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Kiln AI: An AI Tool for LLM Fine-Tuning, Synthetic Data Generation, and Dataset Collaboration
Kiln AI: An AI Tool for LLM Fine-Tuning, Synthetic Data Generation, and Dataset CollaborationIntroduction
Kiln ai is an open-source AI tool for fine-tuning large language models (LLMs), synthetic data generation, and collaborative dataset work. It provides an intuitive interface and supports Windows, macOS, and Linux systems, allowing users to fine-tune a variety of models with zero coding and deploy them automatically.
Kiln AI also supports DeepSeek (I have a deepseek-r1:14b deployed locally via Ollama) and I ran a small test with it. For someone like me who needs to locally distill large language models for development and research, Kiln AI is simply a "godsend."
Kiln AI Features:
Zero-Code Fine-Tuning: Kiln AI allows users to fine-tune models through a simple interface. Users can choose suitable models, such as GPT-4o, the Llama series, and more, and configure parameters for fine-tuning.
Dataset Building: Kiln AI supports users in building and iterating datasets. Users can input their own data sources, and the tool helps improve data quality and diversity to optimize training results.
Synthetic Data Generation: The tool can automatically generate synthetic data to support model training, reducing the cost of data annotation, which is especially useful in data-scarce scenarios.
Team Collaboration: Git-based version control for AI datasets. An intuitive user interface makes it easy to collaborate with QA, PMs, and subject matter experts on structured data.
Prompt Generation: Automatically generate prompts from your data, including chain-of-thought, few-shot, and multi-shot examples.
Broad Model and Provider Support: Supports Ollama, OpenAI, OpenRouter, Fireworks, Groq, AWS, and more.
Supported Models and Providers
Supports any OpenAI-compatible API, including Ollama, OpenAI, OpenRouter, Fireworks, Groq, and AWS.
Extensive Model Library: Supported models include GPT, Llama, Claude, Gemini, Mistral, Gemma, and Phi.
How to Use Kiln AI for Dataset Building and Model Optimization?
1. Download and install Kiln AI:
Visit Kiln AI's GitHub page and download the installer for your operating system.
Follow the setup wizard's instructions to complete the installation.
2. Launch the Kiln AI application:
After installation, launch the Kiln AI application. You will see an intuitive user interface.
3. Create a new task:
In Kiln AI's UI, click the "Create New Task" button. This will take you to the task definition and goal-setting stage.
Enter the task name, description, and any specific input and output requirements.
4. Generate training data:
Use Kiln AI's interactive visualization tools to generate training data. You can produce high-quality synthetic data through techniques such as topic trees, multi-example prompts, and chain-of-thought.
When generating data, ensure diversity and accuracy.
5. Select and fine-tune a model:
In Kiln AI, you can choose from a variety of pre-trained models for fine-tuning, including Llama, GPT-4o, Mixtral, and more.
Configure fine-tuning parameters, such as the training and validation set splits, and start the fine-tuning job.
6. Deploy and use the model:
Once fine-tuning is complete, Kiln AI will automatically deploy the model. You can test and call the fine-tuned model directly in the UI.
If needed, you can also integrate the model into other systems or applications via the API.
7. Team collaboration and version control:
Kiln AI supports Git-based version control, making it easy for team members to collaborate on datasets and models.
You can use Kiln AI's interface or command-line tools for version management and collaboration.
GitHub repository address: https://github.com/Kiln-AI/Kiln
