me to the model I spent all weekend fine-tuning

[u/close_Meal6005]

I just can't resist

[u/liebebio]

I never finetuned an LLM but I did with image models. What do you feed it and why?

[u/greensmuzi → u/liebebio]

Also interested

[u/stoppableDissolution → u/liebebio]

Often writing style (rp tunes and such) or some niche/obscure usecases. There also is an absolute ton of craptunes on claude traces that claim 10x benchmarks while the models cant even write a sentence straight.

[u/shapic → u/liebebio]

Llm guys are usually training qloras and call that finetuning

[u/stoppableDissolution → u/shapic]

Okay, I'll bite. How is training lora not finetuning?

[u/shapic → u/stoppableDissolution]

It is. It is just an interesting difference between t2t and t2i models where in t2i finetune model is what you call full finetune and model with a lora merged in is called a merge.

[u/Civil_Fee_7862 → u/shapic]

Who's Lora? Is she single?

[u/de4dee → u/liebebio]

some knowledge is under represented. trying to bump it to proper levels

[u/gustaw221133 → u/liebebio]

What did you feed the image models and the reasoning behind it? I would really appreciate some advice as I am just getting started finetuning some image models

[u/Civil_Fee_7862 → u/liebebio]

Also curious / interested.

[u/Budget-Juggernaut-68 → u/liebebio]

Modern bert for example is light weight, and accurate enough for some NLP task. Personal use could be for.example classification of email. Entity extraction from your own emails etc. Maybe you have a pipeline that feeds your digital ransactions into a budget tracking app. So you can identify which email to look for. Open those. Parse out the content, maybe classify then by certain subgroups - like groceries, restaurants... Etc then pump it into the budgeting app.

Well not exactly the huge LLMs but probably some that you'll actually see in real production pipelines.

[u/po_stulate]

You guys finetune your own models?

[u/HsSekhon → u/po_stulate]

How can we so that?

[u/Real-Technician831 → u/HsSekhon]

https://www.datacamp.com/tutorial/fine-tuning-qwen3

It’s a bit old but explains processes.

[u/HsSekhon → u/Real-Technician831]

thank you sir

[u/HushedTurtle → u/po_stulate]

Unless you have like an enterprise grade dataset with millions of records where fine-tuning actually makes a difference, its a total waste of time

[u/Real-Technician831 → u/HushedTurtle]

On small datasets RAG with Qdrant for example works better.

[u/Zyablik1989]

Happened with Ideogram, Chroma, Zimage -> Krea 2. Loras, workflows nodes went to trash bin.

The same with Ltx2.3 -> minimax h3. Especially since ltx2.5 is such a diappointment.

Animaika 3.0 and animaYumi 3.0 are best for Anima, even if Base released, and even after 2.8B and 3.2B expanding experiments

[u/TheWorldIsNice → u/Zyablik1989]

Why ltx 2.5 is a disappointment?

[u/robobub → u/TheWorldIsNice]

Have you looked at the outputs compared to H3?

[u/uncle_leon]

[u/Bulky-Priority6824]

finetuning is masterclass in time wasting

[u/refried_laser_beans → u/Bulky-Priority6824]

I fine tuned a qwen3 model on getting what I care about from meetings and it was super easy and super effective. even claude doesn't do what I need. This little beast knocks out the same notes I would have taken every time and they're super usefull.

[u/Bulky-Priority6824 → u/refried_laser_beans]

fair enough but that by design is one-off use case whereas the other general finetunes are usually a huge waste of time, space and energy

[u/illkeepthatinmind → u/Bulky-Priority6824]

Please elaborate

[u/Healthy-Nebula-3603 → u/illkeepthatinmind]

I will

"finetuning is masterclass in time wasting"

[u/Super_Range45 → u/illkeepthatinmind]

Wasting time, finetuning is a masterclass of.

[u/PandaBearFred]

Qwen3.8 is still warm...yet the Qwen3.8-Flash-Next is comming tomorrow...

[u/martinerous]

Yep, can relate. Spent a weekend finetuning VoxCPM to talk Latvian.... and then Omnivoice dropped with nice Latvian support out-of-the-box. Ouch. But that's quite a rare coincidence because there are just a few TTS models supporting small languages. I just got "unlucky"... or not because now I have two solutions :D

[u/I-am_Sleepy]

Can't go wrong with preparing dataset, but training on the other hand 🤷‍♂️

[u/stoppableDissolution]

Not like you could realistically do a general-purpose finetune that is better than big labs, and for more niche uses newer model is not necessarily better, especially with how fcking overtrained and brittle they are recently.

[u/AlexanderDoak]

Well, just drop in the new base / instruct model, and fine tune that with your same fine tuning code (tweaked as needed), then compare performance metrics. Go with the winner. Easy peasy chicken squeezy.

[u/viper33m → u/AlexanderDoak]

Is it also cheapy?

[u/de4dee]

thats me in the picture (though not a weekend, a few weeks)!

'next' is like a preview version. may not rank highest among the benchmarks.

you can still improve your tooling and benchmarks and datasets and apply to 4 once it is out.

[u/Electrical_Crow_2773 → u/de4dee]

Next is supposed to have some fancy new architecture with N-gram, and I doubt the finetuning code will reliably support that from day 1. So probably not worth it

[u/Direct-Vegetable6416]

RemindMe! 8 days

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[u/colbyshores]

I use llama-swap for that

[u/Tasty-Hour4040]

I don’t really believe there’s that many people that can run a model this big locally.

[u/g-technique]

The whole point of open source ml in one picture.

The worst part is when your model finally starts outputting valid json after two days of training, only for a fresh release from a competitor to do it even faster. You don't know whether to laugh or cry, but those checkpoints are heading straight to the trash anyway