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Show HN: Sweep, Open-weights 1.5B model for next-edit autocomplete
Hey HN, we trained and open-sourced a 1.5B model that predicts your next edits, similar to Cursor. You can download the weights here (https://huggingface.co/sweepai/sweep-next-edit-1.5b https://huggingface.co/sweepai/sweep-next-edit-1.5b) or try it in our JetBrains plugin (https://plugins.jetbrains.com/plugin/26860-sweep-ai-autocomplete--coding-agent https://plugins.jetbrains.com/plugin/26860-sweep-ai-autocomp...).
Next-edit autocomplete differs from standard autocomplete by using your recent edits as context when predicting completions. The model is small enough to run locally while outperforming models 4x its size on both speed and accuracy.
We tested against Mercury (Inception), Zeta (Zed), and Instinct (Continue) across five benchmarks: next-edit above/below cursor, tab-to-jump for distant changes, standard FIM, and noisiness. We found exact-match accuracy correlates best with real usability because code is fairly precise and the solution space is small.
Prompt format turned out to matter more than we expected. We ran a genetic algorithm over 30+ diff formats and found simple `original`/`updated` blocks beat unified diffs. The verbose format is just easier for smaller models to understand.
Training was SFT on ~100k examples from permissively-licensed repos (4hrs on 8xH100), then RL for 2000 steps with tree-sitter parse checking and size regularization. The RL step fixes edge cases SFT can’t like, generating code that doesn’t parse or overly verbose outputs.
We're open-sourcing the weights so the community can build fast, privacy-preserving autocomplete for any editor. If you're building for VSCode, Neovim, or something else, we'd love to see what you make with it!
- plutodev 9mo ago[flagged]
- kouteiheika 9mo ago> 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.
- deleted 9mo ago[deleted]
- kevinlu1248 8mo agoHonestly I think we can improve our training throughput drastically via a few more optimizations but we've been spending most of our time on model quality improvements instead.
- oefrha 9mo agoYou’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.
- subscribed 9mo agoThey've submitted "I'm working at io.net" quite openly, but I admit, they should at least announce their employment in the bio, otherwise it's a very poorly executed astroturf post (phrased like they're an experimenting user and not a dev).
- lelanthran 9mo agoOr he could disclose it.l, which he did in a different comment on a different story. I agree that green accounts could be regarded as suspicious and, if it were me, I'd disclose each time I mention it.
- wepaean 9mo ago[dead]
- kamranjon 9mo agoI 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.
- sheepscreek 9mo agoI’m going to speculate a bit here, FIM may stand for something-in-the-middle? I know there are the original autocomplete models that simply complete the endings. Then there are Cursor like models capable of editing/filling text between blocks of code. In essence, they look at both the text before the insertion point and after it - then find the best fitting completion in the middle. My guess is FIM is the latter.
- aidos 9mo agoAs you said. Fill-in-the-middle.
- evolving-silica 9mo agoI was curious as well and wanted to try how this work, so I asked claude to create a plugin for that. This utilizes built-in autocomplete behavior. If you want to give it a try then feel free to have a look here https://github.com/lumnn/AItoComplete https://github.com/lumnn/AItoComplete (did not push it to packagecontrol yet)
- kevinlu1248 8mo agoWe have an explanation here: https://blog.sweep.dev/posts/next-edit-jetbrains#next-edit-autocomplete https://blog.sweep.dev/posts/next-edit-jetbrains#next-edit-a... But basically suggesting changes away from your cursor position
- mgz 9mo agoI use Sweep’s Jetbrains autocomplete plugin daily, it really stands out.
- 8n4vidtmkvmk 9mo agoBetter than the one that ships with Jetbrains? I did buy their $100/yr AI but its about to run out.
- smusamashah 9mo agoDoes it run totally offline?
- dcreater 9mo agoBased on qwen2.5-coder? seems like a "why not/resume embellish/show VC" type release I guess
- dang 9mo ago"Please don't post shallow dismissals, especially of other people's work. A good critical comment teaches us something." https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html
- kevinlu1248 8mo agoYou can see that Qwen3 does worse than Qwen2.5 on our benchmark. Reason is it's never been pretrained for FIM / autocomplete.
- ing33k 9mo agocan it be integrated in monaco editor ?
- bangaladore 9mo agoSo SFT cost less only low hundreds of dollars? (1-10$ per hour per H100 if I'm seeing this correctly). What about SFT? Presumably basing this of Qwen is the reason it can be done for so cheap?
