This model is a fine-tuned version of liminerity/Bitnet-Mistral.0.2-v3 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 4.4220

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: 16
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
training_steps: 1000

Training results

Training Loss	Epoch	Step	Validation Loss
4.6207	0.0226	25	4.5769
4.3648	0.0451	50	4.5716
4.4694	0.0677	75	4.5629
4.1782	0.0903	100	4.5555
4.4231	0.1128	125	4.5486
4.2572	0.1354	150	4.5546
4.3837	0.1580	175	4.5466
4.1915	0.1805	200	4.5419
4.3894	0.2031	225	4.5257
4.2447	0.2257	250	4.5235
4.0355	0.2483	275	4.5236
4.1873	0.2708	300	4.5211
4.3891	0.2934	325	4.5078
4.1322	0.3160	350	4.5019
4.0357	0.3385	375	4.5051
4.3401	0.3611	400	4.4921
4.3848	0.3837	425	4.4903
4.305	0.4062	450	4.4789
4.2776	0.4288	475	4.4765
4.1802	0.4514	500	4.4727
4.0785	0.4739	525	4.4674
4.0607	0.4965	550	4.4623
3.9385	0.5191	575	4.4611
4.194	0.5416	600	4.4565
4.277	0.5642	625	4.4478
4.1751	0.5868	650	4.4457
4.0422	0.6093	675	4.4428
4.1503	0.6319	700	4.4406
4.0552	0.6545	725	4.4366
4.4017	0.6770	750	4.4327
4.2394	0.6996	775	4.4300
4.1975	0.7222	800	4.4277
4.2378	0.7448	825	4.4279
4.078	0.7673	850	4.4256
4.4727	0.7899	875	4.4235
4.1667	0.8125	900	4.4224
4.4079	0.8350	925	4.4223
4.3179	0.8576	950	4.4221
4.0479	0.8802	975	4.4220
4.0943	0.9027	1000	4.4220

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1

liminerity/Bitnet-Mistral.0.2-v5

Bitnet-Mistral.0.2-v5

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

liminerity/
Bitnet-Mistral.0.2-v5