DJLougen / gemma-4-E2B-it-saber-GGUF

huggingface.co
Total runs: 169
24-hour runs: -1
7-day runs: -274
30-day runs: -273
Model's Last Updated: April 15 2026

Introduction of gemma-4-E2B-it-saber-GGUF

Model Details of gemma-4-E2B-it-saber-GGUF

gemma-4-E2B-it-saber — GGUF

GGUF quantizations of DJLougen/gemma-4-E2B-it-saber , a surgically-modified version of google/gemma-4-E2B-it using SABER (Spectral Analysis-Based Entanglement Resolution) that removes safety refusal behavior while preserving model capability.

Quantizations
File Quant Size Description
gemma-4-E2B-it-saber-BF16.gguf BF16 8.7 GB Full precision (brain float 16)
gemma-4-E2B-it-saber-Q8_0.gguf Q8_0 4.7 GB Near-lossless
gemma-4-E2B-it-saber-Q6_K.gguf Q6_K 3.6 GB Very high quality
gemma-4-E2B-it-saber-Q5_K_M.gguf Q5_K_M 3.4 GB High quality
gemma-4-E2B-it-saber-Q5_K_S.gguf Q5_K_S 3.4 GB High quality, smaller
gemma-4-E2B-it-saber-Q4_K_M.gguf Q4_K_M 3.2 GB Good quality — recommended
gemma-4-E2B-it-saber-Q4_K_S.gguf Q4_K_S 3.2 GB Good quality, smaller
gemma-4-E2B-it-saber-IQ4_XS.gguf IQ4_XS 3.1 GB Good quality, compact
gemma-4-E2B-it-saber-Q3_K_L.gguf Q3_K_L 3.1 GB Lower quality
gemma-4-E2B-it-saber-Q3_K_M.gguf Q3_K_M 3.0 GB Low quality
gemma-4-E2B-it-saber-IQ3_M.gguf IQ3_M 3.0 GB Medium-low, IQ
gemma-4-E2B-it-saber-Q3_K_S.gguf Q3_K_S 2.9 GB Low quality, small
gemma-4-E2B-it-saber-Q2_K.gguf Q2_K 2.8 GB Very low quality
gemma-4-E2B-it-saber-mmproj-BF16.gguf BF16 942 MB Vision/audio encoder (multimodal projector)
Multimodal Usage

This is a multimodal model. To use vision/audio features with llama.cpp, you need both a text model GGUF and the mmproj file:

llama-cli -m gemma-4-E2B-it-saber-Q4_K_M.gguf --mmproj gemma-4-E2B-it-saber-mmproj-BF16.gguf -p "Describe this image" --image photo.jpg
Method Overview

SABER identifies and ablates the refusal circuit in an LLM through a multi-stage process:

  1. Probing — Extract activation profiles from both harmful and harmless inputs across all transformer layers
  2. Spectral Analysis — Decompose activation differences into individual refusal directions
  3. Entanglement Quantification — Measure overlap between refusal and capability subspaces
  4. Targeted Ablation — Remove only pure-refusal components
  5. Iterative Refinement — Re-probe after each pass to catch hydra effects
Results
Model Refusal Rate Perplexity
google/gemma-4-E2B-it (baseline) 100% 498
SABER-refined (this model) 0% 450 (-9.6%)
Warning

This model will comply with any request, including harmful ones. It is intended solely for research into alignment, safety, and model behavior.

Runs of DJLougen gemma-4-E2B-it-saber-GGUF on huggingface.co

169
Total runs
-1
24-hour runs
-285
3-day runs
-274
7-day runs
-273
30-day runs

More Information About gemma-4-E2B-it-saber-GGUF huggingface.co Model

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https://huggingface.co/DJLougen/gemma-4-E2B-it-saber-GGUF

DJLougen gemma-4-E2B-it-saber-GGUF online free

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https://huggingface.co/DJLougen/gemma-4-E2B-it-saber-GGUF

gemma-4-E2B-it-saber-GGUF install

gemma-4-E2B-it-saber-GGUF is an open source model from GitHub that offers a free installation service, and any user can find gemma-4-E2B-it-saber-GGUF on GitHub to install. At the same time, huggingface.co provides the effect of gemma-4-E2B-it-saber-GGUF install, users can directly use gemma-4-E2B-it-saber-GGUF installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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DJLougen
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