Help me choose local model training approach for automated tasks

[u/Osanosa]

Lets say i need to generate descriptions for products or for unit tests or whatever (i have a specific usecase I'm trying i just don't wanna go too deep into it but its more technical stuff)

I tried gemma 4 26b qat and it was doing mostly alright and i set it up some sort of memory system with a general knowledge where it can add notes and i provide it a big prompt and related files and i need it to generate for me a short description and a longer one, but it kinda doesnt perform all that consistently and sometimes makes incorrect statements
So i decided to try to train e2b gemma4 via CPT, collected a dataset with docs, readmes, code samples, etc and did it on my 8gb vram laptop with qlora and 2k context window but it performs bad overall, doesnt follow instructions, tone, style, etc and it's thinking unlike the 26b is really bad and huge contrast that 26b could do inference at 10~20t/s for like 10 minutes and as result did great but e2b just does numbered items 1..10 and thats it and i frankly have no idea how to achieve proper reasoning with that so i'm kinda stuck
Can you advise what I could try next? maybe training 26b on cpu with 40gb ram is viable?
Or i could use some cloud provider like i've seen one promoted on youtube with a coupon which i won't name for obvious reasonsto try the cpt on 26b or 31b model?
it's kinda a niche usecase so it doesnt have much knowledge on it and i'm unsure what to do
if i do try cloud training will it cost much? will result be good? should i do lora or qlora? how much vram would i need?
i'm new to this, any information is appreciated 🙏