Kiln AI: Open Source AI Prototyping and Dataset Collaboration Tool

What is Kiln AI?

Kiln AI is an open-source AI development tool that simplifies the fine-tuning of large language models (LLMs), synthetic data generation, and dataset collaboration. It provides an intuitive desktop application compatible with Windows, macOS, and Linux, allowing users to fine-tune various models (such as Llama, GPT4o, and Mixtral) without coding. Kiln AI offers interactive tools for generating training data, supports Git-based version control for team collaboration, and ensures data privacy and security. The Python library is open-source, enabling developers to integrate it into existing workflows.

Key Features of Kiln AI

• Intuitive Desktop Application: Supports Windows, macOS, and Linux, offering one-click installation and a user-friendly interface.
• No-Code Fine-Tuning: Supports various language models like Llama, GPT4o, and Mixtral, with automatic serverless deployment.
• Synthetic Data Generation: Provides interactive visualization tools for generating training data.
• Team Collaboration: Git-based version control supports multi-user collaboration, ideal for QA, PM, and domain experts.
• Automatic Prompt Generation: Automatically generates prompts from data, including chain-of-thought, few-shot, and multi-shot prompts.
• Wide Model and Provider Support: Compatible with models from Ollama, OpenAI, OpenRouter, Fireworks, Groq, AWS, or any OpenAI API-compatible model.

Technical Principles of Kiln AI

• Git-Based Version Control: Uses Git for version control, supporting multi-user collaboration and dataset version management.
• Serverless Deployment: Automatically deploys fine-tuned models to the cloud or local environment without manual server configuration.
• Interactive Data Generation Tools: Provides an interactive interface for generating high-quality synthetic data.
• Python Library Integration: Open-source Python library allows integration into existing workflows, compatible with Jupyter Notebook.
• Multi-Model Support: Supports various language models and platforms through a unified API.

Project Repository

• GitHub Repository: https://github.com/Kiln-AI/Kiln

Quick Start Guide

• Download and Install:
• Desktop Application: Download and install the free desktop application for macOS, Windows, and Linux.
• Python Library: Install the Python library using pip install kiln-ai to integrate datasets into your workflow.
• Launch the Application:
• Start the application, create a project, connect to AI providers (e.g., Ollama, OpenAI, OpenRouter), and use sample tasks or define custom tasks.

Supported Models and AI Providers

• Supported Providers: OpenAI, Groq, OpenRouter, AWS, Fireworks, etc.
• Compatible Servers: Any OpenAI-compatible server like LiteLLM or vLLM.
• Setting Up AI Providers: Configure providers in the settings or edit ~/.kiln_ai/settings.yaml.

Synthetic Data Generation

• Zero-Shot Data Generation: Generate data directly based on task definitions.
• Topic Tree Data Generation: Generate data based on nested topic trees.
• Structured Data Generation: Generate data following a user-defined JSON schema.

Fine-Tuning Guide

• Step 1: Define Task and Objective: Create a new task in Kiln UI with initial prompts and requirements.
• Step 2: Generate Training Data: Use synthetic data generation tools to create high-quality datasets.
• Step 3: Select Model for Fine-Tuning: Choose from supported models like GPT-4o, Mixtral 8x7b MoE, or Llama 3.2.
• Step 4: Start Fine-Tuning: Select model, dataset, and training parameters in the "Fine-Tuning" tab.
• Step 5: Deploy and Run Model: Automatically deploy the fine-tuned model and use it via the "Run" tab.

Training Inference Models

• Key Steps: Ensure training data includes reasoning, select appropriate training strategies, and use consistent prompts.
• Inference vs. Chain-of-Thought: Use inference models for cross-domain reasoning or chain-of-thought prompts for task-specific training.

Application Scenarios

• Customer Support: Generate customer service dialogue datasets to improve response accuracy.
• Healthcare: Collaborate with domain experts to create medical datasets for AI models.
• Rapid Prototyping: Experiment with different models for text generation tasks.
• Education: Build educational datasets for fine-tuning AI models in education.
• Finance: Fine-tune risk assessment models with local data processing for privacy.