UnslothAI Releases 100+ Fine-Tuning Notebooks

• Michael Willson
• June 13, 2025

UnslothAI has released a collection of more than 100 fine-tuning notebooks, making it easier than ever for developers and researchers to train and adapt language models. This update means you can now fine-tune models like Llama, Qwen3, Gemma, and more using straightforward, guided Jupyter notebooks. If you’ve been looking for a fast and simple way to get started with AI fine-tuning, these notebooks are a game changer.

What Makes UnslothAI’s Notebooks Special

UnslothAI’s new release covers all the steps you need to fine-tune a language model. Each notebook is designed to walk you through the entire process: from preparing your data, training, evaluating, and saving your model. This approach helps users understand each part of the workflow without needing to jump between different resources or tools.

Broad Model Support

One of the biggest highlights is the wide range of models supported. Whether you’re working with Llama 3.1–3.2, Qwen3, Gemma 3, Phi-4, Mistral v0.3, DeepSeek-R1, or even Sesame-CSM for TTS, you’ll find a notebook tailored to your needs. This makes it easy to pick the right starting point, whether you’re building chatbots, translation tools, or even text-to-speech systems.

Beginner-Friendly and Free

All the notebooks run on free platforms like Google Colab and Kaggle. Many of them require as little as 3 GB of VRAM, so even users with modest hardware can start training models. This makes fine-tuning more accessible than ever, opening doors for hobbyists, students, and professionals alike.

Performance and Efficiency

UnslothAI has designed its notebooks to be fast and efficient. Thanks to its tooling, fine-tuning can be 2 to 30 times faster than with traditional methods like FlashAttention2—while using less memory. This means you can train larger models or more complex datasets without worrying about hardware limits.

UnslothAI Performance Highlights

Feature	Benefit
Speed	2x–30x faster fine-tuning on the same GPU
VRAM Requirements	As low as 3 GB—runs on free platforms
Memory Efficiency	Lower memory use than FlashAttention2
Hardware Compatibility	Supports Google Colab, Kaggle, and local setups

This table shows why UnslothAI’s notebooks are so practical, even for users with limited resources.

Supported Fine-Tuning Methods

UnslothAI’s notebooks aren’t just for simple supervised fine-tuning. They cover advanced training methods like GRPO, DPO, SFT, QLoRA, continued pre-training, multi-modal setups, and even reasoning tasks. This makes it easier for developers to experiment with new techniques and push the boundaries of what their models can do.

How UnslothAI Stacks Up Against Competitors

While UnslothAI’s release is impressive, it’s important to understand how it compares to other popular tools. Platforms like Hugging Face, GitHub, and Lepton also offer notebooks and tutorials—but they often lack the structure and breadth of UnslothAI’s offering.

Comparison of AI Fine-Tuning Resources

Platform	Notebook Collection	Model Support	Accessibility	Key Strengths
UnslothAI	100+ guided notebooks	Llama, Qwen3, Gemma, Phi-4, TTS	Free on Colab/Kaggle	Wide model coverage, beginner-friendly
Hugging Face	Varies by author	Models only, limited guidance	Free via Spaces	Community support, flexible, but fragmented
GitHub Notebooks	Scattered/not structured	Model-specific scripts	Public access	Full control, but no consistent workflow
Lepton	Integration guides	Advanced dev environments	Subscription-based	Interoperability with multiple frameworks

This table highlights how UnslothAI stands out with a comprehensive, step-by-step approach that’s easy for beginners to pick up.

Areas for Improvement

While UnslothAI’s notebooks are a big step forward, there are still opportunities to make them even better. For example, adding a simple “starter” notebook for absolute beginners would help those new to machine learning. Also, including benchmarks that compare training speed, memory use, and final accuracy would give users a clearer picture of performance.

Interactive tutorials, such as short videos or guided walkthroughs, could also make the learning curve even smoother. Finally, built-in metrics for tracking training progress, VRAM usage, and time spent would be a welcome addition for users looking to optimize their workflows.

Why These Notebooks Matter

These notebooks make advanced AI training accessible to everyone—from hobbyists to experienced developers. With support for many models and training methods, UnslothAI gives users a flexible way to fine-tune language models for different tasks. If you’re interested in boosting your AI skills, pairing these notebooks with a Data Science Certification or an AI Certification can give you the knowledge you need to take your projects to the next level. For those looking to understand how to bring these solutions to market, a Marketing and Business Certification can help you build the skills needed to position and manage AI-powered products effectively.

Conclusion

UnslothAI’s release of over 100 fine-tuning notebooks is a big leap forward in making AI training more accessible, efficient, and practical. With support for popular models, advanced training methods, and free cloud platforms, it’s now easier than ever to fine-tune large language models without needing expensive hardware or complex setups. Whether you’re a student, researcher, or professional developer, these notebooks can help you get started quickly and confidently.