Introduction to Awesome-Efficient-LLM
Awesome-Efficient-LLM is a meticulously curated collection focusing on enhancing the efficiency of large language models (LLMs). This project aggregates various research papers, methodologies, and tools dedicated to optimizing LLMs, which are crucial in many AI applications today. By doing so, it provides a comprehensive resource for researchers and developers interested in making LLMs more efficient and accessible.
Full List of Topics
The Awesome-Efficient-LLM project encompasses a range of topics that address different aspects of improving the performance and efficiency of LLMs:
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Network Pruning / Sparsity: Techniques aimed at reducing the size and computational requirements of LLMs without significantly affecting their accuracy.
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Knowledge Distillation: This involves transferring knowledge from a large model to a smaller one, making it more efficient without substantial loss in performance.
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Quantization: Methods that convert models into a more compact form by reducing their precision, enabling faster computations and lower memory usage.
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Inference Acceleration: Various strategies to speed up the inference process in LLMs, making them more suitable for real-time applications.
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Efficient MOE (Mixture of Experts): Research on optimizing MOE architectures to reduce computational costs while maintaining high performance.
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Efficient Architecture of LLM: Designing new model structures that inherently require fewer resources to achieve similar or improved results.
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KV Cache Compression: Techniques to compress key-value caches in LLMs, thus reducing memory requirements and potentially speeding up processes.
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Text Compression: Methods for compressing text inputs or outputs in LLMs, aiding in storage and processing efficiency.
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Low-Rank Decomposition: Decomposing model weights into lower-dimensional forms, which can significantly reduce model size and computation.
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Hardware / System: Optimizations that leverage hardware capabilities to run LLMs more efficiently.
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Tuning: Fine-tuning strategies that help adjust LLMs to specific tasks with minimal resources.
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Survey: Comprehensive reviews of current methods and tools in the field of efficient LLMs.
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Leaderboard: A comparison table displaying various approaches and their performance statistics, serving as a benchmark for new methods.
Recent Updates
The project has been continuously updated to include the latest advancements in LLM efficiency:
- May 29, 2024: Celebrating a year of providing valuable insights into LLM efficiencies.
- Sep 6, 2023: Added a new subdirectory to manage efficient LLM projects.
- July 11, 2023: Created a directory for papers specifically related to Pretrained Language Models (PLMs).
Contributions
Awesome-Efficient-LLM encourages contributions from the community. Researchers and developers can submit their papers and updates through pull requests. The project facilitates this by providing a script, generate_item.py, to help generate the markdown needed for submissions.
Recommended Papers
For each topic, the project features a selection of recommended papers. These papers are chosen based on the number of GitHub stars or citations they have received, indicating their influence and utility in the field.
Whether you are a researcher, developer, or enthusiast aiming to delve into efficient large language models, Awesome-Efficient-LLM serves as an invaluable resource. By bringing together the latest research and methodologies, this project helps drive forward the development of more robust, accessible, and efficient LLMs.