Maintainer: NousResearch

Total Score


Last updated 5/28/2024


Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

Genstruct-7B is an instruction-generation model designed by NousResearch. It is trained to create valid instructions given a raw text corpus, enabling the creation of new, partially synthetic instruction finetuning datasets. This work was inspired by Ada-Instruct, which trained a custom instruction-generation model, whereas previous methods largely relied on in-context approaches.

Genstruct-7B takes this approach further by grounding the generations in user-provided context passages. It is trained to generate questions involving complex scenarios that require detailed reasoning, allowing for models trained on the generated data to reason step-by-step. This contrasts with models like ChatGPT and RAG which use few-shot prompting or retrieve information from an external knowledge base.

Model inputs and outputs


  • Context passages: Text provided by the user that grounds the instruction generations


  • Instructions: Novel instructions generated based on the input context passages, involving complex reasoning and scenarios


Genstruct-7B can be used to create rich, contextual instruction datasets for training downstream models. By generating instructions that require step-by-step reasoning, it enables the development of models with stronger general language understanding and problem-solving abilities. This contrasts with models trained on more simplistic or templated instructions.

What can I use it for?

The Genstruct-7B model could be used as a tool to quickly generate diverse datasets for training new AI models, across a wide range of domains and applications. For example, you could use it to create instruction datasets for task-oriented dialog, procedural text generation, or educational applications that require complex reasoning.

Things to try

One interesting thing to try with Genstruct-7B would be to experiment with the level of complexity and reasoning required in the generated instructions. By adjusting the input context passages, you could explore how this impacts the downstream model's capabilities and performance on benchmarks like HellaSwag, PIQA, and GSM8K. This could yield insights into the types of instruction-based datasets that are most effective for training robust language models.

This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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