gLM2-1protonly-3epoch-finetune

This model is a fine-tuned version of tattabio/gLM2_650M on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.9921

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: 0.0001
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• total_train_batch_size: 8
• total_eval_batch_size: 8
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
1.2307	0.0529	500	1.2231
1.1913	0.1059	1000	1.1909
1.1672	0.1588	1500	1.1665
1.162	0.2118	2000	1.1493
1.1274	0.2647	2500	1.1367
1.1217	0.3177	3000	1.1252
1.1159	0.3706	3500	1.1151
1.108	0.4235	4000	1.1049
1.095	0.4765	4500	1.0972
1.0907	0.5294	5000	1.0898
1.0894	0.5824	5500	1.0849
1.0903	0.6353	6000	1.0794
1.076	0.6883	6500	1.0745
1.0719	0.7412	7000	1.0685
1.0765	0.7942	7500	1.0639
1.081	0.8471	8000	1.0602
1.0497	0.9000	8500	1.0567
1.0567	0.9530	9000	1.0524
1.0395	1.0059	9500	1.0508
1.0401	1.0589	10000	1.0482
1.0434	1.1118	10500	1.0449
1.0632	1.1648	11000	1.0433
1.0256	1.2177	11500	1.0386
1.0253	1.2706	12000	1.0370
1.0303	1.3236	12500	1.0339
1.028	1.3765	13000	1.0311
1.0076	1.4295	13500	1.0290
1.0303	1.4824	14000	1.0272
1.0277	1.5354	14500	1.0244
1.0268	1.5883	15000	1.0222
1.0074	1.6413	15500	1.0207
1.0211	1.6942	16000	1.0196
1.0178	1.7471	16500	1.0176
1.0068	1.8001	17000	1.0161
1.0169	1.8530	17500	1.0144
1.026	1.9060	18000	1.0126
0.997	1.9589	18500	1.0109
0.9979	2.0119	19000	1.0098
1.0105	2.0648	19500	1.0084
0.9903	2.1177	20000	1.0077
1.0188	2.1707	20500	1.0052
1.005	2.2236	21000	1.0040
0.9967	2.2766	21500	1.0036
0.9914	2.3295	22000	1.0014
0.9845	2.3825	22500	1.0010
0.9974	2.4354	23000	0.9999
0.9949	2.4884	23500	0.9987
0.9829	2.5413	24000	0.9971
0.9882	2.5942	24500	0.9968
0.9931	2.6472	25000	0.9955
0.9964	2.7001	25500	0.9944
0.9975	2.7531	26000	0.9939
0.9824	2.8060	26500	0.9938
0.9752	2.8590	27000	0.9928
0.9757	2.9119	27500	0.9931
0.9694	2.9648	28000	0.9921

Framework versions

• Transformers 4.51.3
• Pytorch 2.7.0+cu126
• Datasets 3.5.1
• Tokenizers 0.21.1