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zackliqcom/
gemma4-E2B-Q40-custom

Name: gemma4-E2B-Q40-custom
Availability: InStock

transformers

gguf

any-to-any

google/gemma-4-E2B-it-qat-q4_0-unquantized

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Hugging Face | GitHub | Launch Blog | Documentation
License: Apache 2.0 | Authors: Google DeepMind

Gemma 4-E2B

This is a custom quantized version of the Gemma 4-E2B model, quantized to Q4_0 with custom OVERRIDE file. It is designed to achieve fast inference on Qualcomm Hexagon NPU while maintaining adequate accuracy.

how model is generated

Built with llama.cpp commit 7c158fb.

Three steps, run from the repo root.

Step 1 — download the unquantized HF model

hf download google/gemma-4-E2B-it-qat-q4_0-unquantized --local-dir ./hf-model

Step 2 — convert HF → F16 GGUF

convert_hf_to_gguf.py ./hf-model --outfile model-f16.gguf --outtype f16

Step 3 — follow the OVERRIDE file and quantize to Q4_0

bash

build/bin/llama-quantize --tensor-type-file <override-file> \
    model-f16.gguf model-q4_0-override.gguf q4_0

Performance Measurement Commands

CPU uses --device none -ngl 0; HTP uses --device HTP0 -ngl 99. For each (model, backend, CTX ∈ {512, 1024, 4096}) two llama-bench runs were issued — one for prefill, one for decode:

# environment on device
export LD_LIBRARY_PATH=./lib
export ADSP_LIBRARY_PATH=./lib

# Prefill (Prefill tok/s; TTFT = CTX / Prefill × 1000)
./bin/llama-bench --device <none|HTP0> -m <model.gguf> \
  --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 --ubatch-size 1024 -fa on \
  -ngl <0|99> -p <CTX> -n 0

# Decode at depth = CTX (Decode tok/s)
./bin/llama-bench --device <none|HTP0> -m <model.gguf> \
  --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 --ubatch-size 1024 -fa on \
  -ngl <0|99> -p 0 -n 128 -d <CTX>

Performance Metrics

Performance on IQ9 (QCS9075M)

Compute	CTX	Unsloth PTQ GGUF	Google QAT GGUF	Ours
CPU	512	82.2 / 22.25	107.2 / 22.51	98.9 / 23.78
CPU	1024	76.6 / 21.65	97.8 / 21.94	90.8 / 23.16
CPU	4096	61.3 / 18.09	76.5 / 18.26	72.3 / 19.10
HTP	512	273.2 / 19.22	582.5 / 16.40	582.4 / 18.33
HTP	1024	266.9 / 18.93	552.3 / 16.45	552.2 / 18.17
HTP	4096	254.9 / 18.30	502.5 / 15.87	501.8 / 17.53

Accuracy Metrics

The MMLU-Pro is measured:

Subject	Unsloth PTQ GGUF	Google QAT GGUF	Ours
mmlu_pro	0.4084	0.4178	0.4131
biology	0.6248 ± 0.0181	0.6262	0.6248 ± 0.0181
business	0.4956 ± 0.0178	0.5019	0.4740 ± 0.0178
chemistry	0.3021 ± 0.0137	0.3127	0.3004 ± 0.0136
computer_science	0.4683 ± 0.0247	0.4854	0.4683 ± 0.0247
economics	0.4538 ± 0.0171	0.4976	0.4431 ± 0.0171
engineering	0.2859 ± 0.0145	0.3375	0.2931 ± 0.0146
health	0.3961 ± 0.0171	0.3423	0.3985 ± 0.0171
history	0.2730 ± 0.0229	0.2310	0.3097 ± 0.0237
law	0.2416 ± 0.0129	0.2289	0.2480 ± 0.0130
math	0.6366 ± 0.0131	0.6366	0.6373 ± 0.0131
other	0.3561 ± 0.0158	0.3528	0.3431 ± 0.0156
philosophy	0.3387 ± 0.0212	0.3848	0.3487 ± 0.0214
physics	0.3580 ± 0.0133	0.4003	0.3926 ± 0.0136
psychology	0.4875 ± 0.0177	0.5113	0.5013 ± 0.0177

License

Apache 2.0

zackliqcom/gemma4-E2B-Q40-custom