Kolo
What is the project about?
Kolo is a tool designed to simplify and speed up the process of generating data for, fine-tuning, and testing Large Language Models (LLMs) locally.
What problem does it solve?
It addresses the complexity and resource-intensive nature of working with LLMs. It removes the need for cloud services, allowing users to perform these tasks on their own machines. It also simplifies the setup of a LLM development environment. It solves the problem of needing to generate synthetic QA training data.
What are the features of the project?
- Runs Locally: Operates on the user's machine, eliminating reliance on cloud services.
- Easy Setup: Uses Docker to handle dependencies, simplifying the installation process.
- Generate Training Data: Allows users to quickly generate synthetic QA training data from text files.
- Support for Popular Frameworks: Integrates with Unsloth, Torchtune, Llama.cpp, Ollama, and Open WebUI.
What are the technologies used in the project?
- Unsloth
- Torchtune
- Llama.cpp
- Ollama
- Docker
- Open WebUI
What are the benefits of the project?
- Speed and Efficiency: Focuses on fast data generation and fine-tuning.
- Cost Savings: Reduces the need for expensive cloud-based LLM services.
- Simplified Workflow: Streamlines the entire process, from data generation to model testing.
- Easy to use: Simple commands to build, train, and test.
What are the use cases of the project?
- Local LLM Development: Developers who want to fine-tune and test LLMs without relying on cloud infrastructure.
- Rapid Prototyping: Quickly iterating on LLM-powered applications.
- Data Generation: Creating synthetic training data for specific tasks or domains.
- Educational Purposes: Learning about and experimenting with LLM fine-tuning.
- Research: Researchers needing a controlled, local environment for LLM experiments.
