tensorboard safetensors generated_from_trainer base_model:m-a-p/OpenCodeInterpreter-DS-6.7B base_model:finetune:m-a-p/OpenCodeInterpreter-DS-6.7B license:apache-2.0 region:us
---
base_model: m-a-p/OpenCodeInterpreter-DS-6.7B
license: apache-2.0
metrics:
- accuracy
- bleu
- sacrebleu
- rouge
tags:
- generated_from_trainer
model-index:
- name: OpenCodeInterpreter-DS-6.7B_Fi__translations_size_104_epochs_10_2024-06-21_23-19-55_3557637
  results: []
---

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# OpenCodeInterpreter-DS-6.7B_Fi__translations_size_104_epochs_10_2024-06-21_23-19-55_3557637

This model is a fine-tuned version of [m-a-p/OpenCodeInterpreter-DS-6.7B](https://huggingface.co/m-a-p/OpenCodeInterpreter-DS-6.7B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0113
- Accuracy: 0.03
- Chrf: 0.519
- Bleu: 0.428
- Sacrebleu: 0.4
- Rouge1: 0.51
- Rouge2: 0.267
- Rougel: 0.473
- Rougelsum: 0.504
- Meteor: 0.393

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|:-----:|:---------:|:------:|:------:|:------:|:---------:|:------:|
| 0.1566        | 4.0   | 104  | 1.1892          | 0.011    | 0.724 | 0.584 | 0.6       | 0.662  | 0.439  | 0.599  | 0.654     | 0.493  |
| 0.1145        | 8.0   | 208  | 1.3290          | 0.015    | 0.697 | 0.556 | 0.6       | 0.643  | 0.393  | 0.569  | 0.635     | 0.515  |
| 0.143         | 12.0  | 312  | 1.4376          | 0.016    | 0.686 | 0.577 | 0.6       | 0.673  | 0.453  | 0.609  | 0.669     | 0.516  |
| 0.6387        | 16.0  | 416  | 1.6801          | 0.018    | 0.723 | 0.599 | 0.6       | 0.69   | 0.49   | 0.634  | 0.684     | 0.508  |
| 0.1887        | 20.0  | 520  | 2.0277          | 0.023    | 0.674 | 0.551 | 0.6       | 0.663  | 0.441  | 0.595  | 0.66      | 0.48   |
| 0.2298        | 24.0  | 624  | 2.3972          | 0.028    | 0.621 | 0.512 | 0.5       | 0.621  | 0.397  | 0.562  | 0.617     | 0.452  |
| 1.0534        | 28.0  | 728  | 2.7032          | 0.024    | 0.578 | 0.479 | 0.5       | 0.577  | 0.344  | 0.53   | 0.565     | 0.427  |
| 0.2948        | 32.0  | 832  | 2.9091          | 0.031    | 0.545 | 0.452 | 0.5       | 0.54   | 0.302  | 0.498  | 0.535     | 0.415  |
| 0.3843        | 36.0  | 936  | 3.0397          | 0.03     | 0.498 | 0.407 | 0.4       | 0.498  | 0.248  | 0.456  | 0.493     | 0.388  |
| 0.3182        | 40.0  | 1040 | 3.0113          | 0.03     | 0.519 | 0.428 | 0.4       | 0.51   | 0.267  | 0.473  | 0.504     | 0.393  |


### Framework versions

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