Doc-to-LoRA (D2L): Learning to Instantly Internalize Contexts :sparkles:Interactive Web | :newspaper:X | :scroll:Paper | :hugs:Hugging Face | :octocat:GitHub A reference implementation of Doc-to-LoRA (D2L). 🛠️ Installation 🤗 Pre-Trained Models 🚀 Python API Usage 🎮 Interactive Demo Video Demo 🧪 Experimental Scripts To run any of the following scripts, use from the root of this project. 🔬 Self-Generated Data Viewer After downloading/generating the data, we can see samples of the data using this script. See more info at webui/SELFGENVIEWER.md. 📚 Citation
| Stars | 828 |
| Forks | 106 |
| Language | Python |
| Category | AI 工具 |
| License | MIT |
| Quality Score | 66.25293072507/100 |
| Last Updated | 2026-06-15 |
| Created | 2026-02-11 |
| Platforms | python |
| Est. Tokens | ~443k |
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doc-to-lora is Hypernetworks that update LLMs to remember factual information. It is categorized as a AI 工具 with 828 GitHub stars.
doc-to-lora is primarily written in Python. It covers topics such as ai, ai-agent, hypernetworks.
You can find installation instructions and usage details in the doc-to-lora GitHub repository at github.com/SakanaAI/doc-to-lora. The project has 828 stars and 106 forks, indicating an active community.
doc-to-lora is released under the MIT license, making it free to use and modify according to the license terms.