[meta_x_ai]

A software developer's time is much more precious than wasting time on sub-optimal models.

Open Weights models has it's place (in training custom agents and custom services), but if you are knowledge worker, using a model even 5% less than SOTA is extremely dumb

[thot_experiment]

100% disagree with this take, the flexibility in controlling the prompt leads to QwenCoder2.5-32b outperforming gpt-o1 and claude sonnet 3.5 for nearly everything that I use it for (true for Gemma-27b and llama3.3-70b, though in this context I'm almost always using the former). A specialist model that's specifically prompted to do the correct thing will outperform a SOTA generic model with a one size fits all system prompt. This is why small autocomplete models can very obviously outperform larger models at that specific task. I am speaking 100% from experience and ignoring all benchmarks in forming this view btw, so maybe it's just my specific situation.

Also, in general I don't find the difference between SOTA models and local models to be that significant in the real world even when used in the exact same way.

[k__]

Sounds great.

Does this run with VSCode and how hard is it to set this up?

[edm0nd]

Are there any small trained models out there that are specifically for python programming that you know of?

[grandma_tea]

Do you have any example prompts or suggestions for coming up with them?