---
license: other
base_model: Artigenz/Artigenz-Coder-DS-6.7B
tags:
- generated_from_trainer
metrics:
- accuracy
- bleu
- sacrebleu
- rouge
model-index:
- name: Artigenz-Coder-DS-6.7B_Fi__components_size_252_epochs_10_2024-06-21_09-34-51_3556543
  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__components_size_252_epochs_10_2024-06-21_09-34-51_3556543

This model is a fine-tuned version of [Artigenz/Artigenz-Coder-DS-6.7B](https://huggingface.co/Artigenz/Artigenz-Coder-DS-6.7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6318
- Accuracy: 0.48
- Chrf: 0.022
- Bleu: 0.0
- Sacrebleu: 0.0
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0
- Meteor: 0.099

## 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: 252
- training_steps: 2520

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Chrf  | Bleu  | Sacrebleu | Rouge1 | Rouge2 | Rougel | Rougelsum | Meteor |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|:-----:|:---------:|:------:|:------:|:------:|:---------:|:------:|
| 0.0255        | 4.0   | 252  | 1.2068          | 0.468    | 0.574 | 0.429 | 0.4       | 0.507  | 0.325  | 0.458  | 0.504     | 0.56   |
| 0.0989        | 8.0   | 504  | 3.8823          | 0.479    | 0.026 | 0.0   | 0.0       | 0.0    | 0.0    | 0.0    | 0.0       | 0.143  |
| 0.1071        | 12.0  | 756  | 3.8516          | 0.477    | 0.036 | 0.036 | 0.0       | 0.158  | 0.079  | 0.155  | 0.158     | 0.135  |
| 1.382         | 16.0  | 1008 | 3.7440          | 0.485    | 0.046 | 0.016 | 0.0       | 0.159  | 0.094  | 0.159  | 0.159     | 0.16   |
| 0.2463        | 20.0  | 1260 | 3.8049          | 0.48     | 0.04  | 0.0   | 0.0       | 0.066  | 0.033  | 0.066  | 0.066     | 0.139  |
| 0.6094        | 24.0  | 1512 | 3.9803          | 0.446    | 0.021 | 0.0   | 0.0       | 0.0    | 0.0    | 0.0    | 0.0       | 0.069  |
| 0.0514        | 28.0  | 1764 | 3.7417          | 0.48     | 0.023 | 0.0   | 0.0       | 0.0    | 0.0    | 0.0    | 0.0       | 0.104  |
| 0.2303        | 32.0  | 2016 | 3.6727          | 0.48     | 0.015 | 0.0   | 0.0       | 0.0    | 0.0    | 0.0    | 0.0       | 0.033  |
| 0.064         | 36.0  | 2268 | 3.6537          | 0.48     | 0.02  | 0.0   | 0.0       | 0.0    | 0.0    | 0.0    | 0.0       | 0.084  |
| 0.0697        | 40.0  | 2520 | 3.6318          | 0.48     | 0.022 | 0.0   | 0.0       | 0.0    | 0.0    | 0.0    | 0.0       | 0.099  |


### Framework versions

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