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chanyongp/
colpali_finetuning

Name: colpali_finetuning
Availability: InStock

transformers

safetensors

colpali

generated_from_trainer

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

finetune

gemma

endpoints_compatible

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colpali_finetuning

This model is a fine-tuned version of vidore/colpaligemma-3b-pt-448-base on the vidore/colpali_train_set dataset. It achieves the following results on the evaluation set:

Loss: 0.0537
Model Preparation Time: 0.0062

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: 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	0.0001	1	0.0537	0.0062
0.0236	0.0135	100	0.0564	0.0062
0.0535	0.0271	200	0.0593	0.0062
0.0306	0.0406	300	0.0557	0.0062
0.034	0.0541	400	0.0695	0.0062
0.0326	0.0677	500	0.0445	0.0062
0.0465	0.0812	600	0.0527	0.0062
0.0344	0.0948	700	0.0533	0.0062
0.0634	0.1083	800	0.0438	0.0062
0.0474	0.1218	900	0.0455	0.0062
0.0383	0.1354	1000	0.0490	0.0062
0.0749	0.1489	1100	0.0460	0.0062
0.0507	0.1624	1200	0.0361	0.0062
0.05	0.1760	1300	0.0385	0.0062
0.0409	0.1895	1400	0.0451	0.0062
0.037	0.2031	1500	0.0427	0.0062
0.0593	0.2166	1600	0.0653	0.0062
0.035	0.2301	1700	0.0596	0.0062
0.0422	0.2437	1800	0.0471	0.0062
0.0356	0.2572	1900	0.0498	0.0062
0.0088	0.2707	2000	0.0608	0.0062
0.0227	0.2843	2100	0.0720	0.0062
0.0166	0.2978	2200	0.0734	0.0062
0.0348	0.3113	2300	0.0589	0.0062
0.0161	0.3249	2400	0.0596	0.0062
0.0316	0.3384	2500	0.0499	0.0062
0.0365	0.3520	2600	0.0543	0.0062
0.0304	0.3655	2700	0.0556	0.0062
0.0105	0.3790	2800	0.0545	0.0062
0.0136	0.3926	2900	0.0545	0.0062
0.015	0.4061	3000	0.0605	0.0062
0.0453	0.4196	3100	0.0611	0.0062
0.0364	0.4332	3200	0.0593	0.0062
0.0347	0.4467	3300	0.0579	0.0062
0.0225	0.4603	3400	0.0602	0.0062
0.0232	0.4738	3500	0.0505	0.0062
0.0318	0.4873	3600	0.0514	0.0062
0.0359	0.5009	3700	0.0542	0.0062
0.0366	0.5144	3800	0.0559	0.0062
0.0323	0.5279	3900	0.0584	0.0062
0.0205	0.5415	4000	0.0615	0.0062
0.0292	0.5550	4100	0.0527	0.0062
0.0458	0.5685	4200	0.0506	0.0062
0.0175	0.5821	4300	0.0503	0.0062
0.0215	0.5956	4400	0.0558	0.0062
0.0142	0.6092	4500	0.0568	0.0062
0.0312	0.6227	4600	0.0612	0.0062
0.0193	0.6362	4700	0.0630	0.0062
0.0077	0.6498	4800	0.0631	0.0062
0.0334	0.6633	4900	0.0708	0.0062
0.0247	0.6768	5000	0.0664	0.0062
0.0201	0.6904	5100	0.0546	0.0062
0.0402	0.7039	5200	0.0630	0.0062
0.0326	0.7175	5300	0.0651	0.0062
0.0127	0.7310	5400	0.0614	0.0062
0.0479	0.7445	5500	0.0599	0.0062
0.0344	0.7581	5600	0.0557	0.0062
0.016	0.7716	5700	0.0542	0.0062
0.0194	0.7851	5800	0.0546	0.0062
0.0236	0.7987	5900	0.0543	0.0062
0.0285	0.8122	6000	0.0577	0.0062
0.0128	0.8257	6100	0.0521	0.0062
0.0218	0.8393	6200	0.0539	0.0062
0.0501	0.8528	6300	0.0515	0.0062
0.0456	0.8664	6400	0.0508	0.0062
0.0247	0.8799	6500	0.0500	0.0062
0.035	0.8934	6600	0.0516	0.0062
0.0068	0.9070	6700	0.0502	0.0062
0.0257	0.9205	6800	0.0527	0.0062
0.0192	0.9340	6900	0.0513	0.0062
0.0334	0.9476	7000	0.0551	0.0062
0.0208	0.9611	7100	0.0544	0.0062
0.0668	0.9747	7200	0.0510	0.0062
0.0264	0.9882	7300	0.0481	0.0062
0.0641	1.0017	7400	0.0486	0.0062
0.0178	1.0153	7500	0.0473	0.0062
0.0206	1.0288	7600	0.0502	0.0062
0.0188	1.0423	7700	0.0537	0.0062
0.0378	1.0559	7800	0.0502	0.0062
0.0313	1.0694	7900	0.0571	0.0062
0.0169	1.0829	8000	0.0586	0.0062
0.0164	1.0965	8100	0.0580	0.0062
0.0327	1.1100	8200	0.0540	0.0062
0.0153	1.1236	8300	0.0507	0.0062
0.0305	1.1371	8400	0.0542	0.0062
0.0279	1.1506	8500	0.0532	0.0062
0.0081	1.1642	8600	0.0552	0.0062
0.027	1.1777	8700	0.0545	0.0062
0.0112	1.1912	8800	0.0551	0.0062
0.0312	1.2048	8900	0.0564	0.0062
0.0244	1.2183	9000	0.0538	0.0062
0.0274	1.2319	9100	0.0529	0.0062
0.0351	1.2454	9200	0.0531	0.0062
0.0172	1.2589	9300	0.0532	0.0062
0.005	1.2725	9400	0.0513	0.0062
0.0195	1.2860	9500	0.0549	0.0062
0.0062	1.2995	9600	0.0558	0.0062
0.0234	1.3131	9700	0.0558	0.0062
0.0157	1.3266	9800	0.0564	0.0062
0.0248	1.3401	9900	0.0556	0.0062
0.0098	1.3537	10000	0.0536	0.0062
0.0133	1.3672	10100	0.0525	0.0062
0.0187	1.3808	10200	0.0526	0.0062
0.0088	1.3943	10300	0.0509	0.0062
0.0315	1.4078	10400	0.0534	0.0062
0.0219	1.4214	10500	0.0537	0.0062
0.0225	1.4349	10600	0.0548	0.0062
0.0338	1.4484	10700	0.0545	0.0062
0.029	1.4620	10800	0.0539	0.0062
0.0354	1.4755	10900	0.0537	0.0062
0.0214	1.4891	11000	0.0536	0.0062

Framework versions

Transformers 4.45.2
Pytorch 2.4.1+cu121
Datasets 3.0.2
Tokenizers 0.20.1

chanyongp/colpali_finetuning

colpali_finetuning

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

chanyongp/
colpali_finetuning