task_categories:text-generation language:en license:apache-2.0 size_categories:1K<n<10K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars region:us
---
dataset_info:
  features:
  - name: prompt
    dtype: string
  - name: context
    dtype: string
  - name: response
    dtype: string
  splits:
  - name: train
    num_bytes: 11115540
    num_examples: 6699
  download_size: 6547413
  dataset_size: 11115540
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: apache-2.0
task_categories:
- text-generation
language:
- en
---
# mixed-llm-instruction

## Overview
The **vietdata/mixed-llm-instruction** dataset is an open-source collection designed for instruction tuning and prompt recovery. This dataset comprises three key columns: `prompt`, `context`, and `response`. Prompts and contexts are sourced from the **databricks/databricks-dolly-15k** dataset. We further use LLMs to generate rewriting prompts (change stype, tone, etc.). Each rewrite prompt is paired with a randomly selected context from the **databricks/databricks-dolly-15k** dataset and processed by various large language models (LLMs) via the **together.ai** API.

## Dataset Structure

### Columns
- **prompt**: The initial query or instruction intended for the language model.
- **context**: The background information or additional details related to the prompt.
- **response**: The output generated by the language model in response to the provided prompt and context.

## Data Generation Process
1. **Source Selection**: Prompts and contexts are selected from the **databricks/databricks-dolly-15k** dataset.
2. **Pairing**: A prompt and a context are randomly paired.
3. **Model Invocation**: The paired prompt and context are forwarded to various large language models (such as Gemma, LLama, etc.) using the **together.ai** API.
4. **Response Collection**: The responses generated by the models are collected and included in the dataset.

## Usage
This dataset is ideal for:
- **Instruction Tuning**: Enhancing the performance and reliability of language models by providing diverse prompts and contexts for fine-tuning.
- **Prompt Recovery**: Developing techniques and models to recover or reconstruct prompts based on given contexts and responses.

## Example
Here is a sample entry from the dataset:

| prompt                         | context                                     | response                                 |
|--------------------------------|---------------------------------------------|------------------------------------------|
| "How do I start a garden?"     | "Gardening requires selecting a suitable..."| "To start a garden, begin by choosing..." |