learning_rate: 0.0005
train_batch_size: 32
eval_batch_size: 64
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Mae	Mse	Rmse
0.0005	1.0	55	0.0003	0.0008	0.0114	0.0003	0.0183
0.0003	2.0	110	0.0005	0.0008	0.0128	0.0005	0.0215
0.0004	3.0	165	0.0003	0.0008	0.0110	0.0003	0.0181
0.0004	4.0	220	0.0003	0.0008	0.0108	0.0003	0.0179
0.0003	5.0	275	0.0004	0.0008	0.0129	0.0004	0.0204
0.0004	6.0	330	0.0005	0.0008	0.0129	0.0005	0.0213
0.0003	7.0	385	0.0004	0.0008	0.0116	0.0004	0.0195
0.0003	8.0	440	0.0003	0.0008	0.0111	0.0003	0.0184
0.0005	9.0	495	0.0004	0.0008	0.0127	0.0004	0.0205
0.0003	10.0	550	0.0003	0.0008	0.0110	0.0003	0.0184
0.0004	11.0	605	0.0004	0.0008	0.0114	0.0004	0.0189
0.0004	12.0	660	0.0004	0.0008	0.0119	0.0004	0.0196
0.0004	13.0	715	0.0003	0.0008	0.0111	0.0003	0.0184
0.0003	14.0	770	0.0004	0.0008	0.0114	0.0004	0.0188
0.0004	15.0	825	0.0004	0.0008	0.0118	0.0004	0.0195
0.0004	16.0	880	0.0005	0.0008	0.0131	0.0005	0.0217
0.0003	17.0	935	0.0004	0.0008	0.0116	0.0004	0.0192
0.0003	18.0	990	0.0004	0.0008	0.0115	0.0004	0.0190
0.0003	19.0	1045	0.0004	0.0008	0.0117	0.0004	0.0192

Framework versions

Transformers 5.12.1
Pytorch 2.12.1+cu130
Datasets 5.0.0
Tokenizers 0.22.2

Generated by ML Intern

This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

Try ML Intern: https://smolagents-ml-intern.hf.space
Source code: https://github.com/huggingface/ml-intern

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = 'lvizcaya/patchtst-exchange-rate'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

For non-causal architectures, replace AutoModelForCausalLM with the appropriate AutoModel class.