I am the author of this Neovim plugin for edit completions. I was able to integrate it with the Sweep Edit model.
For anyone who is interested: https://github.com/leonardcser/cursortab.nvim
People posting stuff like this is really cool because otherwise it kinda feels like nobody gives a crap, for example even with Cline/RooCode/KiloCode there’s no good way for me to hook up an autocomplete model that either runs in Ollama or maybe a remote Cerebras Code model, like KiloCode doesn’t have a proper model configuration option even if it has it for the chat or regular agentic stuff - I don’t get why autocomplete is such a special case.
I guess what I’m saying is that I’m glad someone’s at least trying so I don’t have to keep a Copilot subscription just because I genuinely like their autocomplete and the rest of it is basically wasted: Claude Code and Codex and others are better for the actual chat/agentic stuff, KiloCode and others are really nice IDE plugins.
This is a really good plugin. I'm a diehard JetBrains user, I tried switching to VSCode and its various forks many times because of AI but muscle memory from years of use is hard to override. And for a lot of languages JetBrains is just much better, especially out of the box. But they dropped the ball so hard on AI it's unbelievable. Claude Code pulled it back a bit because at least now the cutting edge tools aren't just VSCode plugins, but I was still missing a solid autocomplete tool. Glad this is here to fill that niche. Very likely will be switching my GitHub copilot subscription to this.
I also really appreciate publishing open weights and allowing a privacy mode for anonymous trial users, even if it's opt-in. Usually these things seem to be reserved for paying tiers these days...
I am thinking that one effect is:
- it will become normal for meta-models to train a model specific to a particular task/product.
Also, differently, I'm quite sure that AGI is not available on this current path (useful tho it is), but that some algo improvements might crack ubiquitous trainable AGI. Probably including some kind of embodiment to provide world-models and emotions (which are essential to embodied survival and success).
I understand that the 1.5B is small enough to run locally... but does it actually in the Sweep AI Jetbrains plugin? That is, if I install the plugin, will I download the model automatically and the plugin doesn't phone home?
This looks really neat, interesting technical writeup as well!
> We’re open sourcing the model weights so the community can build fast, privacy-preserving autocomplete for every IDE - VSCode, Neovim, Emacs, and beyond.
Again amazing work! waiting for what you guys cook next
The diff-format insight is especially interesting. Smaller models struggling with unified diffs lines up with what I’ve seen too simpler original/updated blocks reduce noise and improve intent capture.
On the infra side, training a 1.5B model in ~4 hours on 8×H100 is impressive. For folks experimenting with similar mid-scale models, we’ve been running comparable workloads on decentralized GPU aggregators (I’ve used io.net) to avoid cloud quota limits and keep costs predictable with the tradeoff that you handle orchestration yourself.
Curious if you saw diminishing returns when including older edits as context? That cutoff seems tricky in larger repos.
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.
I did buy their $100/yr AI but its about to run out.
What about SFT?
Presumably basing this of Qwen is the reason it can be done for so cheap?
Would instead of the RL step a constrained decoding say via something like xgrammar fix syntax generation issue ?
It can, but you have to consider two things here:
a) constrained decoding ensures adherence to syntax, not semantics. Say you're editing a field in an enum in rust. You can write syntactically correct rust code that doesn't address the new field further in the code (say in a switch). You'd get correctly syntactic code, but the compiler will scream at you. RL works on both.
b) if your goal is to further train the model, so it works on many tasks, RL helps with exploring new paths and training the model further. Constrained grammars help with inference, but the model doesn't "learn" anything. With RL you can also have many reward functions at the same time. Say one that rewards good syntax, one that rewards "closing" all the functions so tree-sitter doesn't complain, and one that rewards 0 errors from the compiler. The model gets to train on all 3 at the same time.
>We ran a genetic algorithm over 30+ diff formats
Can you you give more information about your genetic algorithm? Did you do crossover over the trained models (for example, ranking by fitness, take 20% most elite and create children by mixing their weights randomly)? Did you have a 'population size' (number of instances) for the genetic algorithms, and if so what was it?
I wonder whether we are perhaps the point of usefulness of 'next edit' code development in 2026 though.