AEON-7

AEON-7 / Qwen3.6-27B-AEON-Ultimate-Uncensored

Public

Lossless abliteration of Qwen3.6-27B with NVFP4 hardware quantization for DGX Spark / Blackwell. BF16 (51 GB) + NVFP4 (26 GB) deployment guide, docker-compose, and QuickStart.

25
3
69% credibility
Found Apr 25, 2026 at 25 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

This repository offers a modified, uncensored version of the Qwen3.6-27B AI model with instructions to run it on high-end GPU hardware using container setups.

How It Works

1
🔍 Discover the uncensored AI

You hear about a powerful AI brain that's been freed to answer any question without refusing, perfect for deep research or creative ideas.

2
Pick your computer's power level
⚡
Newest hardware

Go for the smaller, speed-optimized version that runs blazing fast on cutting-edge cards.

💪
Older hardware

Use the full-size version that works great on proven high-power cards.

3
📥 Grab the AI files

Download the special AI thinking files from a trusted sharing site to your computer.

4
📋 Follow the simple setup guide

Use the ready-made instructions to prepare everything, like plugging in a new appliance—it takes just a few minutes.

5
▶️ Start your AI assistant

Hit go, and watch as your personal AI comes alive on your powerful machine.

💬 Chat without limits

Ask tough questions, get honest detailed answers, and explore ideas freely—your AI is now super helpful and unrestricted.

Sign up to see the full architecture

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Star Growth

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AI-Generated Review

What is Qwen3.6-27B-AEON-Ultimate-Uncensored?

This repo delivers a fully uncensored take on Alibaba's Qwen3.6-27B multimodal model through lossless abliteration, stripping refusals while keeping capabilities intact—zero refusals on 100 harmful prompts, KL divergence under 0.0005. It offers BF16 weights at 51GB for A100/H100 and NVFP4-quantized 26GB version for DGX Spark and Blackwell hardware, with docker-compose files for one-command vLLM deployment serving OpenAI-compatible /v1/chat/completions endpoints. Plug in your HF token, pull models, run docker compose up, and get tool calling, reasoning mode, and image/video inputs at 30-50 tok/s.

Why is it gaining traction?

Unlike basic uncensoring that trashes reasoning, this achieves github lossless cut via surgical abliteration—enhanced chain-of-thought, adversarial reasoning, and no safety tax drag, like github lossless scaling for LLMs. NVFP4 hardware quantization crushes BF16 throughput on Blackwell without perceptible quality loss, paired with DFlash speculative decoding for production speeds. Devs dig the battle-tested configs, 200K context, and spot-check verified coherence across math, code, and security tasks.

Who should use this?

Security researchers red-teaming vulns or building PoCs, AI alignment teams probing uncensored behaviors, and infra engineers deploying 27B-scale inference on DGX Spark or B100/B200 clusters. Ideal for high-stakes apps needing reliable compliance on edgy prompts without babysitting alignments.

Verdict

Grab it if you have Blackwell-or-later silicon—the NVFP4 path and docker-compose guides make deployment dead simple, despite 25 stars signaling early maturity and a mere 0.7% credibility score. Docs and benchmarks outshine most peers; test on your hardware before prod. (198 words)

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