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Show HN: Sweep, Open-weights 1.5B model for next-edit autocomplete

huggingface.co

21–30 of 160 posts

Re: Show HN: Sweep, Open-weights 1.5B model for next-edit autocomplete

#22
Surprising how badly Jetbrains implemented AI. Apparently to such an extent that even after multiple years of LLM's someone felt confident enough to build a company that can do better.

This looks really neat, interesting technical writeup as well!

Re: Show HN: Sweep, Open-weights 1.5B model for next-edit autocomplete

#23
post #2

[flagged]

> On the infra side, training a 1.5B model in ~4 hours on 8×H100 is impressive.

It's hard to compare without more details about the training process and the dataset, but, is it? Genuine question, because I had the opposite impression. Like, for example, recently I did a full finetuning run on a 3B model chewing through a 146k entry dataset (with 116k entries having reasoning traces, so they're not short) in 7 hours on a single RTX 6000.

Re: Show HN: Sweep, Open-weights 1.5B model for next-edit autocomplete

#27
post #2

[flagged]

> On the infra side, training a 1.5B model in ~4 hours on 8×H100 is impressive. It's hard to compare without more details about the training process and the dataset, but, is it? Genuine question, because I had the opposite impression. Like, for example, recently I did a full finetuning run on a 3B model chewing through a 146k entry dataset (with 116k entries having reasoning traces, so they're not short) in 7 hours o…

[deleted]

Re: Show HN: Sweep, Open-weights 1.5B model for next-edit autocomplete

#28
post #2

[flagged]

You’re subtly pushing the same product in basically every one of your comments. If these are good faith comments please edit out the product name, it’s unnecessary and doing so as a green account just makes people consider you a spammer. Establish yourself first.

Re: Show HN: Sweep, Open-weights 1.5B model for next-edit autocomplete

#29
post #26

what do people use for Neovim to integrate these models for tab-completion level of stuff. (i.e. non agentic/vibe coding)

I use llama.vim with llama.cpp and the qwen2.5-coder 7B model. Easily fits on a 16 GB GPU and is fast even on a tiny RTX 2000 card with 70 watts of power. Quality of completions is good enough for me, if I want something more sophisticated I use something like Codex
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