Hi. I’m using Llama-2 for my project in python with transformers library. There is an option to use quantization on any normal model:
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-2-13b-chat-hf",
load_in_4bit=True,
)
If it’s just a matter of single flag, and nothing is recomputed, then why there is so much already quantized models in the hub? Are they better than adding this one line?
file size which impacts load time:
with load_in_4bit it will download and parse the big file (which is 4x bigger if it is bfloat16, or 8x bigger if it is float32) and then will quantize on the fly,
with pre-quantized files, it downloads only the quants, so expect a 4x to 8x faster load time for 4bit quants