# Dataset Card for "alpaca-gpt4"

This dataset contains English Instruction-Following generated by GPT-4 using Alpaca prompts for fine-tuning LLMs.

The dataset was originaly shared in this repository: https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM. This is just a wraper for compatibility with huggingface's datasets library.

## Dataset Description

- **Homepage:** https://instruction-tuning-with-gpt-4.github.io
- **Repository:** https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM
- **Paper:** https://arxiv.org/abs/2304.03277

## Dataset structure

It contains 52K instruction-following data generated by GPT-4 using the same prompts as in Alpaca.
The dataset has the same format as Alpaca data, except the output is generated by GPT-4:

    - `instruction`: `str`, describes the task the model should perform. Each of the 52K instructions is unique.
    - `input`: `str`, optional context or input for the task. 
    - `output`: `str`, the answer to the instruction as generated by `GPT-4`.
    - `text`: `str`, all the previous fields concatenated together, plus the same prompt used in Alpaca at the beginnig.

## Difference with the original Alpaca dataset

The original Alpaca dataset used text-davinci-003 to complete the prompts. This dataset uses those same prompts, but generating the completions with GPT-4. Thus, in general, the responses are of higher quality and lenght. Here is an example:


#### Example from Alpaca-GPT4:

```bash
{'instruction': 'Identify the odd one out.',
 'input': 'Twitter, Instagram, Telegram',
 'output': 'The odd one out is Telegram. Twitter and Instagram are social media platforms mainly for sharing information, images and videos while Telegram is a cloud-based instant messaging and voice-over-IP service.',
 'text': 'Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\nIdentify the odd one out.\n\n### Input:\nTwitter, Instagram, Telegram\n\n### Response:\nThe odd one out is Telegram. Twitter and Instagram are social media platforms mainly for sharing information, images and videos while Telegram is a cloud-based instant messaging and voice-over-IP service.'}
```

#### Same example from original Alpaca:

```bash
{'instruction': 'Identify the odd one out.',
 'input': 'Twitter, Instagram, Telegram',
 'output': 'Telegram',
 'text': 'Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\nIdentify the odd one out.\n\n### Input:\nTwitter, Instagram, Telegram\n\n### Response:\nTelegram'}
```

## Licensing Information

The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode).
task_categories:text-generation task_categories:conversational task_categories:question-answering size_categories:10K<n<100K language:en license:cc-by-nc-4.0 gpt4 alpaca instruction-finetuning synthetic arxiv:2304.03277 region:us
{"language": ["en"], "license": "cc-by-nc-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation", "conversational", "question-answering"], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 88566301, "num_examples": 52002}], "download_size": 48393562, "dataset_size": 88566301}, "tags": ["gpt4", "alpaca", "instruction-finetuning", "synthetic"]}
TAGS
#task_categories-text-generation #task_categories-conversational #task_categories-question-answering #size_categories-10K<n<100K #language-English #license-cc-by-nc-4.0 #gpt4 #alpaca #instruction-finetuning #synthetic #arxiv-2304.03277 #region-us 

# Dataset Card for "alpaca-gpt4"

This dataset contains English Instruction-Following generated by GPT-4 using Alpaca prompts for fine-tuning LLMs.

The dataset was originaly shared in this repository: URL This is just a wraper for compatibility with huggingface's datasets library.

## Dataset Description

- Homepage: URL
- Repository: URL
- Paper: URL

## Dataset structure

It contains 52K instruction-following data generated by GPT-4 using the same prompts as in Alpaca.
The dataset has the same format as Alpaca data, except the output is generated by GPT-4:

    - 'instruction': 'str', describes the task the model should perform. Each of the 52K instructions is unique.
    - 'input': 'str', optional context or input for the task. 
    - 'output': 'str', the answer to the instruction as generated by 'GPT-4'.
    - 'text': 'str', all the previous fields concatenated together, plus the same prompt used in Alpaca at the beginnig.

## Difference with the original Alpaca dataset

The original Alpaca dataset used text-davinci-003 to complete the prompts. This dataset uses those same prompts, but generating the completions with GPT-4. Thus, in general, the responses are of higher quality and lenght. Here is an example:


#### Example from Alpaca-GPT4:



#### Same example from original Alpaca:



## Licensing Information

The dataset is available under the Creative Commons NonCommercial (CC BY-NC 4.0).
# Dataset Card for "alpaca-gpt4"

This dataset contains English Instruction-Following generated by GPT-4 using Alpaca prompts for fine-tuning LLMs.

