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---
library_name: transformers
license: gemma
base_model: vidore/colpaligemma-3b-pt-448-base
tags:
- colpali
- generated_from_trainer
pipeline_tag: visual-document-retrieval
model-index:
- name: finetune_colpali_v1_2-german_ver3-4bit
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. -->
# finetune_colpali_v1_2-german_ver3-4bit
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: 0.0815
- Model Preparation Time: 0.0061
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time |
|:-------------:|:------:|:----:|:---------------:|:----------------------:|
| No log | 0.0146 | 1 | 0.3622 | 0.0061 |
| 1.6392 | 0.1460 | 10 | 0.3430 | 0.0061 |
| 1.1999 | 0.2920 | 20 | 0.3049 | 0.0061 |
| 1.2826 | 0.4380 | 30 | 0.2768 | 0.0061 |
| 0.7583 | 0.5839 | 40 | 0.2492 | 0.0061 |
| 0.603 | 0.7299 | 50 | 0.2258 | 0.0061 |
| 1.014 | 0.8759 | 60 | 0.1958 | 0.0061 |
| 0.8131 | 1.0219 | 70 | 0.1688 | 0.0061 |
| 0.6346 | 1.1679 | 80 | 0.1591 | 0.0061 |
| 0.5089 | 1.3139 | 90 | 0.1502 | 0.0061 |
| 0.4616 | 1.4599 | 100 | 0.1341 | 0.0061 |
| 0.4498 | 1.6058 | 110 | 0.1136 | 0.0061 |
| 0.4422 | 1.7518 | 120 | 0.1062 | 0.0061 |
| 0.3519 | 1.8978 | 130 | 0.0989 | 0.0061 |
| 0.2382 | 2.0438 | 140 | 0.0925 | 0.0061 |
| 0.242 | 2.1898 | 150 | 0.0894 | 0.0061 |
| 0.3462 | 2.3358 | 160 | 0.0907 | 0.0061 |
| 0.1371 | 2.4818 | 170 | 0.0862 | 0.0061 |
| 0.2691 | 2.6277 | 180 | 0.0838 | 0.0061 |
| 0.0869 | 2.7737 | 190 | 0.0833 | 0.0061 |
| 0.3401 | 2.9197 | 200 | 0.0815 | 0.0061 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.3.1
- Datasets 3.1.0
- Tokenizers 0.20.1
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