Omkar2118/datasets huggingface dataset:
SurgeGlobal/LaMini

## Overview
The LaMini Dataset is an instruction dataset generated using [h2ogpt-gm-oasst1-en-2048-falcon-40b-v2](https://huggingface.co/h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2). It is designed for instruction-tuning pre-trained models to specialize them in a variety of downstream tasks.

## Dataset Generation
- **Base Model**: h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2.
- **Seed Instructions**: Sourced from databricks/databricks-dolly-15k dataset.
- **Generation Approach**: Example-guided and topic-guided strategies.
- **Total Instructions**: 1,504 unique instruction examples.

### Dataset Sources

- **Repository:** [Bitbucket Project](https://bitbucket.org/paladinanalytics/workspace/projects/OP)
- **Paper :** [Pre-Print](https://arxiv.org/abs/2404.12195)

## Structure
Each entry in the dataset contains:
- **Instruction**
- **Response**

## Usage
The LaMini Dataset can be used to fine-tune language models to improve their ability to follow instructions and generate relevant responses.

## Access
The dataset is available on HuggingFace at the following link: [https://huggingface.co/datasets/SurgeGlobal/LaMini](https://huggingface.co/datasets/SurgeGlobal/LaMini)

## Citation
If you find our work useful, please cite our paper as follows:
```
@misc{surge2024openbezoar,
      title={OpenBezoar: Small, Cost-Effective and Open Models Trained on Mixes of Instruction Data}, 
      author={Chandeepa Dissanayake and Lahiru Lowe and Sachith Gunasekara and Yasiru Ratnayake},
      year={2024},
      eprint={2404.12195},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

## Dataset Authors

Chandeepa Dissanayake, Lahiru Lowe, Sachith Gunasekara, and Yasiru Ratnayake

Introducing paper: OpenBezoar: Small, Cost-Effective and Open Models Trained on Mixes of Instruction Data
SurgeGlobal/Orca

## Dataset Generation

-   **Base Model**: h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2
-   **Seed Instructions**: Derived from the FLAN-v2 Collection.
-   **Generation Approach**: Explanation tuning with detailed responses generated from [h2ogpt-gm-oasst1-en-2048-falcon-40b-v2](https://huggingface.co/h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2).
-   **Total Instructions**: 5,507 explanation tuning data samples.

### Dataset Sources

-   **Repository:**  [Bitbucket Project](https://bitbucket.org/paladinanalytics/notebooks)
-   **Paper :**  [Pre-Print](https://arxiv.org/abs/2404.12195)

## Structure

The dataset entries consist of:
-   **Query**
-   **Response**
-   **System Message**  (when applicable)

## Usage

The Orca Dataset is intended for fine-tuning language models to not only imitate the style but also the reasoning process of LFMs, thereby improving the safety and quality of the models’ responses.

## Citation

If you find our work useful, please cite our paper as follows:
```
@misc{surge2024openbezoar,
      title={OpenBezoar: Small, Cost-Effective and Open Models Trained on Mixes of Instruction Data}, 
      author={Chandeepa Dissanayake and Lahiru Lowe and Sachith Gunasekara and Yasiru Ratnayake},
      year={2024},
      eprint={2404.12195},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

## Dataset Authors

Chandeepa Dissanayake, Lahiru Lowe, Sachith Gunasekara, and Yasiru Ratnayake

Introducing paper: OpenBezoar: Small, Cost-Effective and Open Models Trained on Mixes of Instruction Data
SurgeGlobal/Evol-Instruct

## Dataset Generation

- **Base Model**: h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2
- **Seed Instructions**: Selected from the databricks/databricks-dolly-15k dataset
- **Generation Approach**: Iterative evolution of instructions using a conversational syntax for in-depth and in-breadth evolving
- **Total Instructions**: 2,304 instruction tuning data samples

### Dataset Sources

- **Repository**: [Bitbucket Project](https://bitbucket.org/paladinanalytics/notebooks)
- **Paper**: [Pre-Print](https://arxiv.org/abs/2404.12195)

### Structure

The dataset entries consist of:
- **Instruction**
- **Response**
- **Evolution Strategy** (in-depth or in-breadth)
- **Category** (of the original instruction)

### Usage

The Evol-Instruct Dataset is designed for the automatic evolution of instruction datasets, enhancing the complexity and diversity of instructions to train language models for a wide range of tasks.

## Citation

If you find our work useful, please cite our paper as follows:
```
@misc{surge2024openbezoar,
      title={OpenBezoar: Small, Cost-Effective and Open Models Trained on Mixes of Instruction Data}, 
      author={Chandeepa Dissanayake and Lahiru Lowe and Sachith Gunasekara and Yasiru Ratnayake},
      year={2024},
      eprint={2404.12195},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

## Dataset Authors

Chandeepa Dissanayake, Lahiru Lowe, Sachith Gunasekara, and Yasiru Ratnayake

Introducing paper: OpenBezoar: Small, Cost-Effective and Open Models Trained on Mixes of Instruction Data