Qwen3.5-122B Uncensored Release Without Quality Loss

What It Is

HauhauCS has released an uncensored variant of Alibaba’s Qwen3.5-122B model that strips away content filters without the typical side effects. Most uncensored models suffer from degraded reasoning, personality drift, or repetitive outputs - this release claims to avoid those pitfalls entirely.

The model, available at https://huggingface.co/HauhauCS/Qwen3.5-122B-A10B-Uncensored-HauhauCS-Aggressive, underwent weeks of refinement to eliminate refusal responses while preserving the base model’s capabilities. Testing showed zero refusals across 465 prompts with no quality degradation or output looping.

The release includes custom K_P quantizations - a per-model tuning approach that maintains quality in critical areas while reducing file size. The Q4_K_P variant reportedly performs near Q6_K levels while adding only 5-15% to the file footprint, making high-quality inference more accessible on consumer hardware.

Why It Matters

Uncensored models fill a legitimate research and development niche. Security researchers need models that won’t refuse to discuss vulnerabilities. Fiction writers require tools that won’t block creative scenarios. Developers building content moderation systems need models that can analyze problematic content without filtering.

Previous uncensored releases often traded capability for compliance removal. Models would answer previously blocked queries but lose coherence, adopt unwanted personas, or produce lower-quality reasoning. This release demonstrates that removing safety filters doesn’t inherently require sacrificing model intelligence.

The K_P quantization approach represents a shift from one-size-fits-all compression. Traditional quantization methods apply uniform precision reduction across all weights. Per-model tuning identifies which layers and parameters tolerate aggressive compression and which require higher precision, optimizing the quality-to-size ratio for specific architectures.

For teams running inference on limited hardware, Q4_K_P quantization delivering near-Q6_K performance changes deployment economics. A model that previously required 80GB of VRAM might run effectively in 45GB, opening access to researchers and developers without enterprise GPU budgets.

Getting Started

Download quantized versions from https://huggingface.co/HauhauCS/Qwen3.5-122B-A10B-Uncensored-HauhauCS-Aggressive/tree/main - note that HuggingFace’s file browser widget currently displays incomplete file listings due to a bug, but downloads work correctly.

The model includes Qwen’s thinking mode by default. Disable it during inference with:

{"enable_thinking": false}

Standard GGUF-compatible tools work without modification. For llama.cpp:

./main -m qwen3.5-122b-uncensored-q4_k_p.gguf -p "Your prompt here" --ctx-size 4096

LM Studio, Ollama, and other GGUF runtimes load the model like any other quantized release. The K_P quants follow standard naming conventions - Q4_K_P, Q5_K_P, Q6_K_P - allowing direct comparison with traditional quantization levels.

Context

Uncensored models exist in a gray area. While they serve legitimate purposes, they also enable misuse. Researchers and developers deploying these models bear responsibility for their applications and should implement appropriate safeguards in production systems.

Alternative uncensored releases include the Dolphin series and various community fine-tunes of Llama models. Most rely on dataset-based uncensoring - training on refusal-free data to override safety behaviors. This approach often introduces artifacts or capability loss. The HauhauCS release appears to use more targeted intervention, though specific methodology isn’t documented.

Traditional Q4_K quantization typically shows noticeable quality degradation compared to Q6_K, particularly in reasoning tasks. If K_P quantization delivers on its claims across different model families, it could become the new standard for consumer-grade inference. However, these benefits likely vary by architecture - what works for Qwen may not transfer directly to Llama or Mistral variants.

The 122B parameter count sits in an awkward middle ground. Too large for most consumer GPUs without quantization, but smaller than frontier models from OpenAI or Anthropic. For teams needing on-premise inference with strong reasoning capabilities and no content filtering, this release offers a practical option that wasn’t previously available without significant quality compromises.