I wonder if the 0.5B model would be usable for ML tasks like summarization, classification, or embeddings, replacing small models like Spacy usually used for embeddings.
It won't. Amazon kind of when that angle with MistralLite[1] (a 7B finetune), and it was barely passing in terms of being an effective summarizer. 0.5B are pretty much useless.
The official Mistral-7B-v0.2 model added support for 32k context, and I think it's far better than MistralLite. Third-party finetunes are rarely amazing at the best of times.
Now, we have Mistral-7B-v0.3, which is supposedly an even better model:
My experience is that < 500M models are pretty useful when fine-tuned on traditional NLP tasks, such as text classification and sentence/token level labeling. A modern LM with a 32K context window size could be a nice replacement for BERT, RoBERTa, BART.