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# AutoGGUF - automated GGUF model quantizer

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AutoGGUF provides a graphical user interface for quantizing GGUF models using the llama.cpp library. It allows users to download different versions of llama.cpp, manage multiple backends, and perform quantization tasks with various options.

## Features

- Download and manage llama.cpp backends
- Select and quantize GGUF models
- Configure quantization parameters
- Monitor system resources during quantization
- Parallel quantization + imatrix generation
- LoRA conversion and merging

## Usage

### Cross-platform
1. Install dependencies:
   ```
   pip install -r requirements.txt
   ```
   or
   ```
   pip install PyQt6 requests psutil shutil numpy torch safetensors gguf
   ```
2. Run the application:
   ```
   python src/main.py
   ```
   or use the `run.bat` script.

### Windows
1. Download the latest release
2. Extract all files to a folder
3. Run `AutoGGUF.exe`

### Verifying Releases

#### Linux/macOS:
```bash
gpg --import AutoGGUF-v1.5.0-prerel.asc
gpg --verify AutoGGUF-v1.5.0-Windows-avx2-prerel.zip.sig AutoGGUF-v1.5.0-Windows-avx2-prerel.zip
sha256sum -c AutoGGUF-v1.5.0-prerel.sha256
```

#### Windows (PowerShell):
```powershell
# Import the public key
gpg --import AutoGGUF-v1.5.0-prerel.asc

# Verify the signature
gpg --verify AutoGGUF-v1.5.0-Windows-avx2-prerel.zip.sig AutoGGUF-v1.5.0-Windows-avx2-prerel.zip

# Check SHA256
$fileHash = (Get-FileHash -Algorithm SHA256 AutoGGUF-v1.5.0-Windows-avx2-prerel.zip).Hash.ToLower()
$storedHash = (Get-Content AutoGGUF-v1.5.0-prerel.sha256 | Select-String AutoGGUF-v1.5.0-Windows-avx2-prerel.zip).Line.Split()[0]
if ($fileHash -eq $storedHash) { "SHA256 Match" } else { "SHA256 Mismatch" }
```

Release keys are identical to ones used for commiting.

## Building

### Cross-platform
```bash
cd src
pip install -U pyinstaller
pyinstaller main.py --onefile
cd dist/main
./main
```

### Windows
```bash
build RELEASE | DEV
```
Find the executable in `build/<type>/dist/AutoGGUF.exe`.

## Dependencies

- PyQt6
- psutil
- shutil
- numpy
- torch
- safetensors
- gguf (bundled)

## Localizations

View the list of supported languages at [AutoGGUF/wiki/Installation#configuration](https://github.com/leafspark/AutoGGUF/wiki/Installation#configuration) (LLM translated, except for English).

To use a specific language, set the `AUTOGGUF_LANGUAGE` environment variable to one of the listed language codes (note: some languages may not be fully supported yet, those will fallback to English).

## Known Issues

- Saving preset while quantizing causes UI thread crash (planned fix: remove this feature)
- Cannot delete task while processing (planned fix: disallow deletion before cancelling or cancel automatically)

## Planned Features

- Actual progress bar tracking 
- Perplexity testing
- Time estimation for quantization

## Troubleshooting

- SSL module cannot be found error: Install OpenSSL or run from source using `python src/main.py` with the `run.bat` script (`pip install requests`)

## Contributing

Fork the repo, make your changes, and ensure you have the latest commits when merging. Include a changelog of new features in your pull request description. Read `CONTRIBUTING.md` for more information.

## User Interface

![image](https://github.com/user-attachments/assets/2660c841-07ba-4c3f-ae3a-e63c7068bdc1)

## Stargazers

[![Star History Chart](https://api.star-history.com/svg?repos=leafspark/AutoGGUF&type=Date)](https://star-history.com/#leafspark/AutoGGUF&Date)