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196 points zmccormick7 | 3 comments | | HN request time: 0s | source
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koakuma-chan ◴[] No.45387475[source]
Has anyone tried making coding agent LoRas yet, project-specific and/or framework-specific?
replies(1): >>45387500 #
CardenB ◴[] No.45387500[source]
I know it isn’t your question exactly, and you probably know this, but the models for coding assist tools are generally fine tunes of models for coding specific purposes. Example: in OpenAI codex they use GPT-5-codex
replies(2): >>45387608 #>>45392185 #
neutronicus ◴[] No.45387608[source]
I think the question is, can I throw a couple thousand bucks of GPU time at fine-tuning a model to have knowledge of our couple million lines of C++ baked into the weights instead of needing to fuck around with "Context Engineering".

Like, how feasible is it for a mid-size corporation to use a technique like LoRA, mentioned by GP, to "teach" (say, for example) Kimi K2 about a large C++ codebase so that individual engineers don't need to learn the black art of "context engineering" and can just ask it questions.

replies(1): >>45387739 #
pu_pe ◴[] No.45387739[source]
I'm curious about it too. I think there are two bottlenecks, one is that training a relatively large LLM can be resource-intensive (so people go for RAGs and other shortcuts), and making it finetuned to your use cases might make it dumber overall.
replies(1): >>45392175 #
1. koakuma-chan ◴[] No.45392175[source]
> making it finetuned to your use cases might make it dumber overall.

LoRa doesn't overwrite weights.

replies(1): >>45394380 #
2. pu_pe ◴[] No.45394380[source]
Do you need to overwrite weights to produce the effect I mentioned above?
replies(1): >>45399429 #
3. koakuma-chan ◴[] No.45399429[source]
Good point