Introducing Awesome-LLM-Eval
Awesome-LLM-Eval is a comprehensive repository that offers a curated collection of tools, datasets, benchmarks, demonstrations, leaderboards, research papers, documentation, and models. Its main focus lies in evaluating Large Language Models (LLMs) and exploring the technological boundaries and potential of Generative Artificial Intelligence (AI).
Overview
This project serves as a central resource for individuals interested in the evaluation of foundational large models. It provides insights into the capabilities and limitations of generative AI, facilitating a deeper understanding of these powerful technologies.
What's Included?
The Awesome-LLM-Eval project is meticulously organized, with several key components:
- Tools: A range of utilities designed to support the evaluation of language models, helping users assess performance metrics and other critical parameters.
- Datasets / Benchmarks: Collections of data and benchmarks, categorized into various sections such as general datasets, domain-specific collections, RAG (Retrieval-Augmented Generation) evaluation, agent abilities, code capabilities, multi-modal and cross-modal tests, long-context handling, reasoning speed, and quantization compression.
- Demos: Demonstrations to showcase specific use cases and capabilities of large language models.
- Leaderboards: Rankings and comparisons of different models based on various performance metrics.
- Papers and Documentation: A collection of academic papers and comprehensive documentation to aid users in understanding the theoretical underpinnings and practical applications of LLM evaluation.
- LLM List: An organized list of large language models, including pre-trained LLMs, instruction-finetuned models, aligned LLMs, open models, and popular LLMs.
- LLMOps: Information on the operations related to large language models.
- Frameworks for Training: Resources for training frameworks to enhance the performance and utility of LLMs.
- Courses and Others: Educational resources and various other related materials.
- Other-Awesome-Lists: Links to other relevant curated lists for further exploration.
- Licenses and Citation: Legal and citation information for utilizing the resources provided.
News and Updates
The project is regularly updated with new resources and tools to stay current with advancements in AI. Recent additions include sections for inference speed and coding evaluation, tools from major AI communities like Hugging Face, and new Chinese LLMs. Regular updates ensure that the Awesome-LLM-Eval remains a dynamic and evolving resource.
Conclusion
Awesome-LLM-Eval is an invaluable resource for researchers, developers, and enthusiasts looking to delve into the evaluation of large language models. By providing a well-rounded collection of tools, data, benchmarks, and educational materials, the project aids in pushing the boundaries of what is possible with generative AI technologies. Whether assessing current models or seeking to explore new frontiers in AI, Awesome-LLM-Eval offers a solid foundation from which to build.