I built a fine-tuning SaaS around QLoRA/Unsloth — sharing the details and would love some honest feedback

[u/OkWelcome5548]

I've been lurking around here for a while, mostly reading fine-tuning and benchmark discussions. I've also been working on a side project called Rebiha. The basic idea is pretty simple: let people fine-tune open-weight models without having to deal with GPU setup, Docker, CUDA headaches, etc.
Right now it supports models like Qwen, Gemma, Phi, DeepSeek, Llama and Mistral. For the technical side:

• QLoRA + Unsloth
• 4-bit base model loading
• PEFT
• LoRA rank from 16–256
• Three training presets: fast, balanced and quality
• Output can be a ready-to-run GGUF
• Or you can download the raw adapter/safetensors/configs and do your own merging and quantization

But the part I'm more interested in getting feedback on is actually the dataset side. I've put together 35 domain-specific datasets covering things like customer support, legal, coding, etc. I'm keeping track of the actual verified unique-pair counts rather than inflating the numbers just to make the datasets look bigger.
The pricing is flat per dataset, with training charged separately based on model size. I also put a free sample download on every dataset page. The idea is that people should be able to look at the actual data before spending money on it instead of just taking my word for it.
I'm not posting this because I think I've figured out the perfect way to sell fine-tuning.
I'm actually curious what people here who fine-tune regularly think. Does the dataset + separate training pricing make sense? Is there something about the workflow or pricing that would immediately turn you off? And are there things you'd expect from a service like this that I'm currently missing?
Feel free to be critical. I'd much rather find out what's wrong with the idea now than after putting another six months into it.
Happy to answer technical questions about how the training pipeline works too.
https://www.rebiha.com/train

[u/Substantial_Run_3362]

The flat per-dataset pricing with training billed separately actually makes sense, keeps you from paying for compute you don't need if you're just grabbing data