⚡ PocketWeights: Gemma 4 12B Agentic Abliterated (Uncensored GGUF)
Heavy models, made light. PocketWeights optimizes state-of-the-art open-source LLMs into efficient GGUF formats so you can run unrestricted, powerful AI locally on consumer hardware, gaming laptops, and edge devices.
🧠 About This Model
Huihui-gemma-4-12B-agentic-fable5-abliterated is a high-powered 12-billion parameter model combining Google's advanced Gemma architecture, multi-step agentic planning capabilities, and refusal-removal techniques by huihui-ai.
🎯 Key Highlights of this PocketWeights Edition
- The 12B Reasoning Advantage: Bridges the gap between 8B and 27B+ models. Offers significantly deeper multi-step logic, technical analysis, and structured outputs while remaining lightweight enough for consumer GPUs.
- Agentic Planning & Tool Use: Specifically tuned for autonomous agent workflows, function calling, complex prompt decomposition, and multi-turn goal execution.
- Orthogonally Abliterated: Internal refusal vectors have been systematically neutralized. The model executes complex red-teaming, unrestricted creative writing, and raw analytical tasks without false-positive refusals or preachy lectures.
⚠️ Disclaimer: This model has had its safety guardrails removed. It is intended for authorized security research, red-teaming simulations, and local sandbox environments.
📦 Available Files & Hardware Requirements
| Quant Format | File Size | Recommended VRAM / RAM | Best For |
|---|---|---|---|
| IQ3_M | 5.73 GB | 6 GB VRAM / 8 GB RAM | Ultra-Low Memory Tier. Allows laptops with 6GB GPUs (RTX 3060 Laptop, RTX 4050, GTX 1660 Ti) to run a 12B model at high speed. |
| Q4_K_M | 7.38 GB | 8 GB–12 GB VRAM / 16 GB RAM | The Sweet Spot. Maximum reasoning and agentic fidelity with minimal perplexity loss. Perfect for RTX 3060 12GB, RTX 4060, and Apple Silicon Macs. |
🚀 Beginner's Quick Start Guide
Running this agentic 12B model offline on your machine takes under 2 minutes:
Option 1: LM Studio (Visual GUI — Easiest)
- Download and open LM Studio (Free for Windows, macOS, and Linux).
- Click the Magnifying Glass (Search) icon in the left sidebar.
- Search for:
PocketWeights/Gemma-4-12B-agentic-abliterated-GGUF - Click Download next to Q4_K_M (or IQ3_M if you have limited VRAM), head to the Chat Tab, load the model at the top, and start running!
Option 2: Ollama (Terminal / CLI)
If you already use Ollama, run either tier immediately with a single terminal command:
# Recommended 4-bit standard tier
ollama run hf.co/PocketWeights/Gemma-4-12B-agentic-abliterated-GGUF:Q4_K_M
# Low-VRAM 3-bit tier
ollama run hf.co/PocketWeights/Gemma-4-12B-agentic-abliterated-GGUF:IQ3_M
Option 3: Jan / Kobold.cpp / llama.cpp
Direct Download: Navigate to the Files and versions tab above and download your desired .gguf file.
Load it directly into Jan.ai, Kobold.cpp, Text-Generation-WebUI, or run via llama.cpp:
llama-cli -m Gemma-4-12B-agentic-abliterated-GGUF-Q4_K_M.gguf -p "Plan a multi-step execution pipeline for..." -ngl 40
🤝 Support the PocketWeights Mission
I build, verify, and maintain automated quantization pipelines to deliver high-quality, unrestricted, and hardware-friendly models to the open-source developer community completely for free.
Maintaining conversion clusters, storage, and continuous build pipelines requires ongoing compute resources. If these weights have saved you time, compute overhead, or cloud hosting bills, please consider supporting the project with a small tip!
☕ Donation Options
Buy me a coffee on Ko-fi: ko-fi.com/iamvishalnarayan
Web3 / Crypto (Polygon / ETH):
0x4FC189bf839A89259dd28DE8cD97883c49e15615
Tip: Sending via the Polygon network keeps network gas fees below $0.01!
📄 License & Attribution
Base Model Architecture: Created by Google
Finetune & Abliteration Source: huihui-ai / yuxinlu1
License: Gemma Terms of Use / Open Model License