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gemma-4-19b-a4b-it-REAP-GGUF
barozp/gemma-4-19b-a4b-it-REAP-GGUF
gemma-4-19b-a4b-it-REAP-GGUF is a text generation model from barozp. Use it when you need the model to write or continue text. The card lists the license as gemma.
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.
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Repo size
161 GB
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3
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Repo makeup
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.gguf161 GB · 100%
At a glance
- Task
- Text Generation
- License
- gemma
- Access
- Public
- Created
- Apr 6, 2026
- Updated
- Apr 6, 2026
- SHA
- 364c9d36
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Base models
Model card
From the Hugging Face model README
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.
- Task
- Text Generation
- License
- gemma
- Languages
- en
- Created
- Apr 6, 2026
- Updated
- Apr 6, 2026