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Low-Level API for Llama_cpp
Overview
This Python script, low_level_api_llama_cpp.py, demonstrates the implementation of a low-level API for interacting with the llama_cpp library. The script defines an inference that generates embeddings based on a given prompt using .gguf model.
Prerequisites
Before running the script, ensure that you have the following dependencies installed:
. Python 3.6 or higher . llama_cpp: A C++ library for working with .gguf model . NumPy: A fundamental package for scientific computing with Python . multiprocessing: A Python module for parallel computing
Usage
install depedencies:
python -m pip install llama-cpp-python ctypes os multiprocessing
Run the script:
python low_level_api_llama_cpp.py
Code Structure
The script is organized as follows:
. Initialization:
Load the model from the specified path.
Create a context for model evaluation.
. Tokenization:
Tokenize the input prompt using the llama_tokenize function.
Prepare the input tokens for model evaluation.
. Inference:
Perform model evaluation to generate responses.
Sample from the model's output using various strategies (top-k, top-p, temperature).
. Output:
Print the generated tokens and the corresponding decoded text.
.Cleanup:
Free resources and print timing information.
Configuration
Customize the inference behavior by adjusting the following variables:
. N_THREADS: Number of CPU threads to use for model evaluation.
. MODEL_PATH: Path to the model file.
. prompt: Input prompt for the chatbot.
Notes
. Ensure that the llama_cpp library is built and available in the system. Follow the instructions in the llama_cpp repository for building and installing the library.
. This script is designed to work with the .gguf model and may require modifications for compatibility with other models.
Acknowledgments
This code is based on the llama_cpp library developed by the community. Special thanks to the contributors for their efforts.
License
This project is licensed under the MIT License - see the LICENSE file for details.