ayousanz/mistral-0.3b-backup

checkpoints-mistral-300M

This model is a fine-tuned version of None on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 2.4867

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: 0.0003
• train_batch_size: 6
• eval_batch_size: 6
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• gradient_accumulation_steps: 16
• total_train_batch_size: 192
• total_eval_batch_size: 12
• optimizer: Adam with betas=(0.9,0.95) and epsilon=0.0001
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 4
• num_epochs: 6
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
4.5141	0.09	1000	4.5160
3.7879	0.18	2000	3.8531
3.5484	0.27	3000	3.5881
3.3734	0.36	4000	3.4287
3.2722	0.45	5000	3.3144
3.2276	0.54	6000	3.2299
3.1809	0.63	7000	3.1597
3.0706	0.72	8000	3.1043
3.0185	0.81	9000	3.0578
2.9496	0.9	10000	3.0157
2.9374	0.99	11000	2.9815
2.8794	1.08	12000	2.9487
2.8407	1.17	13000	2.9229
2.8818	1.26	14000	2.8973
2.8167	1.35	15000	2.8730
2.7941	1.44	16000	2.8515
2.7878	1.53	17000	2.8311
2.7894	1.62	18000	2.8113
2.7158	1.71	19000	2.7935
2.7409	1.8	20000	2.7765
2.7349	1.89	21000	2.7613
2.6631	1.98	22000	2.7451
2.6766	2.07	23000	2.7353
2.6405	2.16	24000	2.7231
2.6707	2.25	25000	2.7121
2.6362	2.34	26000	2.7005
2.5997	2.43	27000	2.6904
2.6549	2.52	28000	2.6798
2.6056	2.61	29000	2.6688
2.5722	2.7	30000	2.6594
2.6179	2.79	31000	2.6509
2.6064	2.88	32000	2.6423
2.5836	2.97	33000	2.6340
2.5502	3.06	34000	2.6285
2.5428	3.15	35000	2.6218
2.5342	3.24	36000	2.6160
2.5152	3.33	37000	2.6090
2.5138	5.13	38000	2.5766
2.5032	5.27	39000	2.5683
2.4783	5.4	40000	2.5609
2.4519	5.54	41000	2.5545
2.4918	5.67	42000	2.5472
2.4591	5.81	43000	2.5411
2.4756	5.94	44000	2.5354
2.4434	6.08	45000	2.5345
2.4312	6.21	46000	2.5301
2.4576	6.35	47000	2.5242
2.4343	6.48	48000	2.5192
2.426	6.62	49000	2.5139
2.4136	6.75	50000	2.5084
2.4463	6.89	51000	2.5037
2.345	7.02	52000	2.5016
2.3736	7.16	53000	2.4990
2.4092	7.29	54000	2.4955
2.3689	7.43	55000	2.4917
2.3797	7.56	56000	2.4867

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.2+cu121
• Datasets 2.14.5
• Tokenizers 0.14.1