The name "Tiny LLMs" does not make sense. LLM stands for large language model. So tiny is kind of the opposite of large. Large usually means >=7B parameters (2

The name "Tiny LLMs" does not make sense. LLM stands for large language model. So tiny is kind of the opposite of large. Large usually means >=7B parameters (2023). If it's less, we would just call it language model, not large language model. And language model has a very specific meaning: It models text. Usually we mean an auto-regressive language model, i.e. input is partial text, and output is the the prediction of the following text. Although there are also other kind of language models. Language model always means text-only. E.g, a model for speech recognition is a speech recognition model, not a language model. You might use a language model in addition here (shallow fusion etc), but it's not necessary (end-to-end models). None of the models I see here are language models. So, to put a title here, you could maybe "neural network models in browser" or so. Or maybe "natural language models". "Natural language processing (NLP)" has a different meaning (https://en.wikipedia.org/wiki/Natural_language_processing https://en.wikipedia.org/wiki/Natural_language_processing) (compared to "language model", https://en.wikipedia.org/wiki/Language_model https://en.wikipedia.org/wiki/Language_model) and includes speech recognition. Sometimes this was also referred to as "human language technology (HLT)" (https://en.wikipedia.org/wiki/Language_technology https://en.wikipedia.org/wiki/Language_technology), which would also include all those models. It would also be nice to add a bit more details on what kind of models we see here, how large they are, etc. E.g. for speech recognition, I see that this uses a port of Whisper to the web (https://github.com/xenova/whisper-web https://github.com/xenova/whisper-web) based on the Transformers.js library (https://github.com/xenova/transformers.js https://github.com/xenova/transformers.js). That uses ONNX, and the standard conversion is via Hugging Face Optimum (https://github.com/huggingface/optimum https://github.com/huggingface/optimum), and that usually would do some dynamic quantization, i.e. compression of the model. So maybe the "tiny large" is referring to that. But I did not really find out which Whisper model this is based on, i.e. whether it is Whisper-large (which is still not too large with 1.5B parameters).