- syntaxing 9mo agoWow super fun read, I love how it went into the technical details. Any way to make it work with vscode?
- martianlantern 9mo agoThis is cool! I am more interested in how you guys generated next edit training data from repos, seems like there are lots of caveats here. Would love your insights Again amazing work! waiting for what you guys cook next
- knowaveragejoe 8mo agoThe blog post has more information: https://blog.sweep.dev/posts/oss-next-edit https://blog.sweep.dev/posts/oss-next-edit
- kevinlu1248 8mo agoAlso more technical details on SFT data here: https://blog.sweep.dev/posts/next-edit-jetbrains#building-autocomplete-from-scratch https://blog.sweep.dev/posts/next-edit-jetbrains#building-au...
- sim04ful 9mo agoI'm very green to this so forgive if this question sounds silly: Would instead of the RL step a constrained decoding say via something like xgrammar fix syntax generation issue ?
- NitpickLawyer 9mo ago> 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.
- kevinlu1248 8mo ago^ these were pretty much the main reasons. The other one is that constrained decoding only works on CFGs (simpler grammars like JSON schemas) since only these ones can produce automatas which can be used for constrained decoding. Programming languages like Python and C++ aren't CFGs so it doesn't work. Also constrained decoding generally worsens model quality since the model would be generating off-policy. So RL helps push corrected syntax back on-policy.
- rationably 9mo agoDo you plan to release Sweep 3B/7B on HF?
- _ache_ 9mo agoYeap, the two seems like game changer. For now, I'm using "Qwen2.5-Coder-7B". Sweep 1.5B is "just" 12 % point better than Qwen2.5-Coder, but Sweep 7B is 25% point better.
- kevinlu1248 8mo agoNot at the moment but we do host it for our Jetbrains plugin
- _ache_ 9mo agoIt'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 https://blog.sweep.dev/posts/oss-next-edit
- WanderlingSmurf 9mo ago[dead]
- mromanuk 9mo agoThere is one already, on of the plugin authors commented here
- evanreichard 9mo agoThere's also https://github.com/ggml-org/llama.vim 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.
- jmanandchanny 8mo agoThanks for sharing this. I personally use vim and not neovim (I do not have anything against it), so this plugin will be a great addon for me. Currently, I have to switch from vim to Cursor and back again for any kind of vibe coding.
- kevinlu1248 8mo agoSomeone in this thread already built a Neovim plugin connecting to this model I believe.
- _boffin_ 9mo agoFollowed your work since the beginning and used it for inspiration for some cool demos on self-healing web scrapers. fascinating to see the transition from original concept to producing models. cool stuff.
- whimsicalism 9mo agoVery interesting - and cool to read about the development process. I'd love to hear more about how genetic algorithm worked here. I wonder whether we are perhaps the point of usefulness of 'next edit' code development in 2026 though.
- dainiusse 9mo agoAny easy way to try on vscode?
- asyncze 9mo ago[dead]
- esquire_900 9mo agoSurprising 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!
- kevinlu1248 8mo agoThanks! Let us know if you have any questions / feedback.
- kleiba 9mo agoVery cool! 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?
- bjarteaarmolund 9mo agono, as far as I can see there is no way to configure the Jetbrains plugin to use a local endpoint.
- NewsaHackO 9mo agoYes, I get the same vibe, as one has to sign in to their site to use the plugin. Kind of grimy for them to seemingly imply that it is locally run when it isn't.
- rkagerer 9mo agoWhy not? Can someone make a better plugin?
- kevinlu1248 8mo agoNot at the moment, if you install the hosted Sweep AI Jetbrains plugin it uses our hosted (larger) model.
- h33t-l4x0r 9mo agoIt sounds like you might be killing Zed's ability to monetize, am I misunderstanding that?
- BoredPositron 9mo agoIf your only feature worth monetizing is replicated by a solo dev in his freetime you might have a problem.
- gunalx 9mo agonot really though. The zed monetization seems to push towards selling tokens for full fledged models with good ide integration as a service. (They have let you run a custom tabcomplete for a little while)
- moelf 9mo agowhat do people use for Neovim to integrate these models for tab-completion level of stuff. (i.e. non agentic/vibe coding)
- dajonker 9mo agoI 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
- magnat 9mo agoIs there a way to use this (or similar) model in Visual Studio? Extensions on Visual Studio Marketplace are clunky and sluggish at best, if they even work at all.
