Phi4-mini-Flash Model
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A Phi4-mini-Flash Model is a hybrid language model from Microsoft's Phi-4 family that supports 64K token context and uses the SambaY architecture with Gated Memory Units to deliver efficient long-context reasoning and high inference throughput.
- AKA: Phi-4-mini-Flash-Reasoning Model, Phi4-mini-Flash Language Model.
- Context:
- It can typically achieve state-of-the-art performance among compact models on mathematical reasoning benchmarks like Math500 and AIME24/25.
- It can often provide up to ten-times higher decoding throughput on long prompts compared to its predecessors, due to its efficient hybrid architecture.
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- It can range from being a Base Phi4-mini-Flash Model to being a Fine-Tuned Phi4-mini-Flash Model, depending on its phi4-mini-flash model customization.
- It can range from being a Math-Focused Phi4-mini-Flash Model to being a General-Reasoning Phi4-mini-Flash Model, depending on its phi4-mini-flash model domain.
- It can range from being a Small Phi4-mini-Flash Model to being a Large Phi4-mini-Flash Model, depending on its phi4-mini-flash model size.
- It can range from being an Open-Source Phi4-mini-Flash Model to being a Hybrid Phi4-mini-Flash Model, depending on its phi4-mini-flash model accessibility.
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- It can support Phi4-mini-Flash Model Use Cases such as formal proof generation, multi-hop question answering and on-device reasoning.
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- Example(s):
- Math Benchmark Phi4-mini-Flash Model, where the model achieves high pass@1 accuracy on Math500 and AIME datasets.
- Long Context Phi4-mini-Flash Model, where the model handles 64K token contexts with efficient throughput.
- On-Device Reasoning Phi4-mini-Flash Model, where the model runs on limited hardware to perform complex reasoning tasks.
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- Counter-Example(s):
- General Transformer Model, which may lack the hybrid state-space architecture and long-context efficiency of Phi4-mini-Flash.
- Large Closed Model, which is not open-source and may require more compute.
- Shallow Context Model, which only supports short prompts and cannot handle long reasoning chains.
- See: Hybrid Model, State Space Model, Large Language Model, Mathematical Reasoning Task, Long-Context Processing, Inference Efficiency, Open Model, On-Device AI.