---
license: llama3
base_model: ajibawa-2023/Code-Llama-3-8B
tags:
- generated_from_trainer
model-index:
- name: Code-Llama-3-8B-finetuned-py-to-cpp
  results: []
pipeline_tag: text-generation
library_name: transformers
---

<!-- 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. -->

# Code-Llama-3-8B-finetuned-py-to-cpp

This model is a fine-tuned version of [ajibawa-2023/Code-Llama-3-8B](https://huggingface.co/ajibawa-2023/Code-Llama-3-8B) on the [XLCoST](https://github.com/reddy-lab-code-research/XLCoST) (Python-C++) dataset, restricted to code snippets of <= 128 tokens long. 
It achieves the following results on the evaluation set:
- Loss: 0.4550

Test set:
- BLEU: 38.48
- COMET: 79.28
- CodeBLEU: 64.11
  - N-gram match score: 40.15
  - Weighted n-gram match score: 77.11
  - Syntax match score: 67.02
  - Dataflow match score: 72.13

## 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: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 0.99  | 87   | 0.5468          |
| No log        | 2.0   | 175  | 0.4626          |
| No log        | 2.98  | 261  | 0.4550          |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.13.3

```