | 0.4719 | 0.8 | 0.7744 | 0.8467 | 0.8089 | |
|
| 0.2587 | 3.0 | 1200 | 0.4709 | 0.7917 | 0.7660 | 0.84 | 0.8013 | |
|
| 0.1716 | 3.5 | 1400 | 0.4906 | 0.8083 | 0.7955 | 0.83 | 0.8124 | |
|
| 0.1672 | 4.0 | 1600 | 0.8441 | 0.7083 | 0.6478 | 0.9133 | 0.7580 | |
|
| 0.0866 | 4.5 | 1800 | 0.6659 | 0.7917 | 0.7522 | 0.87 | 0.8068 | |
|
| 0.0822 | 5.0 | 2000 | 0.6988 | 0.7817 | 0.7394 | 0.87 | 0.7994 | |
|
|
|
|
|
### Framework versions |
|
|
|
- PEFT 0.11.1 |
|
- Transformers 4.44.2 |
|
- Pytorch 2.3.0+cu121 |
|
- Datasets 2.19.1 |
|
- Tokenizers 0.19.1 |
