tensorboard safetensors generated_from_trainer base_model:codellama/CodeLlama-7b-Instruct-hf base_model:finetune:codellama/CodeLlama-7b-Instruct-hf license:llama2 region:us
---
base_model: codellama/CodeLlama-7b-Instruct-hf
license: llama2
metrics:
- accuracy
- bleu
- sacrebleu
- rouge
tags:
- generated_from_trainer
model-index:
- name: CodeLlama-7b-Instruct-hf_Fi__translations_size_104_epochs_10_2024-06-22_03-11-17_3557642
  results: []
---

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# CodeLlama-7b-Instruct-hf_Fi__translations_size_104_epochs_10_2024-06-22_03-11-17_3557642

This model is a fine-tuned version of [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3204
- Accuracy: 0.035
- Chrf: 0.736
- Bleu: 0.656
- Sacrebleu: 0.7
- Rouge1: 0.689
- Rouge2: 0.506
- Rougel: 0.657
- Rougelsum: 0.684
- Meteor: 0.613

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|:-----:|:---------:|:------:|:------:|:------:|:---------:|:------:|
| 0.2911        | 4.0   | 104  | 1.6937          | 0.042    | 0.623 | 0.509 | 0.5       | 0.586  | 0.333  | 0.536  | 0.584     | 0.513  |
| 0.1827        | 8.0   | 208  | 1.9669          | 0.04     | 0.584 | 0.47  | 0.5       | 0.532  | 0.309  | 0.495  | 0.528     | 0.48   |
| 0.1801        | 12.0  | 312  | 1.7206          | 0.039    | 0.621 | 0.53  | 0.5       | 0.582  | 0.353  | 0.55   | 0.579     | 0.538  |
| 0.5158        | 16.0  | 416  | 1.5969          | 0.038    | 0.654 | 0.536 | 0.5       | 0.602  | 0.368  | 0.565  | 0.599     | 0.52   |
| 0.1586        | 20.0  | 520  | 1.6966          | 0.037    | 0.632 | 0.522 | 0.5       | 0.576  | 0.357  | 0.544  | 0.571     | 0.513  |
| 0.1584        | 24.0  | 624  | 1.5539          | 0.037    | 0.694 | 0.597 | 0.6       | 0.651  | 0.447  | 0.61   | 0.643     | 0.564  |
| 0.6664        | 28.0  | 728  | 1.4467          | 0.035    | 0.701 | 0.611 | 0.6       | 0.655  | 0.452  | 0.616  | 0.651     | 0.574  |
| 0.171         | 32.0  | 832  | 1.6334          | 0.033    | 0.68  | 0.568 | 0.6       | 0.633  | 0.417  | 0.59   | 0.627     | 0.526  |
| 0.227         | 36.0  | 936  | 1.3442          | 0.035    | 0.727 | 0.64  | 0.6       | 0.682  | 0.491  | 0.645  | 0.679     | 0.577  |
| 0.1831        | 40.0  | 1040 | 1.3204          | 0.035    | 0.736 | 0.656 | 0.7       | 0.689  | 0.506  | 0.657  | 0.684     | 0.613  |


### Framework versions

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