GitHub - DGoettlich/history-llms: Information hub for our project training the largest possible historical language models.

Summary

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Introduction

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Imagine being a historian trying to understand a crucial past event, such as the Industrial Revolution or World War I. You have a vast amount of historical documents at your disposal, but the task of analyzing them and drawing significant conclusions is arduous and time-consuming. Now, imagine having a language model trained on tens of billions of tokens of historical data, capable of answering complex questions and providing contextual information without being influenced by future events. This is exactly what the History LLMs project offers.

History LLMs is an information hub that focuses on training the largest possible historical language models. These models, based on the Qwen3 architecture, have been trained from scratch on 80 billion tokens of historical data, with knowledge cutoffs up to 1913, 1929, and 1933. This innovative approach allows for exploring the past without contamination from future events, offering a more authentic and accurate view of history.

What It Does

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History LLMs is a project aimed at creating large-scale language models trained on historical data. These models, known as Ranke-4B, are based on the Qwen3 architecture and have been trained on a vast amount of historical data, totaling 80 billion tokens. The goal is to provide advanced tools for historical research, allowing scholars to explore the past more accurately and in detail.

Think of History LLMs as an extremely competent digital archivist. This archivist not only knows a vast amount of historical information but is also able to answer complex questions and provide specific contexts. For example, if you ask who Adolf Hitler was, the model trained up to 1913 will not know how to answer, because it has no information on subsequent events. This approach ensures that the answers are based exclusively on the historical data available up to that point, avoiding any contamination from future events.

Why It’s Amazing

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The “wow” factor of History LLMs lies in its ability to provide contextual and accurate answers based exclusively on historical data. It is not just a language model that repeats learned information; it is an advanced research tool that can be used to explore the past more authentically.

Dynamic and contextual: History LLMs is able to provide contextual answers based on a vast amount of historical data. For example, if you ask for information about a specific event, the model can provide not only the facts but also the historical context in which that event occurred. This is particularly useful for historians seeking to understand the dynamics of a past era.

Real-time reasoning: Thanks to its advanced architecture, History LLMs is able to answer complex questions in real-time. This means you can ask specific questions and get immediate answers, without having to wait for long processing times. For example, if you ask “What were the main causes of the Industrial Revolution?”, the model can provide a detailed and contextual answer in a few seconds.

Exploration without contamination: One of the most innovative aspects of History LLMs is its ability to explore the past without contamination from future events. This is possible thanks to the knowledge cutoff set on specific dates, such as 1913. For example, if you ask for information about a historical figure, the model will not know how to answer if that information was acquired after 1913. This ensures that the answers are based exclusively on the historical data available up to that point, avoiding any influence from future events.

Concrete examples: A concrete example of how History LLMs can be used is historical research on specific events. For example, if you are studying World War I, you can ask specific questions about the historical context, the causes, and the consequences of the conflict. The model can provide detailed and contextual answers, helping you to better understand historical events. Another example is the analysis of historical documents. If you have a vast amount of different types of documents, such as letters, newspapers, and books, History LLMs can help you analyze them and draw significant conclusions. For example, you can ask the model to identify the main themes discussed in the documents and provide a contextual analysis.

How to Try It

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To start using History LLMs, follow these steps:

1. Clone the repository: You can find the source code on GitHub at the following address: history-llms. Clone the repository to your computer using the command git clone https://github.com/DGoettlich/history-llms.git.

2. Prerequisites: Make sure you have Python installed on your system. Additionally, some dependencies need to be installed. You can find the complete list of dependencies in the requirements.txt file present in the repository. Install the dependencies using the command pip install -r requirements.txt.

3. Setup: Once the dependencies are installed, you can configure the model by following the instructions in the documentation. There is no one-click demo, but the setup process is well-documented and relatively simple.

4. Documentation: For further details, consult the main documentation present in the repository. The documentation provides detailed instructions on how to use the model and how to perform specific queries.

Final Thoughts

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History LLMs represents a significant step forward in the field of historical research. Thanks to its ability to provide contextual and accurate answers based exclusively on historical data, this project offers advanced tools for exploring the past more authentically. The ability to explore the past without contamination from future events is particularly valuable for historians and anyone interested in understanding history better.

In an era where access to accurate and contextual information is more important than ever, History LLMs positions itself as a project of great value for the community. Its ability to provide immediate and detailed answers on specific historical events makes it an indispensable tool for historical research and analysis. With the continuous development and improvement of the project, we can expect to see more innovative and useful applications of History LLMs in the future.

Use Cases

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• Private AI Stack: Integration into proprietary pipelines
• Client Solutions: Implementation for client projects
• Development Acceleration: Reduction in time-to-market for projects

Third-Party Feedback

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Community feedback: Users appreciate the idea of language models trained on pre-1913 texts to avoid contamination from future events. There is also discussion about the possibility of exploring advanced concepts such as general relativity and quantum mechanics with these models.

Complete discussion

Resources

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Original Links

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• GitHub - DGoettlich/history-llms: Information hub for our project training the largest possible historical LLMs. - Original link

Article recommended and selected by the Human Technology eXcellence team, elaborated through artificial intelligence (in this case with LLM HTX-EU-Mistral3.1Small) on 2026-01-06 09:36 Original source: https://github.com/DGoettlich/history-llms