On: StarCoder and StarCoderBase: 15.5B parameter model...

[csdvrx]

Have you tried quantization? It's often a cheap and simple way to reduce the VRAM requirements.

What hardware are you using? (CPU,RAM,GPU,VRAM)

Have you considered using llama.cpp for a mixed CPU+GPU use (if you have enough RAM)

[fbodz → csdvrx]

Yeah I am using the default training script with int8 quantisation. It uses peft with lora but this still requires 26gb

[int_19h → fbodz]

I'm not sure about this model specifically, but training with 4-bit quantization has been a thing with LLaMA for a while now, although the setup involves manual hacks of various libraries.

[freeqaz → fbodz]

Is it possible to offload some layers to CPU and still train in a reasonable amount of time?

[generalizations → freeqaz]

There’s also that pruning tool that was on hn in the last couple weeks. It seemed to work really well on the larger models, and could reduce size by 30-50%

[nl → csdvrx]

You probably don't want to fine-tune a quantized model. They are fine for inference but not great for training.