---
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-ufo-4bit
  results: []
datasets:
- davanstrien/ufo-ColPali
---

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# finetune_colpali_v1_2-ufo-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 [davanstrien/ufo-ColPali](https://huggingface.co/datasets/davanstrien/ufo-ColPali) dataset.

The model was trained using the fine tuning [notebook](https://github.com/tonywu71/colpali-cookbooks/blob/main/examples/finetune_colpali.ipynb) from [tonywu71](https://huggingface.co/tonywu71). I changed almost nothing except the data processing steps. 

The dataset used for training was created using synthetic data from [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct). The process for making this dataset is discussed more in the [blog post](https://danielvanstrien.xyz/posts/post-with-code/colpali/2024-09-23-generate_colpali_dataset.html).



The model achieves the following results on the evaluation set:
- Loss: 0.1064
- Model Preparation Time: 0.0056

## Model description

This model is a fine tune of a ColPali [vidore/colpaligemma-3b-pt-448-base](https://huggingface.co/vidore/colpaligemma-3b-pt-448-base):

> ColPali is a model based on a novel model architecture and training strategy based on Vision Language Models (VLMs) to efficiently index documents from their visual features. It is a PaliGemma-3B extension that generates ColBERT- style multi-vector representations of text and images. It was introduced in the paper [ColPali: Efficient Document Retrieval with Vision Language Models](https://huggingface.co/papers/2407.01449). 

## Intended uses & limitations

For retrieving UFO newsletters documents. 

## Training and evaluation data

The training data was created via the following steps:

- Downloading a sample of UFO newsletters from this Internet archive [Collection](https://archive.org/details/ufonewsletters).
- Using the [pdf-to-page-images-dataset](https://huggingface.co/spaces/Dataset-Creation-Tools/pdf-to-page-images-dataset) Space to convert the PDF documents into a single page image dataset
- Use a VLM to generate synthetic queries for these documents using the approach outlines [here](https://danielvanstrien.xyz/posts/post-with-code/colpali/2024-09-23-generate_colpali_dataset.html). This results in [davanstrien/ufo-ColPali](https://huggingface.co/datasets/davanstrien/ufo-ColPali).
- Train the model using the fine tuning [notebook](https://github.com/tonywu71/colpali-cookbooks/blob/main/examples/finetune_colpali.ipynb) from [tonywu71](https://huggingface.co/tonywu71). I changed almost nothing except the data processing steps. 

## 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: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 1.5

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Model Preparation Time |
|:-------------:|:------:|:----:|:---------------:|:----------------------:|
| No log        | 0.0041 | 1    | 0.1879          | 0.0056                 |
| 0.1193        | 0.4090 | 100  | 0.1136          | 0.0056                 |
| 0.1287        | 0.8180 | 200  | 0.1122          | 0.0056                 |
| 0.0662        | 1.2270 | 300  | 0.1063          | 0.0056                 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1