moe_training

This is the final stage of training SteloCoder - MoE (Mixture of Experts) training. The dataset contains samples of code translation with five programming languages to python. The training/validation/testing data is processed and is souced from XLCoST dataset.

Model description

The final model is named SteloCoder, a model designed for code machine translation from multiple languages (C++, C#, Java, JavaScript, PHP) to Python. It is based on StarCoder to which we have added additional parameters using LoRA and MoE methods.

Intended uses & limitations

More information needed

Training and evaluation data

The data is processed sourced from XLCoST dataset.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 50
training_steps: 1000

Training results

Training Loss	Epoch	Step	Validation Loss	Rate
0.1293	0.05	50	0.1218	5e-05
0.1332	0.1	100	0.1135	0.0000
0.1346	0.15	150	0.1117	0.0000
0.1336	0.2	200	0.1127	0.0000
0.1378	0.25	250	0.1116	0.0000
0.1321	0.3	300	0.1083	0.0000
0.1335	0.35	350	0.1075	0.0000
0.1316	0.4	400	0.1065	0.0000
0.1298	0.45	450	0.1062	0.0000
0.1331	0.5	500	0.1055	0.0000
0.1355	0.55	550	0.1048	0.0000
0.1299	0.6	600	0.1044	0.0000
0.1387	0.65	650	0.1048	0.0000
0.1278	0.7	700	0.1047	0.0000
0.1285	0.75	750	0.1045	0.0000
0.1278	0.8	800	0.1045	0.0000
0.1283	0.85	850	0.1045	0.0000
0.124	0.9	900	0.1043	0.0000
0.1258	0.95	950	0.1043	0.0000
0.1319	1.0	1000	0.1043	0.0

Framework versions

Transformers 4.32.1
Pytorch 2.0.1+cu117
Datasets 2.14.4
Tokenizers 0.13.3

jlpan
/

SteloCoder