README.md

---
library_name: peft
license: llama3.2
base_model: meta-llama/Llama-3.2-1B
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: LLama3-finetuning
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# LLama3-finetuning

This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3923
- Accuracy: 0.8414
- F1 Macro: 0.8365

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| 1.8719        | 1.0   | 454  | 0.8635          | 0.6562   | 0.6261   |
| 0.9455        | 2.0   | 908  | 0.4734          | 0.8168   | 0.8068   |
| 0.7437        | 3.0   | 1362 | 0.4071          | 0.8366   | 0.8305   |
| 0.7825        | 4.0   | 1816 | 0.3959          | 0.8433   | 0.8391   |
| 0.6047        | 5.0   | 2270 | 0.3910          | 0.8400   | 0.8341   |


### Framework versions

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