---
license:
- apache-2.0
- cc-by-nc-4.0
datasets: pszemraj/fleece2instructions-codealpaca
tags:
- generated_from_trainer
- instruct
- instructions
- code
metrics:
- rouge
language:
- en
inference:
  parameters:
    max_length: 128
    num_beams: 4
---


# bart-large-code-instructiongen

Use this text2text model to find out what LLM instructions might be able to generate an arbitary piece of code!

This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co./facebook/bart-large) on the `pszemraj/fleece2instructions-codealpaca` dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9222
- Rouge1: 62.0692
- Rouge2: 36.1947
- Rougel: 57.5128
- Rougelsum: 58.6613
- Gen Len: 31.0060


## Intended uses & limitations

🚨 **note:** as the authors elected to release the [original dataset](https://github.com/sahil280114/codealpaca) under `cc-by-nc`, the license carries over to this model and **cannot be used for commercial activity**. 

Intended use: Research on domain adaptation and/or other improvements to LLMs by extending instruction:text data pairs.

## Training and evaluation data

Refer to the linked dataset card for `pszemraj/fleece2instructions-codealpaca` or the [original dataset](https://github.com/sahil280114/codealpaca) repo.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.0914        | 1.0   | 563  | 1.0303          | 60.288  | 34.1884 | 55.9293 | 57.0714   | 30.6267 |
| 0.8688        | 2.0   | 1126 | 0.9333          | 61.0409 | 34.9823 | 56.4887 | 57.6662   | 31.7255 |
| 0.6773        | 3.0   | 1689 | 0.9222          | 62.0692 | 36.1947 | 57.5128 | 58.6613   | 31.0060 |