galactica-1.3b
Maintainer: facebook
59
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The galactica-1.3b model, developed by the Facebook AI team, is a large language model trained on a large-scale scientific corpus. The model is designed to perform a variety of scientific tasks, including citation prediction, scientific question answering, mathematical reasoning, summarization, and molecular property prediction. The model comes in a range of sizes, from 125M to 120B parameters, and the galactica-1.3b checkpoint represents the "base" size of 1.3 billion parameters.
Similar models to galactica-1.3b include the larger galactica-6.7b and galactica-120b models, which are also part of the GALACTICA model family. These models share the same overall architecture and training objective, but differ in their parameter counts and intended use cases.
Model inputs and outputs
Inputs
- Text prompt: The model takes a text prompt as input, which can contain a variety of content related to scientific domains, such as research papers, encyclopedia entries, or mathematical problems.
Outputs
- Text generation: The primary output of the
galactica-1.3bmodel is text generation, where the model continues the input prompt with relevant and coherent scientific text. - Answers to questions: The model can also be used to answer questions related to the input prompt, leveraging its scientific knowledge to provide informative and accurate responses.
Capabilities
The galactica-1.3b model is capable of performing a wide range of scientific tasks, from predicting relevant citations for a given text, to answering questions about complex scientific concepts, to generating summaries of research papers. The model has been trained on a diverse corpus of scientific literature, including papers, textbooks, and online resources, giving it a broad understanding of various scientific domains.
What can I use it for?
The primary intended users of the GALACTICA models are researchers studying the application of language models to scientific domains. The galactica-1.3b model can be used as a starting point for building scientific tools and applications, such as literature search engines, question-answering systems, or even automated scientific writing assistants.
Developers interested in building scientific applications may find the galactica-1.3b model particularly useful, as it provides a strong foundation of scientific knowledge that can be further fine-tuned or adapted for specific use cases. The model's performance on a range of scientific benchmarks suggests that it can serve as a valuable starting point for a variety of scientific AI projects.
Things to try
One interesting aspect of the galactica-1.3b model is its potential to serve as an alternative to traditional scientific search tools. By leveraging the model's broad scientific knowledge and language understanding capabilities, users may be able to explore academic literature in new ways, such as by asking open-ended questions or generating text that summarizes key insights from multiple sources.
Researchers may also find it valuable to experiment with using the galactica-1.3b model as a pre-trained base for fine-tuning on specific scientific tasks or domains, in order to further enhance its capabilities in those areas. The model's strong performance on a range of scientific benchmarks suggests that it can serve as a powerful starting point for building more specialized scientific AI systems.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
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galactica-6.7b
92
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.
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galactica-120b
148
The galactica-120b model is a massive 120 billion parameter language model developed by the Papers with Code team at Meta AI. It is trained on a large-scale scientific corpus including papers, textbooks, websites, and other scientific text and data. The model is designed to perform a variety of scientific tasks like citation prediction, question answering, mathematical reasoning, and entity extraction. The galactica-120b model is part of a series of GALACTICA models that range in size from 125 million to 120 billion parameters. These models are intended for researchers studying the use of large language models in scientific domains. The model is available under a non-commercial CC BY-NC 4.0 license. Similar models include the BLOOM large language model developed by the BigScience workshop, and the Falcon-180B model from TII which is also a massive 180 billion parameter model. Model inputs and outputs Inputs Text**: The model takes text input which can be in the form of prompts, questions, or instructions for scientific tasks. Outputs Text**: The model generates text outputs in response to the input, which can be continuations, answers, or solutions to the given task. Capabilities The galactica-120b model is capable of a wide range of scientific tasks due to its large scale and training on a diverse scientific corpus. It can be used for citation prediction, question answering, mathematical reasoning, summarization, document generation, molecular property prediction, and entity extraction, among other applications. What can I use it for? Researchers studying the application of large language models to scientific domains can use the galactica-120b model as a powerful pre-trained base to fine-tune for their specific tasks and datasets. The model's broad scientific knowledge and capabilities make it useful for developing AI systems that can assist with various scientific workflows and research. While the model is not intended for production use without appropriate safeguards, developers may also be able to leverage the galactica-120b model to build tools and applications that enhance scientific productivity and discovery. Things to try Some interesting things to try with the galactica-120b model include: Prompting the model with open-ended scientific questions and observing the quality and coherence of its responses. Evaluating the model's ability to solve mathematical problems or generate scientifically accurate content. Exploring the model's performance on domain-specific tasks like citation prediction or entity extraction from research papers. Experimenting with ways to fine-tune or adapt the model for your own scientific use cases and datasets.