- denysvitali 9mo agoIf you mean VSCode (or any other editor): > We’re open sourcing the model weights so the community can build fast, privacy-preserving autocomplete for every IDE - VSCode, Neovim, Emacs, and beyond. https://blog.sweep.dev/posts/oss-next-edit https://blog.sweep.dev/posts/oss-next-edit
- KronisLV 9mo agoI remember using Qwen 2.5 Coder for autocomplete with Continue.dev, that experience was a mess both in JetBrains IDEs, as well as Visual Studio Code. 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.
- lostmsu 9mo agollama.cpp has an extension for VS Code, but configuration UX is utter crap
- vichle 9mo agoWhat type of hardware do I need to run a small model like this? I don't do Apple.
- jychang 9mo ago1.54GB model? You can run this on a raspberry pi.
- BoredomIsFun 9mo agoPerformance of LLM inference consists of two independent metrics - prompt processing (compute intensive) and token generation (bandwidth intensive). For autocomplete with 1.5B you can get away with abysmal 10 t/s token generation performance, but you'd want as fast as possible prompt processing, pi in incapable of.
- gunalx 9mo agoif you mean on the new ai hat with npu and integrated 8gb memory, maybe.
- bodegajed 9mo ago1.5B models can run on CPU inference at around 12 tokens per second if I remember correctly.
- moffkalast 9mo agoIngesting multiple code files will take forever in prompt processing without a GPU though, tg will be the least of your worries. Especially when you don't append but change it in random places so caching doesn't work.
- bradfa 8mo agoA FIM or completion model like this won't have a large prompt and caching doesn't work anyways (per their notes). It'll get maybe a few thousand tokens in a prompt, maximum. For a 1.5B model, you should expect usable CPU-only inference on a modern CPU, like at least hundreds of tokens per second of prefill and tens of tokens per second of generation, which is decently usable in terms of responsiveness.
- andruby 9mo agoHow easy is it to re-train these to specific subset of programming languages? Could there be a "ruby+rails+html" version, etc?
- bradfa 8mo agoI'd love to be able to take an open model like this and feed it the codebases that I work on regularly in order to improve its performance for less "hip/modern" languages and frameworks. It would be awesome to see a blog post about how normal users can find tune these models and rough cost estimates with examples!
- vanillameow 9mo agoSometimes when I use a plugin like this I get reminded just how much of a productivity nerf it is to code without an autocomplete AI. Honestly in my opinion if you write a lot of boilerplate code this is almost more useful than something like Claude Code, because it turbocharges your own train of thought rather than making you review someone else's, which may not align with your vision. 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...
- cmrdporcupine 9mo agoYep. I'm coming to resent Claude Code and tools like it for taking me out of direct contact with the code. I think we're still in the early days of these systems. The models could be capable of a lot more than this "chat log" methodology. Agree about JetBrains dropping the ball. Saddens me because I've also been a diehard user of their products since 2004.
- qorrect 9mo agoGlad to hear I'm not alone, the latest releases of JetBrains have been so bad I finally cancelled my subscription. VSCode has been a nice surprise, "its giving old emacs" as the kids would say.
- sitkack 9mo agoI am curious about how both of you think Jetbrains is dropping the ball so much that you are no longer buying the tool. You are still using it but no longer getting updates?
- jedisct1 9mo agoReally cool. But how to use it instead of Copilot in VSCode ?
- flanked-evergl 9mo agoWould love to know myself, I recall there was some plugin for VSCode that did next edits that accepted a custom model but I don't recall what it was now.
- replete 9mo agoRun server with ollama, use Continue extension configured for ollama
- BoredomIsFun 9mo agoI'd stay away from ollana, just use llama.cpp; it is more up date, better performing and more flexible.
- mika6996 8mo agoBut you can't just switch between installed models like in ollama, can you?
- BoredomIsFun 8mo agollama-swap? https://www.nijho.lt/post/llama-nixos/ https://www.nijho.lt/post/llama-nixos/
- ragchronos 9mo agoDoes anyone know if the 7B model is also available somewhere?
- logicallee 9mo agoCongratulations on training a relatively small model that can beat larger models for this important task. >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?
- keepamovin 9mo agoThis is so cool. What is the second order effect of model training becoming democratized? And local models becoming the norm? Tasks like agentic work are well handled by current AI as long as you know what you're doing and can stress the agent against tests/spec, etc. 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).
