Phi-4-mini-reasoning-GGUF

Phi-4-Mini-Reasoning (GGUF Q4_KM) - Sandlogic Lexicons

Model Summary

Phi-4-Mini-Reasoning is a lightweight open-source model from the Phi-4 family, designed with a strong focus on high-quality, reasoning-dense synthetic data. It has been further fine-tuned for advanced mathematical reasoning tasks and supports a 128K token context length. This model is especially optimized for logic-intensive scenarios while maintaining a compact size, making it ideal for memory and compute-constrained environments.

• Model Family: Phi-4
• Parameter Count: 3.8B
• Architecture: Dense decoder-only Transformer
• Context Length: 128K tokens
• Quantization: GGUF Q4_KM
• Supported Language: English
• Release Date: April 2025
• Cutoff Date: February 2025

Intended Uses

Primary Use Cases

Phi-4-Mini-Reasoning is designed to excel at:

• Multi-step mathematical reasoning
• Formal proof generation
• Symbolic computation
• Solving advanced word problems
• Tasks requiring structured logic and analytical thinking

Its high context length and reasoning capabilities make it suitable for latency-bound applications and deployments on resource-constrained hardware.

Use Case Considerations

• This model is optimized specifically for mathematical reasoning tasks.
• It is not evaluated for general-purpose downstream tasks such as conversational AI or creative writing.
• Developers should:
  • Assess use case suitability.
  • Account for limitations in multi-language support.
  • Evaluate performance, safety, and fairness—especially in high-risk or regulated environments.
  • Ensure compliance with all applicable laws and regulations (e.g., privacy and trade compliance).

Training Details

• Model Architecture: Same as Phi-4-Mini with 3.8B parameters
• Notable Enhancements:
  • 200K vocabulary
  • Grouped-query attention
  • Shared input/output embeddings
• Training Dataset Size: 150B tokens
• Training Duration: 2 days
• Hardware Used: 128 × H100-80G GPUs
• Training Date: February 2024
• Output: Generated text
• Input Format: Text (chat-style prompts recommended)

Integration in Lexicons

This quantized GGUF Q4_KM version of Phi-4-Mini-Reasoning is included in our Sandlogic Lexicons model zoo, making it readily available for efficient inference in edge deployments and research use cases focused on math reasoning.

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For optimal results, we recommend using Phi-4-Mini-Reasoning in tasks that require deep mathematical analysis and structured problem solving.