---
library_name: transformers
license: llama3.2
base_model: meta-llama/Llama-3.2-3B
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
- trl
- sft
- generated_from_trainer
model-index:
- name: meta-llama/Llama-3.2-3B
  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. -->

# meta-llama/Llama-3.2-3B

This model is a fine-tuned version of [meta-llama/Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6898

## 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_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: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.7972        | 0.0275 | 200  | 0.8336          |
| 0.7579        | 0.0551 | 400  | 0.7996          |
| 0.8037        | 0.0826 | 600  | 0.7918          |
| 0.7333        | 0.1101 | 800  | 0.7879          |
| 0.7871        | 0.1376 | 1000 | 0.7818          |
| 0.8135        | 0.1652 | 1200 | 0.7736          |
| 0.7612        | 0.1927 | 1400 | 0.7699          |
| 0.7421        | 0.2202 | 1600 | 0.7643          |
| 0.7451        | 0.2478 | 1800 | 0.7595          |
| 0.7388        | 0.2753 | 2000 | 0.7556          |
| 0.7707        | 0.3028 | 2200 | 0.7523          |
| 0.7063        | 0.3303 | 2400 | 0.7481          |
| 0.8091        | 0.3579 | 2600 | 0.7440          |
| 0.764         | 0.3854 | 2800 | 0.7407          |
| 0.714         | 0.4129 | 3000 | 0.7370          |
| 0.6745        | 0.4405 | 3200 | 0.7339          |
| 0.6771        | 0.4680 | 3400 | 0.7295          |
| 0.7419        | 0.4955 | 3600 | 0.7257          |
| 0.71          | 0.5230 | 3800 | 0.7223          |
| 0.6362        | 0.5506 | 4000 | 0.7189          |
| 0.7616        | 0.5781 | 4200 | 0.7159          |
| 0.676         | 0.6056 | 4400 | 0.7126          |
| 0.6732        | 0.6332 | 4600 | 0.7094          |
| 0.7017        | 0.6607 | 4800 | 0.7067          |
| 0.6796        | 0.6882 | 5000 | 0.7038          |
| 0.7065        | 0.7157 | 5200 | 0.7012          |
| 0.6318        | 0.7433 | 5400 | 0.6987          |
| 0.639         | 0.7708 | 5600 | 0.6965          |
| 0.7078        | 0.7983 | 5800 | 0.6949          |
| 0.7029        | 0.8258 | 6000 | 0.6933          |
| 0.6977        | 0.8534 | 6200 | 0.6921          |
| 0.6803        | 0.8809 | 6400 | 0.6911          |
| 0.703         | 0.9084 | 6600 | 0.6905          |
| 0.6819        | 0.9360 | 6800 | 0.6901          |
| 0.6327        | 0.9635 | 7000 | 0.6899          |
| 0.6685        | 0.9910 | 7200 | 0.6899          |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.21.0