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.

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

SandLogicTechnologies/Phi-4-mini-reasoning-GGUF

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

Model Summary

Intended Uses

Primary Use Cases

Use Case Considerations

Training Details

Integration in Lexicons

SandLogicTechnologies/
Phi-4-mini-reasoning-GGUF