Michael-Kozu/Deimos-A4
Deimos-A4 by Michael-Kozu is a 4.66 billion parameter causal language model built on Qwen3.5-4B, specialized for complex reasoning and hard mathematical tasks. It features an internal terse, concise chain-of-thought mechanism, resulting in approximately 60% fewer tokens and 36% faster inference compared to its base model. This model excels in multi-step proofs and long algebraic chains, achieving significant accuracy gains on benchmarks like leaderboard_math_hard and minerva_math500.
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Overview
Michael-Kozu's Deimos-A4 is a 4.66 billion parameter model, fine-tuned from Qwen3.5-4B, specifically designed for advanced reasoning and complex mathematical problems. It employs a unique internal "thinking" mechanism, generating compact, fragment-style reasoning within <think>...</think> blocks before producing a clean, professional user-facing response. This approach leads to substantial efficiency gains, with approximately 60% fewer tokens and 36% faster inference compared to the base Qwen3.5-4B model, while significantly improving accuracy on challenging math benchmarks.
Key Capabilities & Differentiators
- Concise Reasoning: Internally generates terse, compact chain-of-thought, which is then expanded into a polished output, never exposing the internal fragments to the user.
- Enhanced Math Performance: Achieves an average of +40 points accuracy on hard math tasks (AIME, MATH-hard, MATH-500) compared to its base model.
- Token and Speed Efficiency: Demonstrates up to 77% token reduction and 36-47% faster inference times on hard math problems.
- Specialized Training: Trained via length-biased rejection sampling on 4,338 "shortest-correct" traces, teaching the model to optimize for logical structure while minimizing verbosity.
When to Use Deimos-A4
- Hard Math & Reasoning: Ideal for multi-step proofs, long algebraic chains, and complex problem-solving where the base model's reasoning capacity might be insufficient.
- Code Generation: Effective for tasks requiring structured logical thought.
- Optimized Inference: When token efficiency and faster response times are critical for reasoning-heavy applications.
Limitations
- Not a Generalist: Shows knowledge regression on general knowledge recall (MMLU) and strict instruction-following tasks (IFEval) compared to the base model. For these, the base Qwen3.5-4B is recommended.
- Internal Thinking Only: The model's compact reasoning style is designed to remain internal; stripping the chat template or forcing generation outside the
<think>block may lead to suboptimal results. - 4B Pretraining Ceiling: Its underlying knowledge is limited by the Qwen3.5-4B base, focusing on reasoning depth rather than broad knowledge expansion.