Gemma4-Overlooked.Thinker.Uncensored-E2B (GGUF)
📌 Model Overview
Model Name: WithinUsAI/Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf
Organization: Within Us AI
Base Model: google/gemma-4-E2B-it
Parameter Size: ~5B
Format: GGUF (quantized for local inference)
License: Apache 2.0
This model is an uncensored, refusal-abliterated variant of Gemma 4 E2B, designed for deep reasoning, unrestricted responses, and agentic thinking workflows. It removes refusal behavior while preserving model quality and structure through a mathematically constrained modification process. 
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🧬 Architecture & Lineage
Base Foundation
- Built on Gemma 4, a multimodal model family from Google DeepMind
- Supports:
- Text
- Image
- Audio (E2B class)
- Context window up to 128K tokens (E2B) 
Core Design Philosophy
This model follows a simple but powerful idea:
Don’t make the model bigger… make it think freer.
It retains:
- Native reasoning / “thinking mode”
- Function calling support
- Multilingual capability (140+ languages pretraining) 
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🔓 Uncensoring Method (Abliteration)
This model uses norm-preserving biprojected abliteration, a precise weight-editing technique:
- Identifies a “refusal direction” in activation space
- Removes only that behavioral vector
- Preserves original weight magnitudes
Result:
- Model stays structurally intact
- No brute-force fine-tuning degradation
- Behavior changes without breaking intelligence
📊 Outcomes:
- Refusals reduced from 98% → ~0.4% across datasets
- Minimal quality change (~1.01 response ratio) 
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🧠 Key Capabilities
🔍 Reasoning & Thinking
- Step-by-step internal reasoning
- Long-context coherence
- Analytical and philosophical tasks
🤖 Agentic Behavior
- Tool-calling compatible
- Structured output generation
- Multi-step problem solving
💻 Coding
- Code generation & debugging
- Multi-language support
- SWE-style reasoning workflows
🖼️ Multimodal (Base Capability)
- Image understanding (OCR, charts, UI parsing)
- Video frame reasoning
- Audio (E2B support) 
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📦 GGUF Format & Deployment
Optimized for local inference with:
- llama.cpp
- LM Studio
- Ollama (GGUF-compatible builds)
Typical quantizations:
- Q4_K_M (~3.4GB)
- Q5_K_M (~3.6GB) 
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🚀 Intended Use
✅ Ideal For
- Unrestricted AI experimentation
- Agentic reasoning systems
- Advanced roleplay / creative writing
- Research into alignment & behavior control
- Offline local LLM deployments
⚠️ Considerations
- Responses are not filtered for safety
- May generate content that standard aligned models would refuse
- Requires responsible usage and external guardrails if needed
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🛠️ Usage Example (llama.cpp)
./main -m Gemma4-Overlooked.Thinker.Uncensored-E2B.Q4_K_M.gguf
-p "Design a multi-agent system that debugs its own code."
-n 512
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🧪 Training & Modification Pipeline
Within Us AI methodology includes:
- Activation sampling (harmful vs harmless prompts)
- Statistical clipping (winsorization)
- Directional vector extraction
- Orthogonal projection (Gram-Schmidt)
- LoRA-based weight editing
- Final merge into base weights 
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📊 Evaluation Summary
Metric Result
Refusal Rate ~0.4%
Cross-dataset robustness Verified
Quality degradation Negligible
KL Divergence 0.346
Validated across:
- JailbreakBench
- HarmBench
- Refusal datasets 
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📚 Datasets & Training Sources
Following Within Us AI standards:
- Proprietary datasets created by Within Us AI
- May include third-party datasets (no ownership claimed)
- Focus areas:
- Reasoning traces
- Agentic workflows
- Behavioral evaluation datasets
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📜 License
Apache 2.0 (inherits from base Gemma model)
Additional Notes:
- Base architecture: Google DeepMind (Gemma family)
- Modification process: Within Us AI
- Third-party datasets may be used without ownership claims
- Credit belongs to original dataset and model creators
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🙏 Acknowledgements
- Google DeepMind (Gemma architecture)
- Open-source GGUF ecosystem
- Research community on alignment & model editing
- Dataset creators across Hugging Face
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🔗 Links
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🧩 Closing Note
This model feels like a philosopher with the guardrails quietly removed 🧠🔥
Same brain. Same structure.
Just… no instinct to say “no.”
