Earlier quoted context omitted.
Could you give the gist / config on how you made it work with Zed ?
I had Claude add it as an edit-prediction provider (running locally on llama.cpp on my Macbook Pro). It's been working well so far (including next-edit prediction!), though it could use more testing and tuning. If you want to try it out you can build my branch: https://github.com/ihales/zed/tree/sweep-local-edit-predicti... If you have llama.cpp installed, you can start the model with `llama-server -hf sweepai/sweep-…
Show HN: Sweep, Open-weights 1.5B model for next-edit autocomplete
151–160 of 160 posts
Re: Show HN: Sweep, Open-weights 1.5B model for next-edit autocomplete
#152At least for C#, the quality of the cloud offering is rather mediocre, so I don’t expect this model to be that useful there. It’s very overeager, suggesting tons of stuff that I never accepted because it made no sense. It’s also producing bad code, wanting me to use `.Result` for async calls instead of simply await-ing.
Re: Show HN: Sweep, Open-weights 1.5B model for next-edit autocomplete
#153I read the release but didn't quite understand the difference between a next-edit model and a FIM model - does anyone have a clear explanation of when to use one over the other? I'd love if there was a sublime plugin to utilize this model and try it out, might see if I can figure that out.
But basically suggesting changes away from your cursor position
Re: Show HN: Sweep, Open-weights 1.5B model for next-edit autocomplete
#154Re: Show HN: Sweep, Open-weights 1.5B model for next-edit autocomplete
#155Earlier quoted context omitted.
I had Claude add it as an edit-prediction provider (running locally on llama.cpp on my Macbook Pro). It's been working well so far (including next-edit prediction!), though it could use more testing and tuning. If you want to try it out you can build my branch: https://github.com/ihales/zed/tree/sweep-local-edit-predicti... If you have llama.cpp installed, you can start the model with `llama-server -hf sweepai/sweep-…
This is great and similar to what I was thinking of doing at some point. I just wasn't sure if it needed to be specific to Sweep Local or if it could be a generic llama.cpp provider.
Re: Show HN: Sweep, Open-weights 1.5B model for next-edit autocomplete
#156Earlier quoted context omitted.
Personally, I think usable AI is more valuable than simply more intelligence. Many of the labs are pushing towards models that are 1% better on CodeForces and AIME if you just let it think and use tools for hours, instead of more user-friendly models with better coding habits, like writing shorter and more modular code.
Totally this. But the corp labs have incentives to keep researching per investors and staffing load, so they have to show work. I guess a nice advantage of backwardness here is that economic opportunities exist for those who can solve pain points in the use of existing intel. Older models often do almost as well at agentic tasks in reality, can probably go further. Still, AGI should remove a lot of this making it red…
Re: Show HN: Sweep, Open-weights 1.5B model for next-edit autocomplete
#157It's good. The blog post about it is very interesting. I hope, a plugin for neovim will be made soon. https://blog.sweep.dev/posts/oss-next-edit
There's also https://github.com/ggml-org/llama.vim Which I've been using with Qwen3 Coder. As long as infill is supported, that should work. I'll try later today.
Re: Show HN: Sweep, Open-weights 1.5B model for next-edit autocomplete
#158Re: Show HN: Sweep, Open-weights 1.5B model for next-edit autocomplete
#159Earlier quoted context omitted.
Totally this. But the corp labs have incentives to keep researching per investors and staffing load, so they have to show work. I guess a nice advantage of backwardness here is that economic opportunities exist for those who can solve pain points in the use of existing intel. Older models often do almost as well at agentic tasks in reality, can probably go further. Still, AGI should remove a lot of this making it red…
Ya definitely, that makes total sense. It feels to me that currently the labs have great researchers, who only care about making models perform better across raw intel and then they have incompetent applied AI engineers / FDE's who can only suggest using better prompting to remove bad habits to make agents more usable.