---
library_name: peft
license: llama3.1
base_model: meta-llama/Llama-3.1-8B-Instruct
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: llama-7b-offense
  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. -->

# llama-7b-offense

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: 0.5788
- Accuracy: 0.7709
- F1: 0.5386

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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_steps: 100
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| No log        | 0.9662 | 100  | 0.7477          | 0.6895   | 0.3890 |
| No log        | 1.9275 | 200  | 0.6635          | 0.7186   | 0.4739 |
| No log        | 2.8889 | 300  | 0.6167          | 0.7430   | 0.5328 |
| No log        | 3.8502 | 400  | 0.6212          | 0.7384   | 0.5399 |
| 2.5728        | 4.8116 | 500  | 0.5934          | 0.7558   | 0.5532 |
| 2.5728        | 5.7729 | 600  | 0.5906          | 0.7605   | 0.5231 |
| 2.5728        | 6.7343 | 700  | 0.5838          | 0.7698   | 0.505  |
| 2.5728        | 7.6957 | 800  | 0.5780          | 0.7605   | 0.5381 |
| 2.5728        | 8.6570 | 900  | 0.5799          | 0.7686   | 0.5340 |
| 1.9058        | 9.6184 | 1000 | 0.5788          | 0.7709   | 0.5386 |


### Framework versions

- PEFT 0.14.0
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0