SLM_Survey
S
SLM Survey
Overview :
SLM_Survey is a dedicated research project focused on small language models (SLMs), aiming to provide an in-depth understanding and technical assessment of these models through research and measurement. The project covers transformer-based, decoder-only language models with parameter sizes ranging from 100M to 5B. By investigating 59 state-of-the-art open-source SLMs, it analyzes their technological innovations and evaluates their capabilities across multiple domains, including common-sense reasoning, context learning, mathematics, and programming. Additionally, runtime costs such as inference latency and memory usage have been benchmarked. This research is of significant value in advancing the study of SLMs.
Target Users :
The target audience includes researchers, developers, and students in the field of artificial intelligence who require an in-depth understanding of small language models (SLMs) to efficiently deploy language processing models in resource-constrained environments. SLM_Survey offers rich data and insights to help them evaluate and select the most suitable models for their needs.
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Use Cases
Researchers use the data provided by SLM_Survey to compare the performance of different small language models.
Developers utilize the analytical results from the project to select language models suitable for their application scenarios.
Educational institutions use SLM_Survey as teaching material to introduce students to the latest research developments in small language models.
Features
Investigate 59 state-of-the-art open-source small language models (SLMs).
Analyze technological innovations in SLMs regarding architecture, training datasets, and training algorithms.
Evaluate SLMs' capabilities in common-sense reasoning, context learning, mathematics, and programming.
Benchmark the inference latency and memory usage of SLMs to understand their runtime costs.
Provide deep insights into the SLM research field to promote progress in this domain.
How to Use
1. Visit the SLM_Survey GitHub page to understand the project's basic information and research background.
2. Read the project's README file for guidance on how to use the project.
3. Browse the project's Issues and Discussions to learn about current research questions and discussions.
4. Download the project's code and data for local analysis or as a foundation for your research.
5. Utilize the project's benchmark results to assess the performance and efficiency of different SLMs.
6. Refer to the project's analytical methods to evaluate and compare new small language models.
7. Participate as a Contributor to the project and contribute to research in the SLM field.
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