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Can you expand on this? This is my first time seeing this guy’s work
by ebb_earl_co 3y ago
Can you expand on this? This is my first time seeing this guy’s work
- pbronez 3y agoHe’s the main developer of Llama.cpp, which allows you to run a wide range of open-weights models on a wide range of non-NVIDIA processors.
- fl0id 3y agobut it's all inference, and most of Nvidias moat is in training afaik.
- refulgentis 3y agoPeople have really bizarre overdramatic misunderstandings of llama.cpp because they used it a few times to cook their laptop. This one really got me giggling though.
- ilaksh 3y agoI am integrating llama.cpp into my application. I just went through one of their text generation examples line-by-line and converted it into my own class. This is a leading-edge software library that provides a huge boost for non-Nvidia hardware in terms of inference capability with quantized models. If you don't understand that, then you have missed an important development in the space of machine learning.
- refulgentis 3y agoJeez. Lol. At length: - yes, local inference is good. I can't say this strongly enough: llama.cpp is a fraction of a fraction of local inference. - avoid talking down to people and histrionics. It's a hot field, you're in it, but like all of us always, you're still learning. When faced with a contradiction, check your premises, then share them.
- ilaksh 3y agoThere is an example of training https://github.com/ggerganov/llama.cpp/tree/1f0bccb27929e261744c979bc75114955da49e98/examples/train-text-from-scratch https://github.com/ggerganov/llama.cpp/tree/1f0bccb27929e261... But that's absolutely false about the Nvidia moat being only training. Llama.cpp makes it far more practical run inference on a variety of devices. Including ones with or without Nvidia hardware.