YaGGUF - Yet Another GGUF Converter

There are simultaneously too many and not enough GGUF converters in the world.

More screenshots

Features

• llama.cpp under the hood - so that part works
• Download - automatically download models and their auxiliary files from HuggingFace
• Convert - safetensors and PyTorch models to GGUF format
• Quantize - to multiple formats at once
• Cross-platform - works on Windows and Linux (and probably Mac but untested)
• Easy - auto-installs an environment + llama.cpp + CPU binaries for quantizing
• Flexible - can use any local llama.cpp repo or binary installation for quantizing
• Minimal mess - virtual environment prevents conflicts with your python setup

Advanced Features

• Single or split files mode - Generate single or split files for intermediates and quants
• Split/Merge Shards - Split, merge, or resplit GGUF and safetensors files with custom shard sizes
• Importance Matrix - Generate or reuse imatrix files for better low-bit quantization (IQ2, IQ3)
• Imatrix Statistics - Analyze importance matrix files to view statistics
• Custom intermediates - Use existing GGUF files as intermediates for quantization
• Enhanced dtype detection - Detects model precision (BF16, F16, etc.) from configs and safetensors headers
• Model quirks detection - Handles Mistral format, pre-quantized models, and architecture-specific flags
• Vision/Multimodal models - Automatic detection and two-step conversion (text model + mmproj-*.gguf)
• Sentence-transformers - Auto-detect and include dense modules for embedding models
• VRAM Calculator - Estimate VRAM usage and recommended GPU layers (-ngl) for GGUF models

Quantization Types

All quantization types from llama.cpp are supported. Choose based on your size/quality tradeoff:

Type	Size	Quality	Category	Imatrix	Notes
F32	Largest	Original	Unquantized	-	Full 32-bit precision
F16	Large	Near-original	Unquantized	-	Half precision
BF16	Large	Near-original	Unquantized	-	Brain float 16-bit
Q8_0	Very Large	Excellent	Legacy	-	Near-original quality
Q5_1, Q5_0	Medium	Good	Legacy	-	Legacy 5-bit
Q4_1, Q4_0	Small	Fair	Legacy	-	Legacy 4-bit
Q6_K	Large	Very High	K-Quant	Suggested	Near-F16 quality
Q5_K_M	Medium	Better	K-Quant	Suggested	Higher quality
Q5_K_S	Medium	Better	K-Quant	Suggested	5-bit K small
Q4_K_M	Small	Good	K-Quant	Suggested	4-bit K medium
Q4_K_S	Small	Good	K-Quant	Suggested	4-bit K small
Q3_K_L	Very Small	Fair	K-Quant	Recommended	3-bit K large
Q3_K_M	Very Small	Fair	K-Quant	Recommended	3-bit K medium
Q3_K_S	Very Small	Fair	K-Quant	Recommended	3-bit K small
Q2_K	Tiny	Minimal	K-Quant	Recommended	2-bit K
Q2_K_S	Tiny	Minimal	K-Quant	Recommended	2-bit K small
IQ4_NL	Small	Good	I-Quant	Recommended	4-bit non-linear
IQ4_XS	Small	Good	I-Quant	Recommended	4-bit extra-small
IQ3_M	Very Small	Fair	I-Quant	Recommended	3-bit medium
IQ3_S	Very Small	Fair+	I-Quant	Recommended	3.4-bit small
IQ3_XS	Very Small	Fair	I-Quant	Required	3-bit extra-small
IQ3_XXS	Very Small	Fair	I-Quant	Required	3-bit extra-extra-small
IQ2_M	Tiny	Minimal	I-Quant	Required	2-bit medium
IQ2_S	Tiny	Minimal	I-Quant	Required	2-bit small
IQ2_XS	Tiny	Minimal	I-Quant	Required	2-bit extra-small
IQ2_XXS	Tiny	Minimal	I-Quant	Required	2-bit extra-extra-small
IQ1_M	Extreme	Poor	I-Quant	Required	1-bit medium
IQ1_S	Extreme	Poor	I-Quant	Required	1-bit small

Quick Guide:

• Bigger is better (more precision)
• For best quality use F16 or Q8_0
• For decent quality use Q6_K or Q5_K_M
• Medium quality... Use Q4_K_M
• For smallest size use IQ3_M or IQ2_M with importance matrix

Requirements

• Python 3.8 or higher
• Git 2.20 or higher (if you want the update tab to work)

Installation - Windows

# Clone the repository
    git clone https://github.com/usrname0/YaGGUF.git
    cd YaGGUF
# Run the launcher script for Windows (runs a setup script if no venv detected):
    .\run_gui.bat

Installation - Linux

# If you want to select folders via the gui install tkinter (optional):
    sudo apt install python3-tk      # Ubuntu/Debian
    sudo dnf install python3-tkinter # Fedora/RHEL
    sudo pacman -S tk                # Arch

# Clone the repository
    git clone https://github.com/usrname0/YaGGUF.git
    cd YaGGUF

# Run the launcher script for Linux (runs a setup script if no venv detected):
    ./run_gui.sh

Usage

Windows:

• Double-click .\run_gui.bat

Linux:

• Use terminal ./run_gui.sh

The GUI will automatically open in your browser on a free port like: http://localhost:8501

License

MIT License - see LICENSE file for details

Credits

• llama.cpp - GGUF format and conversion/quantization tools
• HuggingFace - Model hosting and transformers library
• Streamlit - Pythonic data apps