- kevinlu1248 8mo agoPersonally, 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.
- keepamovin 8mo agoTotally 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 redundant, and it will then be more about the intel than the tooling. But an opportunity exists now. We may not have widespread AGI until 8 - 10 years later, so plenty of money to be made in the meantime.
- kevinlu1248 8mo agoYa 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.
- rw_panic0_0 9mo agois there any llm lsp it can integrate well with?
- kevinlu1248 8mo agoWe currently integrate with Jetbrains' PSI
- ttoinou 9mo agoWow, I can even chat about C code with that model with LM Studio on my Macbook at 200 tokens per seconds
- kevinlu1248 8mo agoHaha, we never trained it for chat but I would bet it works regardless. Also that's crazy, M4 Mac?
- ttoinou 8mo agoM4 Max 128GB yeah
- leonardcser 9mo agoHi, I tried the model and I am super impressed by the performance/quality. Thanks for making this open source! 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 https://github.com/leonardcser/cursortab.nvim
- lasgawe 8mo agoHey this is really interesting. I'll try your nvim plugin
- treyd 8mo agoIs there a port of this to Emacs or integration with gptel?
- leonardcser 8mo agoHi, not that I know of. Most of the code would not change. It could easily be ported to different editors. The core is the go server (`server/`).
- 9999gold 8mo agoIt seems it would be possible to use this with minuet.el. I’m not familiar with it, though.
- deleted 8mo ago[deleted]
- kevinlu1248 8mo agothis is awesome, i'm going to try this out
- keyle 9mo agoI'm playing around with this in LMStudio (in huggingface -> use this model dropdown -> LMStudio) It's really impressive so far, so quick to respond on a mac mini M2. And it appears to be accurate at least for the obvious questions. I couldn't get it to work as an autocomplete of Zed unfortunately. It looks like it's hardwired to work with some providers and LMStudio is not included in the prediction engines list. Has anyone got a work around?
- kevinlu1248 8mo agoOur hosted autocomplete is coming to Zed in a few weeks.
- woile 9mo agoHey, ollama run as suggested in hf doesn't seem to work with this model. This worked instead: ollama pull hf.co/sweepai/sweep-next-edit-1.5B
- woile 9mo agoI've been using it with the Zed editor and it works quite well! Congrats. This kind of AI are the ones I like and I'm looking to run in my workstation.
- theophaniel 9mo agoCould you give the gist / config on how you made it work with Zed ?
- Imustaskforhelp 9mo ago+1, I wasn't able to make it work on zed either and It would really help if woile can tell how they made it work on their workstation.
- Imustaskforhelp 8mo agoEdit: I asked chatgpt and just tinkered around till I found a setting which could work { "agent": { "default_model": { "model": "hf.co/sweepai/sweep-next-edit-1.5B:latest" } }, "inline_completion": { "default_provider": { "model": "hf.co/sweepai/sweep-next-edit-1.5B" } }, "chat_panel": { "default_provider": { "model": "hf.co/sweepai/sweep-next-edit-1.5B" } } } Then go on the down bottom AI button or that gemini like logo and then select sweep model. And also you are expected to run ollama run command and ollama serve it ollama pull hf.co/sweepai/sweep-next-edit-1.5B ollama run hf.co/sweepai/sweep-next-edit-1.5B I did ask Chatgpt some parts about it tho and had to add this setting into my other settings too so ymmw but Its working for me It's an interesting model for sure but I am unable to get tab auto_completion/inline in zed, I can ask it in summary and agentic mode of sorts and have a button at top which can generate code in file itself (which I found to be what I preferred in all this) But I asked it to generate a simple hello world on localhost:8080 in golang and in the end it was able to but it took me like 10 minutes. But some other things like simple hello world was one shot for the most part It's definitely an interesting model that's for sure. We need stronger model like these I can't imagine how strong it might be at 7B or 8B as iirc someone mentioned that this i think already has it or similar. A lot of new developments are happening in here to make things smaller and I am all for it man!
- _mugencode 9mo agoGreat! I have been trying to do something similar for Clojure. This is a great resource to explore similar approach. https://blog.sweep.dev/posts/oss-next-edit https://blog.sweep.dev/posts/oss-next-edit My notes so far https://kapilreddy.me/notes/2024/11/17/building-clojure-slm-part-1/ https://kapilreddy.me/notes/2024/11/17/building-clojure-slm-...
