---
license: mit
language:
- en
base_model:
- microsoft/Phi-4-mini-reasoning
pipeline_tag: text-generation
library_name: transformers
tags:
- Reasoning
- phi3
- math
- code
- conversational
---
# 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](https://huggingface.co/SandLogicTechnologies) 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.*