Any way to prune LLMs?
Any way to prune LLMs?
Hey, I'm working on some local LLM applications and my goal is to run the smallest model possible without crippling performance. I'm already using 4 bit GPTQ but I want something smaller. These models have been trained on such a massive amount of data but my specific use case only touches a very very small fraction of that, so I would imagine it's possible to cut away large chunks of the model that I don't care about. I'm wondering if there has been any work on runtime pruning of LLMs (not just static pruning based on model weights) based on "real world" data. Something like: you run the model a bunch of times with your actual data and monitor the neuron activations to inform some kind of pruning process. Does anyone here know about something like that?
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I don't know about that, but you could try GGML (llama.cpp). It has quantization up to 2-bits so that might be small enough.