Phi-4-mini-GGUF

<p align="center"> <img src="https://raw.githubusercontent.com/Worth-Doing/brand-assets/main/png/variants/04-horizontal.png" alt="worthdoing" width="400"/> </p> <p align="center"><strong>Author: Simon-Pierre Boucher</strong></p> <p align="center"> <img src="https://img.shields.io/badge/Format-GGUF-blue?style=for-the-badge" alt="GGUF"/> <img src="https://img.shields.io/badge/Params-3.8B-orange?style=for-the-badge" alt="Parameters"/> <img src="https://img.shields.io/badge/Platform-Apple_Silicon-black?style=for-the-badge&logo=apple" alt="Apple Silicon"/> <img src="https://img.shields.io/badge/License-Apache_2.0-green?style=for-the-badge" alt="License"/> <img src="https://img.shields.io/badge/Quantized_by-worthdoing-purple?style=for-the-badge" alt="worthdoing"/> </p> <p align="center"> <img src="https://img.shields.io/badge/Q4__K__M-2.0_GB-brightgreen?style=flat-square" alt="Q4_K_M"/> <img src="https://img.shields.io/badge/Q5__K__M-2.4_GB-yellow?style=flat-square" alt="Q5_K_M"/> <img src="https://img.shields.io/badge/Q8__0-3.5_GB-red?style=flat-square" alt="Q8_0"/> </p>

Phi-4-mini - GGUF Quantized by worthdoing

Quantized for local Mac inference (Apple Silicon / Metal) by worthdoing

About

This is a GGUF quantized version of Phi-4-mini, optimized for running locally on Apple Silicon Macs with llama.cpp, Ollama, or LM Studio.

• Original model: microsoft/Phi-4-mini-instruct
• Parameters: 3.8B
• Quantized by: worthdoing
• Pipeline: corelm-model v1.0

Description

Microsoft's reasoning powerhouse in a tiny package.

Available Quantizations

How to Use

With Ollama

# Create a Modelfile
cat > Modelfile <<'MODELEOF'
FROM ./phi-4-mini-Q4_K_M-worthdoing.gguf
MODELEOF

ollama create phi-4-mini -f Modelfile
ollama run phi-4-mini

With llama.cpp

llama-cli -m phi-4-mini-Q4_K_M-worthdoing.gguf -p "Your prompt here" -ngl 99

With LM Studio

1. Download the GGUF file
2. Open LM Studio -> My Models -> Import
3. Select the GGUF file and start chatting

Quantization Method

Our quantization pipeline (corelm-model v1.0) follows a rigorous multi-step process to ensure maximum quality and compatibility:

Step 1 — Download & Validation

• Model weights are downloaded from HuggingFace Hub in SafeTensors format (.safetensors)
• Legacy formats (.bin, .pt) are excluded to ensure clean, verified weights
• Tokenizer, configuration, and all metadata are preserved

Step 2 — Conversion to GGUF F16 Baseline

• The original model is converted to GGUF format at FP16 precision using convert_hf_to_gguf.py from llama.cpp
• This lossless baseline preserves the full original model quality
• Architecture-specific tensors (attention, FFN, embeddings, MoE routing) are mapped to their GGUF equivalents

Step 3 — K-Quant Quantization

• The F16 baseline is quantized using llama-quantize with k-quant methods
• K-quants use a mixed-precision approach: more important layers (attention, output) retain higher precision, while less sensitive layers (FFN) are compressed more aggressively
• Each quantization level offers a different quality/size tradeoff:

Step 4 — Metadata Injection

• Custom metadata is embedded directly in each GGUF file:
  • general.quantized_by: worthdoing
  • general.quantization_version: corelm-1.0
• This ensures full traceability and provenance of every quantized file

Tools & Environment

• llama.cpp: Used for both conversion and quantization — the industry-standard open-source LLM inference engine
• Target platform: Apple Silicon Macs (M1/M2/M3/M4) with Metal GPU acceleration
• Inference runtimes: Compatible with llama.cpp, Ollama, LM Studio, koboldcpp, and any GGUF-compatible runtime

Recommended Hardware

Tags

general, reasoning, coding, math

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Quantized with corelm-model pipeline by worthdoing on 2026-04-17