
## Evolved codealpaca

Updates:

* 2023/08/26 - Filtered results now only contain pure english instruction and removed any mentioned of trained by OAI response

Median sequence length : 471

We employed a methodology similar to that of [WizardCoder](https://huggingface.co/WizardLM/WizardCoder-15B-V1.0), with the exception that ours is open-source. We used the gpt-4-0314 and gpt-4-0613 models to augment and answer each response, with the bulk of generation handled by gpt-4-0314.

The aim of this dataset is twofold: firstly, to facilitate the recreation of other wizardcoder models using newer pretrained models, such as LLaMA-2; and secondly, to serve as a testing ground for the [evol-dataset](https://github.com/theblackcat102/evol-dataset) package, as we strive to develop improved future augmentation strategies.

We used a total of [10 strategies](https://github.com/theblackcat102/evol-dataset/tree/main/evolinstruct/instructions) to augment the [HuggingFaceH4/CodeAlpaca_20K](https://huggingface.co/datasets/HuggingFaceH4/CodeAlpaca_20K) dataset and create our own.

It's important to note that we introduced a new "language" augmentation strategy in this project, which enables the conversion of existing instructions into Chinese.

A Chinese code evol version is now available here : [theblackcat102/evol-code-zh](https://huggingface.co/datasets/theblackcat102/evol-code-zh)


## Comparison to existing dataset

Comparing to [nickrosh/Evol-Instruct-Code-80k-v1](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1), evol-codealpaca-v1 has longer instruction and output conversation

![](./48f1b380-dc0b-4b0b-9b97-3cc5aa619655.png)


## Datasets which uses /evol-codealpaca-v1

[argilla/ultrafeedback-binarized-preferences-cleaned](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned)

[ise-uiuc/Magicoder-Evol-Instruct-110K](https://huggingface.co/datasets/ise-uiuc/Magicoder-Evol-Instruct-110K)


### Citation

If you use this dataset to finetune any LLMs just cite wizard coder

```
@misc{luo2023wizardcoder,
      title={WizardCoder: Empowering Code Large Language Models with Evol-Instruct}, 
      author={Ziyang Luo and Can Xu and Pu Zhao and Qingfeng Sun and Xiubo Geng and Wenxiang Hu and Chongyang Tao and Jing Ma and Qingwei Lin and Daxin Jiang},
      year={2023},
}

```
task_categories:text-generation size_categories:100K<n<1M language:en license:apache-2.0 code region:us
{"language": ["en"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation"], "tags": ["code"]}
TAGS
#task_categories-text-generation #size_categories-100K<n<1M #language-English #license-apache-2.0 #code #region-us 


## Evolved codealpaca

Updates:

* 2023/08/26 - Filtered results now only contain pure english instruction and removed any mentioned of trained by OAI response

Median sequence length : 471

We employed a methodology similar to that of WizardCoder, with the exception that ours is open-source. We used the gpt-4-0314 and gpt-4-0613 models to augment and answer each response, with the bulk of generation handled by gpt-4-0314.

The aim of this dataset is twofold: firstly, to facilitate the recreation of other wizardcoder models using newer pretrained models, such as LLaMA-2; and secondly, to serve as a testing ground for the evol-dataset package, as we strive to develop improved future augmentation strategies.

We used a total of 10 strategies to augment the HuggingFaceH4/CodeAlpaca_20K dataset and create our own.

It's important to note that we introduced a new "language" augmentation strategy in this project, which enables the conversion of existing instructions into Chinese.

A Chinese code evol version is now available here : theblackcat102/evol-code-zh


## Comparison to existing dataset

Comparing to nickrosh/Evol-Instruct-Code-80k-v1, evol-codealpaca-v1 has longer instruction and output conversation

![](./URL)


## Datasets which uses /evol-codealpaca-v1

argilla/ultrafeedback-binarized-preferences-cleaned

ise-uiuc/Magicoder-Evol-Instruct-110K


If you use this dataset to finetune any LLMs just cite wizard coder


## Evolved codealpaca

Updates:

* 2023/08/26 - Filtered results now only contain pure english instruction and removed any mentioned of trained by OAI response

Median sequence length : 471

We employed a methodology similar to that of WizardCoder, with the exception that ours is open-source. We used the gpt-4-0314 and gpt-4-0613 models to augment and answer each response, with the bulk of generation handled by gpt-4-0314.

