Hi,
there are two issues (with different solutions):
- You are able to complete the warmup phase, but need to add more sampling iterations.
- Your model does not even complete the warmup phase
In case 1) you should be able to replicate the process from the Python reference with cmdstanr - (see Saving & reusing adaptation in cmdstanr - #44 by mike-lawrence) but also with rstan - you need to get the inverse metric, init values and step sizes from the previous run to start a new run without warmup
Case 2) is a bit more complex as the warmup is relatively complex thing… You might be able to get OK results by chaining together several shorter adaptations. You might also be able to run the individual adaptation windows separately (with the same method for restarting), by appropriately manipulating the init_buffer, term_buffer and window parameters. But I am just guessing and have never really tried doing that myself.
Best of luck with your model!