Turns out that Ollama on windows will run multiple models in parallell consuming all available VRAM and RAM. Changing it to 1 fixed the issue, now it's working great! However, the context length for the output is very small - only 1024 tokens.
That's some really strange behavior, I don't know why that would cause poor results rather than just poor performance.
Can you configure the context size with `/set parameter num_ctx N`? On my laptop with an RTX A3000 12GB I can run `yi-coder:9b-chat` (Q4_0) with 32768 context and it produces good results quickly. That uses 11GB of VRAM so it's maxed out for this setup.
