Currently, the language models are parsed from json files and loaded into simple maps at runtime. Even though accessing the maps is pretty fast, they consume a significant amount of memory. The goal is to investigate whether there are more suitable data structures available that require less storage space in memory, something like NumPy for Python. Perhaps it is even possible to store those data structures in some kind of binary format on disk which can be loaded faster than the current json files.
pemistahl
commented
1 month ago
Currently, the language models are parsed from json files and loaded into simple maps at runtime. Even though accessing the maps is pretty fast, they consume a significant amount of memory. The goal is to investigate whether there are more suitable data structures available that require less storage space in memory, something like NumPy for Python. Perhaps it is even possible to store those data structures in some kind of binary format on disk which can be loaded faster than the current json files.
Promising candidates could be: