Depends on which hardware you have. A rule of thumb is that if you want to be efficient, you need about 3x the model size in VRAM to store optimizers state, plus some headroom for data.

You also need to use float for training, due to stability issues. So unless your GPU supports float8, double the RAM.

Realistically, if you have an RTX 4090, you can go up to 6-7B models (Bloom-6B, GPT-j, …). Anything below, and I would aim at 2.7B models (GPT-neo).

I would avoid LLaMA family due to how you get access to pretrained model weights, for liability, and stay with FOSS. In the latter case you can contribute back and gain some visibility this way, assuming you want some.