TuneKit
TuneKit is an open-source platform that enables fast and efficient fine-tuning of small language models (SLMs) with no coding required, powered by Unsloth optimization technology.
https://tunekit.app/?ref=producthunt
Product Information
Updated:Jan 13, 2026
What is TuneKit
TuneKit is a specialized platform designed to simplify and accelerate the process of fine-tuning language models. It provides a user-friendly interface where developers can upload their data and get production-ready models in under 15 minutes, without needing to write code or deal with complex infrastructure. The platform is built to be accessible to users of all technical levels while leveraging powerful AI optimization techniques.
Key Features of TuneKit
TuneKit is an open-source platform designed to fine-tune Small Language Models (SLMs) with improved efficiency and accessibility. It offers a no-code approach to model training, featuring automated data validation, smart model selection, and optimized training configurations that run 2x faster while using 70% less VRAM. The platform enables users to train models on free Google Colab GPUs and export them in various formats for different deployment scenarios.
Automated Model Configuration: AI-powered system that analyzes user data and automatically recommends the most suitable model and hyperparameters for specific tasks
Optimized Training Performance: Leverages Unsloth optimization to deliver 2x faster training speeds while reducing VRAM usage by 70%
Flexible Export Options: Supports multiple export formats including GGUF for Ollama, merged weights for HuggingFace, and LoRA adapters
No-Code Interface: Simple upload-and-train workflow with automated data validation and pattern analysis
Use Cases of TuneKit
Rapid Prototyping: Developers can quickly experiment with and fine-tune models for different applications without extensive setup or coding
Research Projects: Researchers can efficiently test different model configurations and training approaches using free computational resources
Production Model Development: Teams can develop production-ready models in under 15 minutes without infrastructure costs
Pros
No cost - uses free Google Colab GPUs
Fast training with optimized performance
User-friendly interface requiring no coding experience
Cons
Dependent on Colab's availability and limitations
May not be suitable for very large-scale training projects
How to Use TuneKit
Upload Data: Drop your JSONL file containing conversation data into TuneKit's interface. The system will automatically validate the format and analyze patterns in your data.
Configure Model Settings: TuneKit's AI will analyze your data and automatically recommend the best model (e.g. Llama 3.2 3B) and optimal hyperparameters for your specific task.
Launch Training: Open the provided one-click Colab notebook, click 'Run All' to start training on Google Colab's free T4 GPU. Training typically completes in under 15 minutes.
Export Model: Once training is complete, export your fine-tuned model in your preferred format - GGUF for Ollama, merged weights for HuggingFace, or LoRA adapters.
TuneKit FAQs
TuneKit is an open-source tool for fine-tuning small language models (SLMs). It allows users to fine-tune models 2x faster without coding, guesswork or infrastructure costs.
TuneKit Video
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