11 related articles

Research finds uncensored open-source LLMs are measurably more optimistic than base models. This article analyzes how uncensoring changes model personality and the coupling effects of alignment.

Deep dive into Heretic uncensoring technology applied to Jamba2-Mini, Qwen3.5-9B, and 27B open-source models, exploring how refusal rates dropped from 97% to 4% and the safety debates involved.

How a developer ran 4,000 multi-seed abliteration trials to build a 100% HarmBench-compliant uncensored text encoder for Krea 2's Qwen3-VL-4B, with five quantization formats and ComfyUI deployment guide.

A Bilibili creator ran Qwen 122B with 256K context on just 8GB VRAM + 64GB RAM using llama.cpp. Full breakdown of quantization, deployment params, performance, and cost-effective alternatives.

Run Qwen3 35B-A3B uncensored locally on just 6GB VRAM using llama.cpp and GGUF quantization. Full deployment guide, code generation tests, and Agent integration walkthrough.

Meta Muse Spark 1.1 deep dive: native multimodal architecture, platform tools, social data retrieval, e-commerce vision — Meta's first closed-source API model benchmarks against Anthropic Sonnet.

A comprehensive comparison of eight mainstream text-to-image models including Krea2, Flux2, and Qwen Image, covering realistic portraits, Ghibli, 3D anime, and Japanese anime styles.
Boko Haram's Abuse of Frontier AI: How…
Boko Haram is systematically exploiting AI tools for propaganda automation, multilingual recruitment, and operational coordination. An in-depth analysis of generative AI abuse by terror groups, the open-source governance dilemma, and the AI safety arms race.

Hands-on review of Qwythos-9B, distilled from 500M+ Claude reasoning traces. Supports 1.04M token context, uncensored, runs on just 4GB VRAM. Full deployment guide included.

A detailed guide to deploying a multimodal AI Agent on a 3080Ti with 12GB VRAM, covering LLM, STT, TTS, image and video generation module selection, dynamic VRAM loading, and real-world performance.
TutorialsDecode LLM naming conventions, understand 32B parameters & AWQ/GGUF quantization formats, with 4-bit VRAM estimation formulas, MOE model pitfalls, and model selection by GPU tier.