Merlin-Research/Qwen3.5-4B-Safety-Thinking
Qwen3.5-4B-Safety-Thinking is a 4-billion parameter language model developed by Merlin Research, based on the Qwen/Qwen3.5-4B architecture. It is specifically optimized for structured reasoning, strict instruction adherence, and safety-aligned behavior, with a potential context length of 1M tokens. This model excels in applications requiring robust, predictable, and safe autonomous behavior, leveraging a post-training stack focused on reliable real-world performance. Its core strength lies in enhancing safety reasoning and maximizing controllability for assistant and agentic workflows.
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Model Overview
Merlin-Research/Qwen3.5-4B-Safety-Thinking is a 4-billion parameter model built upon the Qwen/Qwen3.5-4B base, developed by Merlin Research. It is distinguished by its rigorous post-training optimization for safety and reasoning, with a potential context length of 1 million tokens. The model leverages a LoRA-based Supervised Fine-Tuning (SFT) approach, resulting in a merged BF16 checkpoint.
Key Capabilities
- Structured Reasoning Quality: Enhanced ability to perform step-by-step thinking, explicitly supporting the
<think>...</think>format to separate reasoning from final output. - Instruction Adherence: Superior capability to follow complex and strict guidelines provided in prompts.
- Safety-Aligned Behavior: Designed for safe operation in practical assistant and autonomous agent workflows, with increased robustness against misalignment and adversarial inputs.
- Controllability: Optimized for maximizing control over model responses and ensuring consistency.
Training and Data
The model was trained on Merlin Research's private datasets, focusing on reasoning reliability, instruction-following robustness, safety behavior refinement, and misalignment reduction. It incorporates Anthropic’s Bloom&Petri framework for behavioral alignment.
Intended Use Cases
This model is particularly well-suited for:
- Building safety-oriented reasoning assistants and chatbots.
- Tasks demanding strict, constrained instruction-following.
- AI alignment, safety research, and robustness testing.
- Agentic workflows requiring predictable and safe autonomous behavior.