This dataset evaluates instruction following ability of large language models. There are 500+ prompts with instructions such as "write an article with more than 800 words", "wrap your response with double quotation marks", etc.

LLaVA-Bench is a dataset created to evaluate the capability of large multimodal models (LMM) in more challenging tasks and generalizability to novel domains. It consists of a diverse set of 24 images with 60 questions in total, including indoor and outdoor scenes, memes, paintings, sketches, etc., and each image with a highly-detailed and manually-curated description and a proper selection of questions. The dataset is part of the LLaVA project, which aims to develop multimodal chatbots that follow human intents to complete various daily-life visual tasks in the wild.

Grounded SCAN poses a simple task, where an agent must execute action sequences based on a synthetic language instruction. The agent is presented with a simple grid world containing a collection of objects, each of which is associated with a vector of features. The agent is evaluated on its ability to follow one or more instructions in this environment. Some instructions require interaction with particular kinds of objects. gSCAN Example

MultI-Modal In-Context Instruction Tuning (MIMIC-IT) is a dataset for instruction tuning into multi-modal models, motivated by the Flamingo model's upstream interleaved format pretraining dataset. The data sample consists of a queried image-instruction-answer triplet, with the instruction-answer tailored to the image, and context. The context contains a series of image-instruction-answer triplets that contextually correlate with the queried triplet, emulating the relationship between the context and the queried image-text pair found in the MMC4 dataset. Source: Otter: A Multi-Modal Model with In-Context Instruction Tuning Image Source: Otter: A Multi-Modal Model with In-Context Instruction Tuning

UGIF is a multi-lingual, multi-modal UI grounded dataset for step-by-step task completion on the smartphone. It contains 523 natural language instructions with paired sequences of multilingual UI screens and actions that show how to execute the task in eight languages. Source: UGIF: UI Grounded Instruction Following Image Source: https://arxiv.org/pdf/2211.07615v1.pdf

Bactrian-X is a comprehensive multilingual parallel dataset of 3.4 million instruction-response pairs across 52 languages. The instructions were obtained from alpaca-52k, and dolly-15k, and tranlated into 52 languages (52 languages x 67k instances = 3.4M instances). Source: Bactrian-X : A Multilingual Replicable Instruction-Following Model with Low-Rank Adaptation Image Source: Bactrian-X : A Multilingual Replicable Instruction-Following Model with Low-Rank Adaptation

CIDAR contains 10,000 instructions and their output. The dataset was created by selecting around 9,109 samples from Alpagasus dataset then translating it to Arabic using ChatGPT. In addition, we append that with around 891 Arabic grammar instructions from the webiste Ask the teacher. All the 10,000 samples were reviewed by around 12 reviewers.

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InstructOpenWiki is a substantial instruction tuning dataset for Open-world IE enriched with a comprehensive corpus, extensive annotations, and diverse instructions. Source: PIVOINE: Instruction Tuning for Open-world Information Extraction Image Source: PIVOINE: Instruction Tuning for Open-world Information Extraction

This is the sequential instructions dataset from Understanding the Effects of RLHF on LLM Generalisation and Diversity. The dataset is in the alpaca_eval format. For information about how the dataset was generated, see https://github.com/RobertKirk/stanford_alpaca. The instructions in the dataset generally have a sequence of steps we expect the model to complete all at once. In our work, we found that RLHF models generalise much better to this dataset than SFT models when trained on the AlpacaFarm datasets.

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 - Paper: Pre-Print ### 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

Overview The LaMini Dataset is an instruction dataset generated using 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 - Paper : Pre-Print ## 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 ## 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

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. - Total Instructions: 5,507 explanation tuning data samples. ### Dataset Sources - Repository: Bitbucket Project - Paper : Pre-Print ## 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

Dataset Card for "tamil-alpaca" This repository includes a Tamil-translated version of the Alpaca dataset. This dataset is part of the release of Tamil LLaMA family of models – an important step in advancing LLMs for the Tamil language. To dive deep into the development and capabilities of this model, please read the research paper and the introductory blog post (WIP) that outlines our journey and the model's potential impact. GitHub Repository: https://github.com/abhinand5/tamil-llama ## Models trained using this dataset | Model | Type | Data | Base Model | # Params | Download Links | |--------------------------|-----------------------------|-------------------|----------------------|------|------------------------------------------------------------------------| | Tamil LLaMA 7B Instruct | Instruction following model | 145k instructions | Tamil LLaMA 7B Base | 7B | HF Hub | | Tamil LLaMA 13B Instruct | Instruction following model | 145k instructions | Tamil LLaMA 13B Base | 13B | HF Hub | ## Meet the Developers Get to know the creators behind this innovative model and follow their contributions to the field: - Abhinand Balachandran ## Citation If you use this model or any of the the Tamil-Llama datasets in your research, please cite: bibtex @misc{balachandran2023tamilllama, title={Tamil-Llama: A New Tamil Language Model Based on Llama 2}, author={Abhinand Balachandran}, year={2023}, eprint={2311.05845}, archivePrefix={arXiv}, primaryClass={cs.CL} }

Dataset Card for "tamil-alpaca" This repository includes a Tamil-translated versions of the Alpaca dataset and a subset of OpenOrca dataset. This dataset is part of the release of Tamil LLaMA family of models – an important step in advancing LLMs for the Tamil language. To dive deep into the development and capabilities of this model, please read the research paper and the introductory blog post (WIP) that outlines our journey and the model's potential impact. GitHub Repository: https://github.com/abhinand5/tamil-llama ## Models trained using this dataset | Model | Type | Data | Base Model | # Params | Download Links | |--------------------------|-----------------------------|-------------------|----------------------|------|------------------------------------------------------------------------| | Tamil LLaMA 7B Instruct | Instruction following model | 145k instructions | Tamil LLaMA 7B Base | 7B | HF Hub | | Tamil LLaMA 13B Instruct | Instruction following model | 145k instructions | Tamil LLaMA 13B Base | 13B | HF Hub | ## Meet the Developers Get to know the creators behind this innovative model and follow their contributions to the field: - Abhinand Balachandran ## Citation If you use this model or any of the the Tamil-Llama datasets in your research, please cite: bibtex @misc{balachandran2023tamilllama, title={Tamil-Llama: A New Tamil Language Model Based on Llama 2}, author={Abhinand Balachandran}, year={2023}, eprint={2311.05845}, archivePrefix={arXiv}, primaryClass={cs.CL} }

VSTaR-1M is a 1M instruction tuning dataset, created using Video-STaR, with the source datasets: * Kinetics700 * STAR-benchmark * FineDiving The videos for VSTaR-1M can be found in the links above. VSTaR-1M is built off of diverse task with the goal of enhancing video-language alignment in Large Video-Language Models (LVLMs). * kinetics700_tune_.json - Instruction tuning QA pairs for the Kinetics700 source dataset. Good for increasing diversity and for more fine-grained activity recognition. * starb_tune_.json - Instruction tuning QA pairs for the STAR-benchmark source dataset. Good for temporal reasoning. * finediving_tune_.json - Instruction tuning QA pairs for the FineDiving source dataset. Example of adapting LVLMs for novel tasks (Olympic diving judge).