microsoft/Phi-4-reasoning
Microsoft's Phi-4-reasoning is a 14.7 billion parameter dense decoder-only Transformer model, fine-tuned from Phi-4. It is specifically optimized for advanced reasoning tasks in math, science, and coding, leveraging supervised fine-tuning on chain-of-thought traces and reinforcement learning. With a 32k token context length, it excels in scenarios requiring logical deduction and problem-solving, particularly in memory/compute-constrained and latency-bound environments.
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Microsoft Phi-4-reasoning: An Advanced Reasoning Model
Microsoft's Phi-4-reasoning is a 14.7 billion parameter decoder-only Transformer model, building upon the Phi-4 architecture. It has been meticulously fine-tuned using supervised learning on chain-of-thought traces and reinforcement learning, with a strong focus on high-quality data for advanced reasoning in math, science, and coding. The model is designed to produce responses with a distinct reasoning chain-of-thought block followed by a summarization block, guiding users through its problem-solving process.
Key Capabilities
- Enhanced Reasoning: Optimized for complex logical deduction, mathematical problem-solving (e.g., AIME, OmniMath), scientific inquiry (GPQA-Diamond), and algorithmic tasks (3SAT, TSP).
- Code Generation: Demonstrates strong performance in code generation benchmarks like LiveCodeBench and HumanEvalPlus, particularly in Python.
- Efficiency: Designed for use in memory/compute-constrained and latency-bound environments, making it suitable for various deployment scenarios.
- Structured Output: Generates responses with a clear thought process and a concise solution, aiding in understanding and verification.
- Robust Safety: Incorporates extensive safety post-training via supervised fine-tuning and rigorous red-teaming evaluations to mitigate harmful content and biases.
Good For
- Accelerating research in language models and serving as a building block for generative AI features.
- General-purpose AI systems requiring strong reasoning and logic capabilities, primarily in English.
- Applications where detailed, step-by-step reasoning is crucial for problem-solving.
- Developers seeking a capable model for math, science, and coding tasks that can operate efficiently.