Qwen3.5-9B Abliterated: 0% Refusals + Vision

🔑 Enhanced Key Takeaways

• •The abliterated version uses a two-stage orthogonal projection plus LoRA method to achieve 0% refusals, as detailed in the Hugging Face model card methodology[5].
• •Model size is 6.6GB with a 256K context window, supporting both text and image inputs for local deployment[5].
• •Qwen3.5 series, the base for this model, features hybrid attention architecture like Gated DeltaNet (3 layers linear attention : 1 layer full attention) for efficient edge performance[3].
• •Abliteration technique originates from the 'remove-refusals-with-transformers' method to uncensor models while warning of risks for sensitive outputs[4].

🛠️ Technical Deep Dive

• •Base model: Qwen3.5-9B-Instruct from Alibaba, a compact multimodal model with native vision support and scaled RL training for edge devices[3].
• •Abliteration process: Applies orthogonal projection in two stages combined with LoRA adapters to eliminate refusal behaviors without retraining[1][4].
• •Quantization and size: Deployed at 6.6GB (likely Q4 or similar GGUF quantization), enabling 256K context on consumer hardware like LM Studio or Ollama[4][5].
• •Vision enhancements inherited from Qwen3.5: Supports image understanding with hybrid attention for flat memory usage during long-context multimodal inference[2][3].

🔮 Future ImplicationsAI analysis grounded in cited sources

⏳ Timeline