| | --- |
| | license: bigcode-openrail-m |
| | base_model: bigcode/starcoder |
| | tags: |
| | - generated_from_trainer |
| | model-index: |
| | - name: SteloCoder |
| | results: [] |
| | --- |
| | |
| | # 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 |
| | |
