While learning about different types of large language models I stumbled upon Bidirectional Encoder's (so called BERT models).
Before, I used to always think you needed a ton of computing resources to do anything LLM related locally, but these models fascinated me because they could effectively run using only a few GBs of RAM, and without any GPU acceleration.
As an experimental project I wanted to see how efficient it would be at analyzing Reddit threads and ended up with a pretty cool tool.
This published version uses the `j-hartmann/emotion-english-distilroberta-base` fine tuned version of distiloberta-base.
While learning about different types of large language models I stumbled upon Bidirectional Encoder's (so called BERT models).
Before, I used to always think you needed a ton of computing resources to do anything LLM related locally, but these models fascinated me because they could effectively run using only a few GBs of RAM, and without any GPU acceleration.
As an experimental project I wanted to see how efficient it would be at analyzing Reddit threads and ended up with a pretty cool tool.
This published version uses the `j-hartmann/emotion-english-distilroberta-base` fine tuned version of distiloberta-base.