4 ms·

You can run llama using 4 bits per parameter, 64 GB of RAM is more than enough

• geysersam 4y ago
  4 bits is ridiculously little. I'm very curious what makes these models so robust to quantization.
  • MacsHeadroom 4y ago
    Read The Case for 4 Bit Precision. https://arxiv.org/abs/2212.09720 https://arxiv.org/abs/2212.09720 Spoiler: it's the parameter count. As parameter count goes up, but depth matters less. It just so happens that at around 10B+ parameters you can quantize down to 4bit with essentially no downsides. Models are that big now. So there's no need to waste RAM by having unnecessary precision for each parameter.
    • Taek 4y ago
      For completeness, there's also another paper that demonstrated you get more power/accuracy per-bit at 4 bits than at any other level of precision (including 2 bits and 3 bits)
      • MacsHeadroom 4y ago
        That's the paper I referenced. But newer research is already challenging it. 'Int-4 llama is not enough [0] - Int-3 and beyond' suggests 3-bit is best for models larger than ~10B parameters when combining binning and GPTQ. [0] https://nolanoorg.substack.com/p/int-4-llama-is-not-enough-int-3-and https://nolanoorg.substack.com/p/int-4-llama-is-not-enough-i...