File size: 1,908 Bytes
4ba6f07 db55776 4ba6f07 962fce9 4ba6f07 a6a7cdf 4ba6f07 99be392 db55776 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 |
---
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
``` |