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galpaca-30b
55
The galpaca-30b is a large language model developed by the Georgia Tech Research Institute. It is a fine-tuned version of the GALACTICA 30B model, which was trained on a large-scale scientific corpus to perform a variety of scientific tasks. The GALACTICA models range in size from 125M to 120B parameters, with the galpaca-30b being the "large" 30B parameter variant. The galpaca-30b model was further fine-tuned on the Alpaca dataset, a collection of 52K instruction-response pairs designed to enhance the instruction-following capabilities of pre-trained language models. This fine-tuning was done using a modified version of the Self-Instruct Framework. Model inputs and outputs Inputs Freeform text**: The galpaca-30b model can accept arbitrary freeform text as input, such as instructions, questions, or prompts. Outputs Generated text**: Based on the input text, the model will generate relevant output text. This can include answers to questions, responses to instructions, or continuations of the provided prompt. Capabilities The galpaca-30b model demonstrates strong performance on a range of scientific tasks, including citation prediction, scientific question answering, mathematical reasoning, summarization, and more. It outperforms several existing language models on knowledge-intensive tasks, thanks to its large-scale training on scientific data. However, the model is also prone to hallucination, meaning it can generate factually incorrect information, especially for less popular scientific concepts. Additionally, while the model exhibits lower toxicity levels compared to other large language models, it still exhibits some biases. What can I use it for? The primary intended users of the GALACTICA models, including the galpaca-30b, are researchers studying the application of language models to scientific domains. The model could be used to build various scientific tooling, such as literature discovery, scientific question answering, and mathematical reasoning assistants. That said, the maintainers caution against using the model in production environments without proper safeguards, due to the risk of hallucination and biases. Things to try Given the model's strengths in scientific tasks, users may want to experiment with prompts related to various scientific fields, such as requesting explanations of scientific concepts, generating research paper abstracts, or solving mathematical problems. However, it's important to be aware of the model's limitations and not rely on its outputs as authoritative sources of information.
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galpaca-30B-GPTQ
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The galpaca-30B-GPTQ is a 4-bit quantized version of the Galpaca 30B model, created by TheBloke. It is an attempt to create a smaller, more efficient version of the Galpaca 30B model while preserving its performance. This model was fine-tuned on the Alpaca dataset, which consists of 52,000 instruction-response pairs designed to enhance the instruction-following capabilities of language models. Model inputs and outputs The galpaca-30B-GPTQ model is a text-to-text transformer that takes natural language instructions as input and generates corresponding text responses. It can be used for a variety of tasks, such as answering questions, generating summaries, and providing explanations. Inputs Natural language instructions:** The model takes textual instructions or prompts as input, which can cover a wide range of topics and tasks. Outputs Natural language responses:** The model generates coherent and relevant textual responses to the provided instructions or prompts. Capabilities The galpaca-30B-GPTQ model demonstrates strong performance on tasks that require following instructions and providing informative responses. For example, it can accurately explain the meaning of Maxwell's equations when prompted, or generate a Python function that implements the Sherman-Morrison matrix inversion lemma using NumPy. What can I use it for? The galpaca-30B-GPTQ model can be used for a variety of applications that involve natural language understanding and generation, such as: Virtual assistants:** The model can be used to build conversational AI assistants that can follow instructions and provide helpful responses to users. Content generation:** The model can be used to generate informative and coherent text on a wide range of topics, such as summaries, explanations, and creative writing. Educational tools:** The model can be used to create interactive learning experiences, where users can ask questions and receive tailored responses. Things to try One interesting thing to try with the galpaca-30B-GPTQ model is to explore its capabilities on tasks that require technical knowledge or problem-solving skills. For example, you could prompt the model to write a detailed explanation of a scientific concept, or to provide step-by-step instructions for solving a complex mathematical problem. Additionally, you could experiment with different prompting strategies to see how the model responds, and try to fine-tune the model further on specific datasets or tasks.
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