Then, run opt_proxy.py to compute the proxy loss for a specified quantization method. In a similar manner to opt.py, run opt_saveH.py to save the H matrices resulting from the specified model and quantization method. Run python optq_counter.py to compute the proxy loss of our W,H counterexample. Note OPTQ's implementation requires running on a GPU. The simple options let you put a priority on creating an elegant document that will look good, no matter what device. You can choose from different header types and list options by using the formatting bar that follows you as you write and edit. Run the following script to empirically verify that the output of OPTQ's implementation and our implementation of LDLQ are identical: python optq_ldlq_equiv.py. Quip's formatting bar helps you create simple, beautiful documents. So thats why we search and collect all the latest money-saving coupon codes, promotional codes, discount codes and deals for quip we can find. This argument works with the quantization methods. ![]() We implement a lazy batch update to te weight matrix specified by -lazy_batch. On larger models, a low compute-to-memory-access ratio can slow down the quantization algorithms. To run other OPT models replace opt-125m with one of: opt-350m, opt-1.3b, opt-2.7b, opt-6.7b, opt-13b, opt-30b, etc. The -incoh_processing argument is a meta argument which sets the following flags -pre_gptqH -pre_rescale -pre_proj -qfn b.įor more control into the pre and post processing, these arguments can be set individually. ldlbal_admm: alternative algorithm which constraints the rounded weights to be sufficiently close to their original, giving a better theoretical bound.allbal: algorithm to run greedy updates by themselves, with -npasses the argument controlling the number of passes over the weights. ![]()
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