barozp/gemma-4-19b-a4b-it-REAP-GGUF

gemma-4-19b-a4b-it-REAP — GGUF Quantizations

GGUF quantizations of 0xSero/gemma-4-19b-a4b-it-REAP, a 30% expert-pruned variant of google/gemma-4-26b-a4b-it using the REAP (Router-weighted Expert Activation Pruning) method.

Available Files

File	Quant	Size	BPW	Description
gemma-4-19b-a4b-it-REAP-BF16.gguf	BF16	~36 GB	16.0	Full precision, for re-quantization
gemma-4-19b-a4b-it-REAP-Q8_0.gguf	Q8_0	~19 GB	8.0	Near-lossless, large file
gemma-4-19b-a4b-it-REAP-Q6_K.gguf	Q6_K	~15 GB	6.56	Near-lossless, recommended for high quality
gemma-4-19b-a4b-it-REAP-Q5_K_M.gguf	Q5_K_M	~13 GB	5.68	High quality, larger size
gemma-4-19b-a4b-it-REAP-Q5_K_S.gguf	Q5_K_S	~12 GB	5.52	High quality, slightly smaller
gemma-4-19b-a4b-it-REAP-Q4_K_M.gguf	Q4_K_M	~12 GB	4.89	Recommended — best quality/size balance
gemma-4-19b-a4b-it-REAP-Q4_K_S.gguf	Q4_K_S	~11 GB	4.63	4-bit small
gemma-4-19b-a4b-it-REAP-Q3_K_L.gguf	Q3_K_L	~9.5 GB	4.27	3-bit large
gemma-4-19b-a4b-it-REAP-Q3_K_M.gguf	Q3_K_M	~9 GB	3.91	3-bit medium
gemma-4-19b-a4b-it-REAP-Q3_K_S.gguf	Q3_K_S	~8.5 GB	3.66	3-bit small
gemma-4-19b-a4b-it-REAP-Q2_K.gguf	Q2_K	~7.5 GB	2.96	Smallest size, lowest quality

Model Details

Property	Value
Architecture	Gemma 4 (hybrid sliding/full attention MoE)
Parameters	19.02B total / ~4B active per token
Experts	90 total / 8 active per token
Context Length	262,144 tokens
Original dtype	BF16
Quantization tool	llama.cpp
License	Gemma

Quantization Process

# 1. Convert BF16 SafeTensors → GGUF
python convert_hf_to_gguf.py 0xSero/gemma-4-19b-a4b-it-REAP \
  --outfile gemma-4-19b-a4b-it-REAP-BF16.gguf \
  --outtype bf16

# 2. Quantize (example: Q4_K_M)
llama-quantize gemma-4-19b-a4b-it-REAP-BF16.gguf \
  gemma-4-19b-a4b-it-REAP-Q4_K_M.gguf Q4_K_M

Usage

llama.cpp

llama-cli \
  -m gemma-4-19b-a4b-it-REAP-Q4_K_M.gguf \
  -ngl 99 -c 4096 \
  -p "Your prompt here"

llama-server (OpenAI-compatible API)

llama-server \
  -m gemma-4-19b-a4b-it-REAP-Q4_K_M.gguf \
  -ngl 99 -c 4096 \
  --port 8080

LM Studio / Jan / Ollama

Download the .gguf file and load it directly in your preferred local inference UI.

Hardware Requirements

Config	VRAM / RAM
Full GPU (Q4_K_M, recommended)	14+ GB VRAM
Hybrid CPU+GPU (Q4_K_M)	8 GB VRAM + 8 GB RAM
CPU only (Q4_K_M)	16+ GB RAM

About the Original Model

0xSero/gemma-4-19b-a4b-it-REAP applies REAP expert pruning (arXiv:2510.13999) to remove 30% of MoE experts (38 of 128 per layer) from Gemma 4 26B-A4B-it, while preserving routing behavior. Active parameters per token remain unchanged at ~4B. The result is a ~31% smaller model with near-identical generation quality across coding, math, and reasoning benchmarks.

License

Gemma — see Google's Gemma Terms of Use.