---
library_name: peft
license: llama3.1
base_model: meta-llama/Llama-3.1-8B-Instruct
tags:
- base_model:adapter:meta-llama/Llama-3.1-8B-Instruct
- lora
- transformers
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: llama3-section-classifier
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# llama3-section-classifier

This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1236
- Accuracy: 0.6536
- Precision: 0.6613
- Recall: 0.6536
- F1: 0.6553

## 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: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 9.6616        | 1.0   | 310  | 1.1046          | 0.6464   | 0.6476    | 0.6464 | 0.6244 |
| 7.0864        | 2.0   | 620  | 1.0653          | 0.64     | 0.6520    | 0.64   | 0.6414 |
| 4.771         | 3.0   | 930  | 1.1236          | 0.6536   | 0.6613    | 0.6536 | 0.6553 |


### Framework versions

- PEFT 0.17.1
- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1