Instructions to use WithinUsAI/Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use WithinUsAI/Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="WithinUsAI/Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf", filename="Gemma4-Overlooked.Thinker.Uncensored-E2B-Q4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use WithinUsAI/Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf WithinUsAI/Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf WithinUsAI/Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf WithinUsAI/Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf WithinUsAI/Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf WithinUsAI/Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf WithinUsAI/Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf WithinUsAI/Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf WithinUsAI/Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf:Q4_K_M
Use Docker
docker model run hf.co/WithinUsAI/Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use WithinUsAI/Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf with Ollama:
ollama run hf.co/WithinUsAI/Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf:Q4_K_M
- Unsloth Studio new
How to use WithinUsAI/Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for WithinUsAI/Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for WithinUsAI/Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for WithinUsAI/Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf to start chatting
- Pi new
How to use WithinUsAI/Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf WithinUsAI/Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "WithinUsAI/Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use WithinUsAI/Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf WithinUsAI/Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default WithinUsAI/Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use WithinUsAI/Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf with Docker Model Runner:
docker model run hf.co/WithinUsAI/Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf:Q4_K_M
- Lemonade
How to use WithinUsAI/Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull WithinUsAI/Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf:Q4_K_M
Run and chat with the model
lemonade run user.Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf-Q4_K_M
List all available models
lemonade list
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
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. 
⸻
🧬 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) 
⸻
🔓 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) 
⸻
🧠 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) 
⸻
📦 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) 
⸻
🚀 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
⸻
🛠️ 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
⸻
🧪 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 
⸻
📊 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 
⸻
📚 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
⸻
📜 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
⸻
🙏 Acknowledgements
- Google DeepMind (Gemma architecture)
- Open-source GGUF ecosystem
- Research community on alignment & model editing
- Dataset creators across Hugging Face
⸻
🔗 Links
- Model: https://huggingface.co/WithinUsAI/Gemma4-Overlooked.Thinker.Uncensored-E2B.gguf
- Organization: https://huggingface.co/WithinUsAI
⸻
🧩 Closing Note
This model feels like a philosopher with the guardrails quietly removed 🧠🔥
Same brain. Same structure. Just… no instinct to say “no.”
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