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---
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
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