---
library_name: transformers
license: gemma
base_model: vidore/colpaligemma-3b-pt-448-base
tags:
- colpali
- generated_from_trainer
model-index:
- name: finetune_colpali_v1_2-german-4bit
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# finetune_colpali_v1_2-german-4bit

This model is a fine-tuned version of [vidore/colpaligemma-3b-pt-448-base](https://huggingface.co/vidore/colpaligemma-3b-pt-448-base) on the German_docx dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1100
- Model Preparation Time: 0.008

## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Model Preparation Time |
|:-------------:|:------:|:----:|:---------------:|:----------------------:|
| No log        | 0.0533 | 1    | 0.3717          | 0.008                  |
| 1.1358        | 0.5333 | 10   | 0.3356          | 0.008                  |
| 1.2182        | 1.0667 | 20   | 0.2811          | 0.008                  |
| 0.844         | 1.6    | 30   | 0.2365          | 0.008                  |
| 0.7722        | 2.1333 | 40   | 0.1990          | 0.008                  |
| 0.4823        | 2.6667 | 50   | 0.1758          | 0.008                  |
| 0.46          | 3.2    | 60   | 0.1451          | 0.008                  |
| 0.1477        | 3.7333 | 70   | 0.1252          | 0.008                  |
| 0.1764        | 4.2667 | 80   | 0.1258          | 0.008                  |
| 0.2329        | 4.8    | 90   | 0.1100          | 0.008                  |


### Framework versions

- Transformers 4.46.1
- Pytorch 2.3.1
- Datasets 3.1.0
- Tokenizers 0.20.1