add python/pytorch version compat notes#44
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ggerganov merged 1 commit intoggml-org:masterfrom Mar 12, 2023
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44670
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Aug 2, 2023
* RAM usage reduction and calculations Removed -b batch limit (1024) (tested up to-b 8192) Fixed a integer overflow in ggml matmul (happened at around nbatch 3000) Added a dynamic calculation for batched scratch memory consumption Overall reduced RAM buffer sizes by magnitudes for normal settings RAM usage scales quadratically with increasing context size * batch Using a small batch (or default 1) will result in a very small memory footprint even at thousands of tokens processed Tested up to 13,000 tokens prompt and 8k batch Needs more tests on various platforms * removed debug * minor ---------
SamuelOliveirads
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Dec 29, 2025
* Adding iq1_tn - 1.6875 bpw for TriLM ternary models * iq1_tn: NEON * iq1_tn: faster NEON * iq2_bn: improve performance on NEON We now get TG-128 = 100 t/s for Bitnet-3B-1.58b! * iq1_tn: improve AVX2 PP-512 goes to 533 t/s up from 455. TG-128 @ 2 threads goes to 16.6 t/s up from 14.2. However, we seem to have a bottleneck somewhere as TG saturates at 8 threads. * iq1_tn: improve Zen4 PP-512 goes to 485 t/s up from 352. With FA we get 545 t/s up from 380. TG-128 @ 1 thread goes to 12.4 t/s up from 10.4. However, we seem to have a bottleneck somewhere as TG saturates at 8 threads. * iq2_bn: improve on Zen4 We now get PP-512 = 614 t/s up from 542 t/s * iq2_bn: improve AVX2 implementation We now get PP-512 = 753 t/s up from 680 t/s. * Remove unnecessary barrier in ggml_compute_forward_mul_mat --------- Co-authored-by: Iwan Kawrakow <[email protected]>
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see #32 (comments)