large language model
The benchmarking dataset, GenAI on the Edge, contains performance metrics from evaluating Large Language Models (LLMs) on edge devices, utilizing a distributed testbed of Raspberry Pi devices orchestrated by Kubernetes (K3s). It includes performance data collected from multiple runs of prompt-based evaluations with various LLMs, leveraging Prometheus and the Llama.cpp framework. The dataset captures key metrics such as resource utilization, token generation rates/throughput, and detailed inference timing for stages such as Sample, Prefill, and Decode.
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We collected programming problems and their solutions from previous studies. After applying some pre-processing steps, we queried advanced LLMs, such as GPT4, with the collected problems to produce machine-generated codes, while the original solutions were labeled as human-written codes. Finally, the entire collected dataset was divided into training, validation, and test sets, ensuring that there is no overlap among these sets, meaning no solutions in two different sets that solve the same programming problem.
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