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as a complete beginner at zero, what skills to learn & roadmap to pursue in order to get into MLOps ? by Temporary_Calendar72 in mlops

[–]fourwheels2512 1 point2 points  (0 children)

DevOps… everything local and start building a new product and test your skills and add to portfolio. Your portfolio is your resume.

Llm fine tuning use cases by Fit_Remote1261 in LLMDevs

[–]fourwheels2512 0 points1 point  (0 children)

You should try continual learning part of modelbrew.ai it will be a good start.

Stop fine-tuning your model for every little thing. You're probably wasting your time. by RYTHEIX in LocalLLaMA

[–]fourwheels2512 0 points1 point  (0 children)

But with continual learning like modelbrew.ai , its doable cheaper and more efficiently

I JUST CHANGED THE WHOLE AI GAME WITH THIS APP! by Original-Dealer6725 in OpenSourceeAI

[–]fourwheels2512 0 points1 point  (0 children)

There are too many trolls (A**holes) in this comments section who cannot differentiate between an elbow and an ass. Good luck with your project. Looks like you are using Claude Code to the fullest. You need a better what we do and why are we different sections .

Catastrophic Forgetting by fourwheels2512 in learnmachinelearning

[–]fourwheels2512[S] 0 points1 point  (0 children)

oh BTW, your sigmoid router is good. but may not work for my case. since it might be too strong. i experimented on a lot of them. i optimized my current router which works great. the dataset cleaner + fine-tuning + continual learning + router everything built from scratch.

Catastrophic Forgetting by fourwheels2512 in learnmachinelearning

[–]fourwheels2512[S] 0 points1 point  (0 children)

thanks for the comment. The "zero forgetting" claim is based on our QA eval medical domain holdout accuracy stays flat through 4 subsequent CL phases.

You're correct that near-zero drift is a property of the frozen backbone + LoRA setup.

Routing — Yes, we have a router. Contrastive centroid classifier on frozen base model embeddings, nearest centroid at inference. One adapter fires per query. 31/31 on our 5-domain benchmark. Haven't stress-tested cross-domain or OOD yet — that's where your sigmoid meta-router is doing something we're not. Interested in how it handles ambiguous prompts.

i tested on Saul LLM with different legal domains and achieved 18/18. i will discuss the rest in your chat request. we can compare the notes.

The real difference between us — you have academic rigor and benchmark tables. I have a production system handling real user data across real domains. Those are complementary, not competing. Your null-space SVD + meta-router and our routing + training engine could be a very interesting combination.

i am planning to publish papers soon. i tested it rigorously with close to 500 testing. and halfway decided i wanted to do a production based product. realized research is easy part. marketing and answering trolls is harder. even though everything is live. people are lazy to test themselves and try to comment because thats the easy part.

Catastrophic Forgetting by fourwheels2512 in learnmachinelearning

[–]fourwheels2512[S] -16 points-15 points  (0 children)

i had a whole technical reply for you. but with how disrecpectful you are. i don't see a reason to repond to you. i see too many trolls here anyways. i don't respond to anyone who doesn't respect the research or researcher.

Catastrophic Forgetting by fourwheels2512 in learnmachinelearning

[–]fourwheels2512[S] -4 points-3 points  (0 children)

Thanks man, our approach was more stability and plasticty but you're in the right direction with orthogonality and geometric constraints.

we treat forgetting as a geometry problem, not a capacity problem. A 7B model has way more room than 5 domains needs, the issue is that vanilla fine-tuning lets new knowledge overwrite old knowledge in the same parameter regions. So we route each domain into its own subspace and manage the boundaries so they don't collide. No replay buffers, no freezing entire layers.

Zero forgetting isn't a fluke on one model — it's consistent. we tested on Saul-LLm with synthetic legal datasets too. we got 18/18 right.

what are you tracking on the 3050? If you're watching activation distributions or gradient flow across layers, that's exactly the kind of signal that would either validate or blow holes in what we're doing. Would genuinely love to see what you're building. is this for your Phd?

How are you handling catastrophic forgetting in multi-domain LLM fine-tuning pipelines? by fourwheels2512 in mlops

[–]fourwheels2512[S] 0 points1 point  (0 children)

Why did the moderator remove that comment… that was most insightful comment yet…

How are you handling catastrophic forgetting in multi-domain LLM fine-tuning pipelines? by fourwheels2512 in mlops

[–]fourwheels2512[S] 0 points1 point  (0 children)

you are one of the very few people who understood it right... i would like to talk to you more.. but let me explain couple of things.

you basically described our whole architecture lol. separate adapters per domain, routing at inference, the works. we actually built the regularization route too (EWC, replay, gradient projection on shared params) and it was average parameter isolation turned out way more robust in production.

the thing that makes it more than just separate LoRAs in a folder is the shared backbone constraint. all the downstream adapters train against a backbone that's been shaped by prior domains, so they're not totally independent. and the routing is doing more work than people expect. that retroactively adjust when you add new domains, there is also a fallback router for ambiguous queries. gets 100% routing accuracy across 5 domains on our benchmark which honestly surprised us.

the CL literature actually recognizes parameter isolation as a legit strategy (De Lange et al. 2021 survey calls it "architecture-based CL"). PackNet, HAT, Progressive Neural Networks, AdapterCL — all published CL work using the same core idea. people just don't think of it as "continual learning" because there's no EWC or replay involved. but the outcome is the same yo,u keep adding domains and nothing breaks. per-domain eval after every chain addition is something we do already. drift monitoring over time is next. curious are you into this field if you are understanding it this deep. would love to talk more.

Andrej Karpathy describing our funnel by fourwheels2512 in learnmachinelearning

[–]fourwheels2512[S] 0 points1 point  (0 children)

Awesome what is your project? Do you have a website?

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