Been playing with that, but doesn’t seem to have much effect. It works very well to limit output to smaller bits, like setting it to 100-200. But above 2-4k the output seems to never get longer than about 1 page
Might try using the models with mlx instead of ollama to see if that makes a difference
Any tips on prompting to get longer outputs?
Also, does the model context size determine max output size? Are the two related or are they independent characteristics of the model?
> Total output context up to 128K tokens for the 4B, 12B, and 27B sizes, and 32K tokens for the 1B size per request, subtracting the request input tokens
I don't know how to get it to output anything that length though.
edit: just tried mistral3.1 and the quality of the output is very good, at least compared to the other models I tried (llama2:7b-chat, llama2:latest, gemma3:12b, qwq and deepseek-r1:14b)
Doing some research, because of their training sets, it seems like most models are not trained on producing long outputs so even if they technically could, they won’t. Might require developing my own training dataset and then doing some fine tuning. Apparently the models and ollama have some safeguards against rambling and repetition
You can probably find some long-form tuned models on HF. I've had decent results with QwQ-32B (which I can run on my desktop) and Mistral Large (which I have to run on my server). Generating and refining an outline before writing the whole piece can help, and you can also split the piece up into multiple outputs (working a paragraph or two at a time, for instance). So far I've found it to be a tough process, with mixed results.
