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zeroxjason200/google_gemma-4-26B-A4B-it-assistant
google_gemma-4-26B-A4B-it-assistant is a machine learning model from zeroxjason200. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Q80 Quant of the google assistant model (MTP) for the 26B-A4B. Works with latest llama.cpp (9551). All the other quants uploaded seem to have non matching architecture so failed to load for me [1].
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From the Hugging Face model README
Q8_0 Quant of the google assistant model (MTP) for the 26B-A4B. Works with latest llama.cpp (9551). All the other quants uploaded seem to have non matching architecture so failed to load for me [1].
Gives me 100ts/s up from about 85t/s without MTP (--spec-draft-max 2) on a fresh task.
.38.515.475 I slot print_timing: id 3 | task 0 | prompt eval time = 1147.24 ms / 2419 tokens ( 0.47 ms per token, 2108.55 tokens per second)
0.38.515.479 I slot print_timing: id 3 | task 0 | eval time = 14292.86 ms / 1432 tokens ( 9.98 ms per token, 100.19 tokens per second)
0.38.515.481 I slot print_timing: id 3 | task 0 | total time = 15440.10 ms / 3851 tokens
0.38.515.485 I slot print_timing: id 3 | task 0 | graphs reused = 668
0.38.515.486 I slot print_timing: id 3 | task 0 | draft acceptance = 0.56231 ( 758 accepted / 1348 generated)
0.38.515.506 I statistics draft-mtp: #calls(b,g,a) = 1 674 674, #gen drafts = 674, #acc drafts = 461, #gen tokens = 1348, #acc tokens = 758, dur(b,g,a) = 0.003, 2567.398, 1.100 ms
[1] e.g. stuff like this when you try to load them
0.01.002.728 I srv load_model: [mtmd] estimated worst-case memory usage of mmproj is 1279.96 MiB
0.01.314.746 E llama_model_load: error loading model: unknown model architecture: 'gemma4_mtp'
0.01.314.762 E llama_model_load_from_file_impl: failed to load model
0.01.314.822 W srv load_model: [spec] failed to measure draft model memory: failed to load model