- notsylver 9mo agoI've been waiting for something like this for ages. Cursor making me pay $20/month when all I use from it is autocomplete was always a little annoying, especially as they changed the UI to push agents more and it got in the way. I was even considering doing it myself but wasn't sure about gambling on models small enough to run locally being smart enough to do anything useful. I threw together a vscode extension to run it and while the extension is rough, the model seems decent. I'm trying to keep my expectations contained, in the past local models have been absolutely terrible for inline completion, this seems much better already. I hope this kicks off more competition.
- kevinlu1248 8mo agoLet me know if you have any questions. We have a lot of harness code that cleans up many bad behaviours that makes it a lot more usable (like token healing: https://blog.sweep.dev/posts/token-healing-autocomplete https://blog.sweep.dev/posts/token-healing-autocomplete).
- dainiusse 8mo agoDo you have anything to share? Would be curious trying it out
- zoobab 9mo agoWhere is the training data? We can't keep calling those models "open source" if we have a black box and know precisely how they were made. "Open weights" are the new binary.
- kevinlu1248 8mo agoWoops meant to say open-weight. We put open-weight in the title and but accidentally wrote open-source in the description.
- bberenberg 9mo agoThis seems great for code, but can this be used for non-code use cases?
- kevinlu1248 8mo agoYes, I've used it to write blog posts / large user-facing copy.
- ajayarama 9mo agoThis is actually a game changer. I’ve been meaning to want to run models to accomplish exactly this, but don’t have enough VRAM on my GPU for the conventional LLM-method for the most part. This seems to be a far more efficient method of accomplishing a more scoped problem. Thank you for making it open source!
- kevinlu1248 8mo agoLet me know if you have any questions! What hardware are you on?
- smusamashah 9mo agoCan this be used offline in Jetbrain IntelliJ? Looking at the plugin, it looks like it requires sign in and then it uses the cloud based model instead of the local one. Can't tell.
- deepsquirrelnet 9mo agoThis is really awesome detail. I’m very impressed by the amount of care taken to identify a good template. I started a small hook to try and do this using DSPy prompt optimizers, but haven’t had a compelling use case to try it with. This seems like an ideal case for trying DFT as well. I’m not sure if you’re using trl, but I’d suggest checking that out.
- kevinlu1248 8mo agoWe're using an internal fork of trl for some of the steps.
- pdyc 9mo agoI dont want to hand edit i want the output of better ai model with edit instructions like //update here <code> // new code <code> insert here etc. and local model read the files and apply the updates. I tried generating patch format but both bigger models fail to generate it accurately and smaller models have hard time in using them. Is there some way to do this with this kind of model? or its for completions while editing only?
- cmrdporcupine 9mo agoI've been trying my hands at implementing an emacs package for inline completions with this. I have it mostly working and performance is good enough but I haven't been blown away by the quality of its suggestions unfortunately. Which I guess is expected from a 1.5B model. I'd love to see them making a larger model in the 10-20b range maybe? I know most people wouldn't be able to run that on their machines, but some could. Running on ollama locally on NVIDIA Spark GB10. Tried it also with vLLM. Pretty fast.
- mijoharas 8mo agoDo you care to share your implementation?
- cmrdporcupine 8mo agoIf I can make it clean and decent I will. I might look at again after work and see if I can tune it up. It was a bit flake and I wasn't blown away by the interaction.
- kevinlu1248 8mo agoAre you using the right format? https://huggingface.co/sweepai/sweep-next-edit-1.5B/blob/main/run_model.py#L30-L103 https://huggingface.co/sweepai/sweep-next-edit-1.5B/blob/mai...
- cmrdporcupine 8mo agoYea, I tweaked it a bunch to try to follow what was described there
- jrop 8mo agoBetween GLM-4.7-Flash and this announcement, THIS is what I'm excited to see in this space: pushing the capabilities of _small_ models further and further. It really feels like we're breaking into a space where models that can run on hardware that I actually own is getting better and better, and that has me excited.
- Semaphor 8mo agoAt 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.
- kevinlu1248 8mo agoIt's a bit undertrained on C#, we'll continue improving on this!
- k9294 8mo agoIs there an oss model for next word / edits predictions for texts in general? e.g. Typing emails?
- dubesar55 8mo agoHas somebody built any vscode extensions for this? Also is anyone serving this model?
- zekejohn 8mo agoNice, could this be used to auto complete terminal/cli commands?