galactica-6.7b

Maintainer: facebook

Total Score

92

Last updated 5/28/2024

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PropertyValue
Run this modelRun on HuggingFace
API specView on HuggingFace
Github linkNo Github link provided
Paper linkNo paper link provided

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Model overview

galactica-6.7b is a large language model developed by Facebook's Papers with Code team. It is part of a series of GALACTICA models ranging in size from 125M to 120B parameters, all trained on a large-scale scientific corpus including papers, textbooks, websites, and more. The galactica-6.7b model is the "standard" size in the series and is designed to perform a variety of scientific tasks like citation prediction, question answering, mathematical reasoning, and molecular property prediction.

Similar models in the GALACTICA family include the galactica-120b "huge" model and the bloom-7b1 and bloom-1b7 models developed by the BigScience workshop, all of which are large multilingual language models trained on scientific or academic data.

Model inputs and outputs

The galactica-6.7b model follows a standard text-to-text transformer architecture, taking in natural language prompts and generating relevant text outputs. The model can be used for a variety of tasks by providing appropriate prompts.

Inputs

  • Natural language prompts for tasks like scientific question answering, citation prediction, summarization, or open-ended generation.

Outputs

  • Relevant text outputs for the given input prompt, such as answers to questions, predicted citations, summaries, or generated scientific content.

Capabilities

The galactica-6.7b model is capable of performing a wide range of scientific and academic tasks. It has shown strong performance on benchmarks for citation prediction, scientific question answering, mathematical reasoning, and more. The large scale of the model's training data allows it to draw upon a broad knowledge base spanning multiple scientific domains.

What can I use it for?

Researchers studying the application of large language models to scientific and academic tasks could find the galactica-6.7b model useful. Developers looking to build scientific tools and applications could also leverage the model's capabilities. However, it's important to be cautious about the model's potential to hallucinate or exhibit biases, so appropriate safeguards should be in place for production use.

Things to try

One interesting aspect of the galactica-6.7b model is its ability to generate relevant citations for a given scientific prompt. Experimenting with citation prediction tasks could yield insights into the model's understanding of academic literature and references. Additionally, probing the model's performance on domain-specific tasks like chemical property prediction or mathematical reasoning could uncover its strengths and limitations in specialized scientific areas.



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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galactica-1.3b

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Total Score

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