Firebirth/Qwen3.5-27B-Derestricted
Firebirth/Qwen3.5-27B-Derestricted is a 27 billion parameter causal language model developed by Arli AI, based on the Qwen3.5 architecture. This model is specifically derestricted using Norm-Preserving Biprojected Abliteration to remove refusal behaviors while maintaining or potentially improving the original model's high-performance reasoning capabilities. It supports a context length of 32768 tokens and is optimized for use cases requiring uncensored, high-fidelity language generation and complex problem-solving.
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Qwen3.5-27B-Derestricted: Uncensored Reasoning
This model, developed by Arli AI, is a derestricted version of the powerful Qwen3.5-27B. Its primary innovation lies in its Norm-Preserving Biprojected Abliteration methodology, a technique designed to remove refusal behaviors without degrading the model's core reasoning abilities.
Key Differentiators
- Refusal Removal without "Lobotomization": Unlike standard abliteration methods that can damage a model's learned feature norms, this technique preserves weight magnitudes. This ensures that the model remains logically sound and avoids increased hallucinations often seen in other uncensored models.
- Enhanced Reasoning Potential: By not wasting compute on suppressing outputs, the model may even exhibit improved reasoning capabilities and expose previously hidden knowledge.
- Advanced Abliteration Technique: Utilizes a three-step process (Biprojection, Decomposition, Norm-Preservation) to precisely target and remove refusal components from the directional aspect of weights, while recombining with original magnitudes.
Core Capabilities (inherited from Qwen3.5-27B)
- Unified Vision-Language Foundation: Features early fusion training for strong multimodal understanding across reasoning, coding, agents, and visual tasks.
- Efficient Hybrid Architecture: Incorporates Gated Delta Networks and sparse Mixture-of-Experts for high-throughput inference.
- Scalable RL Generalization: Trained with reinforcement learning across millions of agent environments for robust adaptability.
- Extensive Multilingual Support: Expanded to 201 languages and dialects for global deployment.
- Long Context Window: Natively supports 262,144 tokens, extensible up to 1,010,000 tokens with RoPE scaling.
Ideal Use Cases
This model is particularly suited for applications where unrestricted, high-quality text generation and complex reasoning are critical, without the typical safety-induced refusal behaviors. Developers seeking a powerful, uncensored LLM for diverse tasks, including creative content generation, advanced problem-solving, and agentic workflows, will find this model highly effective.