---
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