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svenbl80/
finetune_colpali_v1_2-german-4bit

Name: finetune_colpali_v1_2-german-4bit
Availability: InStock

transformers

safetensors

colpali

generated_from_trainer

vidore/colpaligemma-3b-pt-448-base

finetune

gemma

endpoints_compatible

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finetune_colpali_v1_2-german-4bit

This model is a fine-tuned version of 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

svenbl80/finetune_colpali_v1_2-german-4bit

finetune_colpali_v1_2-german-4bit

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

svenbl80/
finetune_colpali_v1_2-german-4bit