[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?

[thot_experiment]

yes, the vscode extension is a one click install, so is ollama which is a separate project that provides local inference

you'll then have to download a model, which ollama makes very easy. choosing which one will depend on your hardware but the biggest QwenCoder2.5 you can fit is a very solid starting place. it's not ready for your grandma, but it's easy enough that I'd trust a junior dev to be able to get it done

[k__]

What's the extension name?

[thot_experiment]

Continue, I talk about it at length in the gp post.

[k__]

Ah, thanks.

I just read the parent post, lol.

[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?