tensorboard safetensors generated_from_trainer base_model:codellama/CodeLlama-13b-Instruct-hf base_model:finetune:codellama/CodeLlama-13b-Instruct-hf license:llama2 region:us
---
base_model: codellama/CodeLlama-13b-Instruct-hf
license: llama2
metrics:
- accuracy
- bleu
- sacrebleu
- rouge
tags:
- generated_from_trainer
model-index:
- name: CodeLlama-13b-Instruct-hf_Fi__translations_size_104_epochs_10_2024-06-22_02-50-05_3557641
  results: []
---

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# CodeLlama-13b-Instruct-hf_Fi__translations_size_104_epochs_10_2024-06-22_02-50-05_3557641

This model is a fine-tuned version of [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4179
- Accuracy: 0.035
- Chrf: 0.719
- Bleu: 0.636
- Sacrebleu: 0.6
- Rouge1: 0.678
- Rouge2: 0.497
- Rougel: 0.645
- Rougelsum: 0.675
- Meteor: 0.594

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|:-----:|:---------:|:------:|:------:|:------:|:---------:|:------:|
| 0.3439        | 4.0   | 104  | 2.0524          | 0.036    | 0.581 | 0.472 | 0.5       | 0.556  | 0.309  | 0.5    | 0.55      | 0.493  |
| 0.194         | 8.0   | 208  | 2.0784          | 0.036    | 0.55  | 0.435 | 0.4       | 0.519  | 0.275  | 0.479  | 0.513     | 0.466  |
| 0.2001        | 12.0  | 312  | 2.1078          | 0.061    | 0.564 | 0.456 | 0.5       | 0.544  | 0.291  | 0.504  | 0.538     | 0.502  |
| 0.5322        | 16.0  | 416  | 1.7052          | 0.037    | 0.64  | 0.527 | 0.5       | 0.6    | 0.366  | 0.563  | 0.598     | 0.524  |
| 0.1677        | 20.0  | 520  | 1.8442          | 0.037    | 0.606 | 0.493 | 0.5       | 0.566  | 0.334  | 0.529  | 0.557     | 0.496  |
| 0.1649        | 24.0  | 624  | 1.6364          | 0.037    | 0.66  | 0.558 | 0.6       | 0.627  | 0.408  | 0.593  | 0.621     | 0.535  |
| 0.6799        | 28.0  | 728  | 1.5108          | 0.034    | 0.692 | 0.597 | 0.6       | 0.647  | 0.452  | 0.608  | 0.644     | 0.541  |
| 0.1644        | 32.0  | 832  | 1.4941          | 0.035    | 0.709 | 0.623 | 0.6       | 0.671  | 0.478  | 0.634  | 0.667     | 0.553  |
| 0.2382        | 36.0  | 936  | 1.4313          | 0.035    | 0.715 | 0.633 | 0.6       | 0.681  | 0.494  | 0.645  | 0.679     | 0.556  |
| 0.1956        | 40.0  | 1040 | 1.4179          | 0.035    | 0.719 | 0.636 | 0.6       | 0.678  | 0.497  | 0.645  | 0.675     | 0.594  |


### Framework versions

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