How to setup a Feature Extraction system on Linux

Follow these steps to install and set up Llama.cpp for AI feature extraction on your Ubuntu system:

1. Install Prerequisites

You will need some basic libraries and tools before installing Llama.cpp. Open your terminal and run the following commands:

sudo apt update
sudo apt install git build-essential cmake libopenblas-dev libomp-dev
    

This will install Git, CMake, and necessary libraries for compilation and performance optimization.

2. Clone Llama.cpp Repository

Now, clone the official Llama.cpp repository from GitHub:

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
    

3. Compile Llama.cpp

To compile the Llama.cpp project, execute the following commands:

mkdir build
cd build
cmake ..
make
    

This will create the necessary binaries that you can use to load the LLaMA models and perform inference.

4. Download LLaMA Model Weights

For Llama.cpp to work, you need to download the LLaMA model weights from Meta AI (if you have access). Store the weights in the appropriate directory:

Create a folder to store the model:

mkdir models
    

Move the model weights into the `models` folder you created.

5. Convert Model Weights to ggml Format

Llama.cpp uses the ggml format for optimized inference. Convert the weights using the provided Python script:

python3 convert-pth-to-ggml.py models/7B/ 1
    

The script will convert the PyTorch checkpoint into the ggml format optimized for inference.

6. Run LLaMA Inference

After converting the weights, you can run inference on the LLaMA model using the following command:

./main -m models/7B/ggml-model-f32.bin -p "Extract features from this sentence"
    

This will extract features from the given text prompt using the LLaMA model.

7. AI Feature Extraction in Python (Optional)

If you want to extract features using Python, Llama.cpp supports a Python binding. First, install the Python dependencies:

pip install llama-cpp-python
    

Then, you can load the model and extract features as follows:

import llama_cpp

llm = llama_cpp.Llama(model_path="models/7B/ggml-model-f32.bin")
output = llm("Extract features from this text")
print(output)
    

8. Performance Optimization (Optional)

You can enable AVX, FMA, or GPU acceleration to speed up the inference process. Use the following commands:

make clean
LLAMA_CUBLAS=1 make
    

For more advanced settings, refer to the official Llama.cpp GitHub page.

Conclusion

You have successfully set up AI feature extraction using Llama.cpp on your Ubuntu machine. You can now experiment with different LLaMA models and extract features for various applications!