The dataset was originaly shared in this repository: URL This is just a wraper for compatibility with huggingface's datasets library. ## Dataset Description

- Homepage: URL
- Repository: URL
- Paper: URL ## Dataset structure

It contains 52K instruction-following data generated by GPT-4 using the same prompts as in Alpaca.
The dataset has the same format as Alpaca data, except the output is generated by GPT-4:

    - 'instruction': 'str', describes the task the model should perform. Each of the 52K instructions is unique.
    - 'input': 'str', optional context or input for the task. 
    - 'output': 'str', the answer to the instruction as generated by 'GPT-4'.
    - 'text': 'str', all the previous fields concatenated together, plus the same prompt used in Alpaca at the beginnig. ## Difference with the original Alpaca dataset

The original Alpaca dataset used text-davinci-003 to complete the prompts. This dataset uses those same prompts, but generating the completions with GPT-4. Thus, in general, the responses are of higher quality and lenght. Here is an example: #### Example from Alpaca-GPT4: #### Same example from original Alpaca: ## Licensing Information

The dataset is available under the Creative Commons NonCommercial (CC BY-NC 4.0).
TAGS
#task_categories-text-generation #task_categories-conversational #task_categories-question-answering #size_categories-10K<n<100K #language-English #license-cc-by-nc-4.0 #gpt4 #alpaca #instruction-finetuning #synthetic #arxiv-2304.03277 #region-us 
 # Dataset Card for "alpaca-gpt4"

This dataset contains English Instruction-Following generated by GPT-4 using Alpaca prompts for fine-tuning LLMs.

The dataset was originaly shared in this repository: URL This is just a wraper for compatibility with huggingface's datasets library. ## Dataset Description

- Homepage: URL
- Repository: URL
- Paper: URL ## Dataset structure

It contains 52K instruction-following data generated by GPT-4 using the same prompts as in Alpaca.
The dataset has the same format as Alpaca data, except the output is generated by GPT-4:

    - 'instruction': 'str', describes the task the model should perform. Each of the 52K instructions is unique.
    - 'input': 'str', optional context or input for the task. 
    - 'output': 'str', the answer to the instruction as generated by 'GPT-4'.
    - 'text': 'str', all the previous fields concatenated together, plus the same prompt used in Alpaca at the beginnig. ## Difference with the original Alpaca dataset

The original Alpaca dataset used text-davinci-003 to complete the prompts. This dataset uses those same prompts, but generating the completions with GPT-4. Thus, in general, the responses are of higher quality and lenght. Here is an example: #### Example from Alpaca-GPT4: #### Same example from original Alpaca: ## Licensing Information

The dataset is available under the Creative Commons NonCommercial (CC BY-NC 4.0).
passage: TAGS
#task_categories-text-generation #task_categories-conversational #task_categories-question-answering #size_categories-10K<n<100K #language-English #license-cc-by-nc-4.0 #gpt4 #alpaca #instruction-finetuning #synthetic #arxiv-2304.03277 #region-us 
# Dataset Card for "alpaca-gpt4"

This dataset contains English Instruction-Following generated by GPT-4 using Alpaca prompts for fine-tuning LLMs.

The dataset was originaly shared in this repository: URL This is just a wraper for compatibility with huggingface's datasets library.## Dataset Description

- Homepage: URL
- Repository: URL
- Paper: URL## Dataset structure

It contains 52K instruction-following data generated by GPT-4 using the same prompts as in Alpaca.
The dataset has the same format as Alpaca data, except the output is generated by GPT-4:

    - 'instruction': 'str', describes the task the model should perform. Each of the 52K instructions is unique.
    - 'input': 'str', optional context or input for the task. 
    - 'output': 'str', the answer to the instruction as generated by 'GPT-4'.
    - 'text': 'str', all the previous fields concatenated together, plus the same prompt used in Alpaca at the beginnig.## Difference with the original Alpaca dataset

The original Alpaca dataset used text-davinci-003 to complete the prompts. This dataset uses those same prompts, but generating the completions with GPT-4. Thus, in general, the responses are of higher quality and lenght. Here is an example:#### Example from Alpaca-GPT4:#### Same example from original Alpaca:## Licensing Information

The dataset is available under the Creative Commons NonCommercial (CC BY-NC 4.0).