tensorboard safetensors generated_from_trainer base_model:Artigenz/Artigenz-Coder-DS-6.7B base_model:finetune:Artigenz/Artigenz-Coder-DS-6.7B license:other region:us
---
base_model: Artigenz/Artigenz-Coder-DS-6.7B
license: other
metrics:
- accuracy
- bleu
- sacrebleu
- rouge
tags:
- generated_from_trainer
model-index:
- name: Artigenz-Coder-DS-6.7B_Fi__translations_size_104_epochs_10_2024-06-21_23-22-47_3557638
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Artigenz-Coder-DS-6.7B_Fi__translations_size_104_epochs_10_2024-06-21_23-22-47_3557638

This model is a fine-tuned version of [Artigenz/Artigenz-Coder-DS-6.7B](https://huggingface.co/Artigenz/Artigenz-Coder-DS-6.7B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9722
- Accuracy: 0.038
- Chrf: 0.522
- Bleu: 0.437
- Sacrebleu: 0.4
- Rouge1: 0.515
- Rouge2: 0.259
- Rougel: 0.472
- Rougelsum: 0.507
- Meteor: 0.41

## 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: 0.001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 3407
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 4
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 104
- training_steps: 1040

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Chrf  | Bleu  | Sacrebleu | Rouge1 | Rouge2 | Rougel | Rougelsum | Meteor |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|:-----:|:---------:|:------:|:------:|:------:|:---------:|:------:|
| 0.1488        | 4.0   | 104  | 1.1476          | 0.024    | 0.727 | 0.608 | 0.6       | 0.69   | 0.485  | 0.629  | 0.684     | 0.537  |
| 0.0978        | 8.0   | 208  | 1.1940          | 0.014    | 0.721 | 0.6   | 0.6       | 0.675  | 0.466  | 0.607  | 0.668     | 0.53   |
| 0.1307        | 12.0  | 312  | 1.3964          | 0.017    | 0.708 | 0.596 | 0.6       | 0.677  | 0.464  | 0.612  | 0.668     | 0.532  |
| 0.5772        | 16.0  | 416  | 1.6675          | 0.02     | 0.71  | 0.62  | 0.6       | 0.697  | 0.5    | 0.635  | 0.691     | 0.529  |
| 0.1787        | 20.0  | 520  | 2.0574          | 0.043    | 0.664 | 0.556 | 0.6       | 0.65   | 0.429  | 0.586  | 0.642     | 0.493  |
| 0.2195        | 24.0  | 624  | 2.4599          | 0.038    | 0.624 | 0.533 | 0.5       | 0.623  | 0.395  | 0.57   | 0.616     | 0.461  |
| 1.0463        | 28.0  | 728  | 2.7031          | 0.029    | 0.564 | 0.468 | 0.5       | 0.569  | 0.33   | 0.513  | 0.559     | 0.436  |
| 0.2775        | 32.0  | 832  | 2.8662          | 0.038    | 0.551 | 0.469 | 0.5       | 0.56   | 0.303  | 0.504  | 0.549     | 0.422  |
| 0.3616        | 36.0  | 936  | 2.9521          | 0.037    | 0.535 | 0.446 | 0.4       | 0.524  | 0.27   | 0.479  | 0.516     | 0.415  |
| 0.3042        | 40.0  | 1040 | 2.9722          | 0.038    | 0.522 | 0.437 | 0.4       | 0.515  | 0.259  | 0.472  | 0.507     | 0.41   |


### Framework versions

- Transformers 4.37.0
- Pytorch 2.2.1+cu121
- Datasets 2.20.0
- Tokenizers 0.15.2
