---
library_name: peft
license: llama3.2
base_model: meta-llama/Llama-3.2-3B
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: LLama3-3B-finetuning
  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. -->

# LLama3-3B-finetuning

This model is a fine-tuned version of [meta-llama/Llama-3.2-3B](https://huggingface.co./meta-llama/Llama-3.2-3B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4744
- Accuracy: 0.8077
- F1 Macro: 0.8046

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| 2.4745        | 1.0   | 149  | 1.1147          | 0.5471   | 0.5000   |
| 1.0995        | 2.0   | 298  | 0.5006          | 0.8047   | 0.8023   |
| 0.7117        | 3.0   | 447  | 0.4292          | 0.8215   | 0.8218   |
| 0.5494        | 4.0   | 596  | 0.4039          | 0.8468   | 0.8469   |
| 0.4276        | 5.0   | 745  | 0.3924          | 0.8418   | 0.8415   |
| 0.2451        | 6.0   | 894  | 0.4101          | 0.8300   | 0.8312   |


### Framework versions

- PEFT 0.14.0
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0