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---
license: gemma
base_model: google/gemma-2b
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: gemma-2b-coedit
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. -->
# gemma-2b-coedit
This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7456
- Rouge1: 0.5006
- Rouge2: 0.3991
- Rougel: 0.4788
- Rougelsum: 0.4786
- Sacreblue: 20.7764
- Memory Used: 79283.5
- Cuda Allocated: 9625.1006
- Cuda Reserved: 73102.0
- Ram Usage: 10024.6953
- Em: 0.0
- Gen Len: 101.5333
## 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: 35
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 140
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Sacreblue | Memory Used | Cuda Allocated | Cuda Reserved | Ram Usage | Em | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:---------:|:-----------:|:--------------:|:-------------:|:----------:|:---:|:--------:|
| 0.5426 | 0.22 | 100 | 0.7076 | 0.3807 | 0.297 | 0.3623 | 0.3621 | 18.8513 | 69159.5 | 9625.1431 | 62980.0 | 5073.7852 | 0.0 | 101.5333 |
| 0.5051 | 0.44 | 200 | 0.6849 | 0.4094 | 0.3207 | 0.3907 | 0.3905 | 21.1175 | 67317.5 | 9625.1196 | 61138.0 | 5067.1328 | 0.0 | 101.5333 |
| 0.4909 | 0.66 | 300 | 0.6735 | 0.4943 | 0.3926 | 0.473 | 0.4729 | 11.0979 | 67319.5 | 9625.1182 | 61138.0 | 9820.3711 | 0.0 | 101.5333 |
| 0.4804 | 0.88 | 400 | 0.6672 | 0.4995 | 0.4004 | 0.4796 | 0.4795 | 24.1464 | 67319.5 | 9625.1079 | 61138.0 | 9803.6172 | 0.0 | 101.5333 |
| 0.2842 | 1.1 | 500 | 0.7475 | 0.5011 | 0.3995 | 0.4792 | 0.4792 | 27.3521 | 79283.5 | 9625.0977 | 73102.0 | 9845.9766 | 0.0 | 101.5333 |
| 0.2471 | 1.32 | 600 | 0.7447 | 0.4908 | 0.3906 | 0.4694 | 0.4693 | 24.0058 | 79283.5 | 9625.1123 | 73102.0 | 9916.7539 | 0.0 | 101.5333 |
| 0.2422 | 1.54 | 700 | 0.7361 | 0.4967 | 0.3954 | 0.4749 | 0.4749 | 21.4519 | 79283.5 | 9625.1196 | 73102.0 | 9910.2695 | 0.0 | 101.5333 |
| 0.2354 | 1.76 | 800 | 0.7443 | 0.4882 | 0.3882 | 0.467 | 0.4669 | 19.4531 | 79283.5 | 9625.124 | 73102.0 | 10050.582 | 0.0 | 101.5333 |
| 0.2334 | 1.98 | 900 | 0.7456 | 0.5006 | 0.3991 | 0.4788 | 0.4786 | 20.7764 | 79283.5 | 9625.1006 | 73102.0 | 10024.6953 | 0.0 | 101.5333 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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