---
license: gemma
base_model: google/gemma-2-2b
tags:
- easylm
- alignment-handbook
- trl
- sft
- generated_from_trainer
- trl
- sft
- generated_from_trainer
datasets:
- ultrafeedback-sft
model-index:
- name: easylm-ultrafeedback-sft-gemma-2-2b
  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. -->

# easylm-ultrafeedback-sft-gemma-2-2b

This model is a fine-tuned version of [google/gemma-2-2b](https://huggingface.co/google/gemma-2-2b) on the ultrafeedback-sft dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2897

## 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: 3e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 2
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step  | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 1.5578        | 0.0371 | 500   | 1.4651          |
| 1.4645        | 0.0742 | 1000  | 1.4362          |
| 1.4198        | 0.1113 | 1500  | 1.4196          |
| 1.3469        | 0.1484 | 2000  | 1.4051          |
| 1.3816        | 0.1855 | 2500  | 1.3920          |
| 1.3653        | 0.2226 | 3000  | 1.3809          |
| 1.4087        | 0.2596 | 3500  | 1.3715          |
| 1.2973        | 0.2967 | 4000  | 1.3615          |
| 1.348         | 0.3338 | 4500  | 1.3545          |
| 1.4639        | 0.3709 | 5000  | 1.3480          |
| 1.4405        | 0.4080 | 5500  | 1.3408          |
| 1.2926        | 0.4451 | 6000  | 1.3349          |
| 1.3452        | 0.4822 | 6500  | 1.3268          |
| 1.3076        | 0.5193 | 7000  | 1.3202          |
| 1.2696        | 0.5564 | 7500  | 1.3154          |
| 1.3833        | 0.5935 | 8000  | 1.3104          |
| 1.3217        | 0.6306 | 8500  | 1.3060          |
| 1.2351        | 0.6677 | 9000  | 1.3026          |
| 1.5295        | 0.7047 | 9500  | 1.2990          |
| 1.293         | 0.7418 | 10000 | 1.2967          |
| 1.2231        | 0.7789 | 10500 | 1.2942          |
| 1.2721        | 0.8160 | 11000 | 1.2926          |
| 1.3877        | 0.8531 | 11500 | 1.2913          |
| 1.2929        | 0.8902 | 12000 | 1.2903          |
| 1.4017        | 0.9273 | 12500 | 1.2900          |
| 1.2126        | 0.9644 | 13000 | 1.2897          |


### Framework versions

- Transformers 4.43.3
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1