---
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