# Download cleaned Alpaca dataset

The <mark style="color:yellow;">last instruction</mark> entered was to git clone the alpaca-cleaned dataset to the local directory:&#x20;

```bash
git clone https://huggingface.co/datasets/yahma/alpaca-cleaned
```

This command downloaded this Huggingface <mark style="color:yellow;">42MB json dataset</mark> into the directory you created called **datasets.**

**Within datasets, this directory is located at alpaca-cleaned.  The full path is:**

*<mark style="color:blue;">your primary directory</mark>*/<mark style="color:yellow;">axolotl</mark>/<mark style="color:purple;">datasets</mark>/<mark style="color:green;">alpaca-cleaned</mark>

The screenshot below shows the contents of the alpaca-cleaned dataset.  Note that it is in JSON format and that the training set is in Alpaca format:

<figure><img src="/files/BE3h4h65qb6ecRZeIMCs" alt=""><figcaption><p>A screenshot from VS Code demonstrating the contents of the alpaca-cleaned dataste</p></figcaption></figure>

### <mark style="color:blue;">What is Alpaca format?</mark>

When using <mark style="color:blue;">instruction fine tuning.</mark> there are various formats for the training set.  The Alpaca format has become one of the 'standards' for the structure of a dataset

#### <mark style="color:green;">**Data Structure in**</mark><mark style="color:green;">**&#x20;**</mark><mark style="color:green;">**`alpaca_data.json`**</mark>

This dataset is formatted as a <mark style="color:blue;">JSON file</mark>, where each entry is represented as a dictionary with the following key-value pairs:

**Instruction&#x20;**<mark style="color:yellow;">**(**</mark><mark style="color:yellow;">**`instruction`**</mark><mark style="color:yellow;">**)**</mark><mark style="color:yellow;">:</mark>

* Type: String <mark style="color:yellow;">(</mark><mark style="color:yellow;">`str`</mark><mark style="color:yellow;">)</mark>
* Description: Specifies the task to be performed by the model.

**Input (`input`)**:

* Type: String <mark style="color:yellow;">(</mark><mark style="color:yellow;">`str`</mark><mark style="color:yellow;">)</mark> optional.
* Description: Provides additional context or information needed to perform the task described in the <mark style="color:yellow;">`instruction`</mark><mark style="color:yellow;">.</mark>
* Example: If the instruction is "Summarize the following article", the input would be the text of the article.

Prevalence: In the original 52k Alpaca dataset, approximately <mark style="color:yellow;">40% of the entries</mark> in the dataset include an `input` field.

**Output&#x20;**<mark style="color:yellow;">**(**</mark><mark style="color:yellow;">**`output`**</mark><mark style="color:yellow;">**)**</mark><mark style="color:yellow;">:</mark>

* Type: String <mark style="color:yellow;">(</mark><mark style="color:yellow;">`str`</mark><mark style="color:yellow;">)</mark>
* Description: The response generated by the text-davinci-003 model, which represents the answer or completion of the task defined in the <mark style="color:yellow;">`instruction`</mark>.

### <mark style="color:blue;">**Fine-Tuning Prompts for Alpaca Model**</mark>

Two distinct prompt structures were used in the fine-tuning process, depending on whether the <mark style="color:yellow;">`input field`</mark> is present or not.

**For Entries with&#x20;**<mark style="color:yellow;">**Non-Empty Input Field**</mark>:

{% code fullWidth="false" %}

```json
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{instruction}

### Input:
{input}

### Response:
```

{% endcode %}

**For Entries with&#x20;**<mark style="color:yellow;">**Empty Input Field**</mark>:

```json
Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{instruction}

### Response:
```

**For a full review of the different types of dataset techniques and structures used in Axolotl please visit datasets.**


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