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Are you looking for a way to start LLM (Large Language Model) fine-tuning for free and easily? Unsloth Notebooks provides the perfect solution through over 100 Jupyter notebooks that enable free fine-tuning of various LLMs on Google Colab and Kaggle.
Unsloth Notebooks is an open-source project that has gained attention in the AI development community with 2k stars and 282 forks. This repository provides guided notebooks for fine-tuning the latest LLMs in various ways, including the complete pipeline from data preparation to model training, evaluation, and saving.
Completely Free: Utilizes free GPUs from Google Colab and Kaggle 100+ Notebooks: Provides customized notebooks for various models and use cases Guided Structure: Step-by-step guidance that even beginners can easily follow Continuous Updates: Latest models are continuously added LGPL-3.0 License: Freely available as open source
The Qwen3 family represents cutting-edge conversational AI capabilities:
Qwen3 (14B): Large model with excellent conversational reasoning abilities Qwen3-Base (4B): Supports GRPO (Group Relative Policy Optimization) Models recognized for superior reasoning abilities and conversation quality
Google’s latest open-source model offering efficient performance relative to its size and optimization for conversational tasks.
Llama 3.2 (3B): Lightweight model for conversational tasks Llama 3.2 Vision (11B): Supports multimodal (text+image) processing Llama 3.1 (8B): Supports Alpaca format fine-tuning
Microsoft’s latest small language model featuring excellent reasoning capabilities and efficiency.
China’s prominent open-source model with GRPO optimization support.
Llama 3.2 Vision (11B): Processes images and text together Qwen2.5 VL (7B): Vision-language multimodal tasks Qwen2 VL (7B): Previous version vision model
Spark TTS (0.5B): Text-to-speech conversion Sesame CSM (1B): Speech synthesis model Whisper: Speech recognition and transcription
Fine-tuning for natural conversations with users involves training models to engage in human-like dialogue with appropriate context understanding and response generation.
The standard format from Stanford Alpaca project provides structured instruction-input-output formatting that has become widely adopted in the fine-tuning community.
Advanced optimization technique based on reinforcement learning that significantly improves model response quality through sophisticated reward modeling and policy optimization.
Multimodal fine-tuning that processes text and images together, enabling models to understand and respond to visual content alongside textual information.
The process involves several straightforward steps:
Notebook Selection: Choose notebooks matching your desired model and use case Open in Colab: Click the “Open in Colab” button Runtime Setup: Select GPU runtime (T4 or higher specification) Step-by-Step Execution: Run notebook cells in order
Kaggle environment offers the same notebooks with additional advantages:
Longer Execution Time: 30 hours of free GPU usage per week More Storage Space: 20GB output data storage capability Team Collaboration: Share notebooks and collaborate with team members
Chain of Thought (CoT) fine-tuning for CodeForces problem solving demonstrates advanced reasoning capabilities through structured problem-solving approaches.
Training models to call external APIs or functions enables integration with external systems and services, expanding model capabilities beyond text generation.
No-code/low-code environment for intuitive model fine-tuning provides accessible interfaces for users without extensive programming backgrounds.
Unsloth employs memory-saving optimization techniques including:
LoRA (Low-Rank Adaptation): Trains only subset of parameters instead of full model Gradient Checkpointing: Saves memory usage during training Mixed Precision Training: Improves training speed and efficiency
Effective dataset preparation for successful fine-tuning involves creating high-quality datasets with clear instructions, relevant context information, and accurate, useful responses.
Contributing to Unsloth Notebooks project involves:
Use Templates: Start with Template_Notebook.ipynb file
Follow Naming Conventions:
<Model Name>-<Type>.ipynb<Model Name>-Vision.ipynb
Automatic Updates: Run python update_all_notebooks.py
Submit Pull Requests: Share changes with the communityThe project benefits from 12 core contributors who continuously develop the project, regular updates that add latest models, and comprehensive documentation available at official docs.
Customer Service Chatbots: Fine-tune with company-specific data Internal Document Summarization: Learn company documents for automatic summarization Code Review Tools: Review bots tailored to development team coding styles
Personalized Tutors: Explanations tailored to learner levels Research Paper Analysis: Analysis tools trained on domain-specific papers Language Learning Assistants: AI tutors specialized for specific language learning
Novel Writing Assistants: Creative support tailored to specific genres or styles Scenario Generation: Tools for creating movie or game scenarios Marketing Copy Generation: Copy writing tailored to brand tone and manner
Essential prerequisites include:
Beginner: Start with Llama 3.2 (3B) Conversational Intermediate: Progress to Qwen3 (14B) Reasoning for complex reasoning learning Advanced: Explore Vision models for multimodal experience Expert: Investigate GRPO or Tool Calling for advanced features
The notebooks demonstrate integration of reinforcement learning techniques with traditional fine-tuning approaches, enabling development of models that can learn from feedback and improve their responses over time.
Vision-enabled notebooks showcase sophisticated approaches to training models that can process and understand both textual and visual information, opening possibilities for more comprehensive AI applications.
The collection includes specialized notebooks for different domains, demonstrating how to adapt general-purpose models for specific industries, use cases, and application requirements.
The notebooks include comprehensive evaluation frameworks that assess model performance across various metrics, ensuring that fine-tuned models meet quality standards and performance expectations.
Many notebooks provide comparative analysis between different training approaches, helping users understand the trade-offs between various fine-tuning strategies and select optimal approaches for their specific needs.
The framework supports iterative improvement processes, enabling users to refine their models based on performance feedback and evolving requirements.
Unsloth Notebooks represents an excellent resource that significantly lowers the barriers to LLM fine-tuning. Through over 100 free notebooks, you can experiment with cutting-edge AI models, and the guided structure makes it easy for beginners to get started.
The ability to utilize free GPUs from Google Colab and Kaggle without cost burden is the greatest attraction, allowing you to experiment with high-performance models. If you want to create your own specialized AI model, start with Unsloth Notebooks right away!
The continuous addition of new models and regular updates ensure that the resource remains current with the latest developments in AI technology, making it a valuable long-term learning and development platform.
💡 Recommendation: We recommend starting with simple conversational models and gradually progressing to complex tasks. Each notebook can run independently, so you can also start directly with models that interest you.
The Unsloth Notebooks project represents a collaborative effort to democratize access to advanced AI training techniques, making cutting-edge LLM fine-tuning accessible to developers and researchers worldwide.