The aim of this dataset is twofold: firstly, to facilitate the recreation of other wizardcoder models using newer pretrained models, such as LLaMA-2; and secondly, to serve as a testing ground for the evol-dataset package, as we strive to develop improved future augmentation strategies.

We used a total of 10 strategies to augment the HuggingFaceH4/CodeAlpaca_20K dataset and create our own.

It's important to note that we introduced a new "language" augmentation strategy in this project, which enables the conversion of existing instructions into Chinese.

A Chinese code evol version is now available here : theblackcat102/evol-code-zh ## Comparison to existing dataset

Comparing to nickrosh/Evol-Instruct-Code-80k-v1, evol-codealpaca-v1 has longer instruction and output conversation

![](./URL) ## Datasets which uses /evol-codealpaca-v1

argilla/ultrafeedback-binarized-preferences-cleaned

ise-uiuc/Magicoder-Evol-Instruct-110K


If you use this dataset to finetune any LLMs just cite wizard coder
TAGS
#task_categories-text-generation #size_categories-100K<n<1M #language-English #license-apache-2.0 #code #region-us 
 ## Evolved codealpaca

Updates:

* 2023/08/26 - Filtered results now only contain pure english instruction and removed any mentioned of trained by OAI response

Median sequence length : 471

We employed a methodology similar to that of WizardCoder, with the exception that ours is open-source. We used the gpt-4-0314 and gpt-4-0613 models to augment and answer each response, with the bulk of generation handled by gpt-4-0314.

The aim of this dataset is twofold: firstly, to facilitate the recreation of other wizardcoder models using newer pretrained models, such as LLaMA-2; and secondly, to serve as a testing ground for the evol-dataset package, as we strive to develop improved future augmentation strategies.

We used a total of 10 strategies to augment the HuggingFaceH4/CodeAlpaca_20K dataset and create our own.

It's important to note that we introduced a new "language" augmentation strategy in this project, which enables the conversion of existing instructions into Chinese.

A Chinese code evol version is now available here : theblackcat102/evol-code-zh ## Comparison to existing dataset

Comparing to nickrosh/Evol-Instruct-Code-80k-v1, evol-codealpaca-v1 has longer instruction and output conversation

![](./URL) ## Datasets which uses /evol-codealpaca-v1

argilla/ultrafeedback-binarized-preferences-cleaned

ise-uiuc/Magicoder-Evol-Instruct-110K


If you use this dataset to finetune any LLMs just cite wizard coder
passage: TAGS
#task_categories-text-generation #size_categories-100K<n<1M #language-English #license-apache-2.0 #code #region-us 
## Evolved codealpaca

Updates:

* 2023/08/26 - Filtered results now only contain pure english instruction and removed any mentioned of trained by OAI response

Median sequence length : 471

We employed a methodology similar to that of WizardCoder, with the exception that ours is open-source. We used the gpt-4-0314 and gpt-4-0613 models to augment and answer each response, with the bulk of generation handled by gpt-4-0314.

The aim of this dataset is twofold: firstly, to facilitate the recreation of other wizardcoder models using newer pretrained models, such as LLaMA-2; and secondly, to serve as a testing ground for the evol-dataset package, as we strive to develop improved future augmentation strategies.

We used a total of 10 strategies to augment the HuggingFaceH4/CodeAlpaca_20K dataset and create our own.

It's important to note that we introduced a new "language" augmentation strategy in this project, which enables the conversion of existing instructions into Chinese.

A Chinese code evol version is now available here : theblackcat102/evol-code-zh## Comparison to existing dataset

Comparing to nickrosh/Evol-Instruct-Code-80k-v1, evol-codealpaca-v1 has longer instruction and output conversation

![](./URL)## Datasets which uses /evol-codealpaca-v1

argilla/ultrafeedback-binarized-preferences-cleaned

ise-uiuc/Magicoder-Evol-Instruct-110K


If you use this dataset to finetune any LLMs just cite wizard coder