---
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