Codeium Windsurf is an open-source framework designed to streamline the fine-tuning of large language models (LLMs) using both supervised learning and Direct Preference Optimization (DPO). Developed by the team at Codeium, Windsurf empowers machine learning engineers and researchers to efficiently adapt foundational LLMs for custom tasks, products, or organizational needs.
With growing demand for domain-specific and high-performance AI systems, Windsurf offers a flexible and extensible platform that bridges the gap between general-purpose models and use-case-specific deployment. It supports fine-tuning on proprietary datasets, preference-based optimization, and easy integration into existing MLOps pipelines.
Features
Supervised Fine-Tuning
- Supports supervised training on question-answer pairs or task-specific datasets
- Ideal for enhancing instruction-following, domain-specific knowledge, or style
Direct Preference Optimization (DPO)
- Enables preference-based model training using chosen vs. rejected responses
- Trains LLMs to generate outputs aligned with human preferences or quality metrics
Model-Agnostic Design
- Compatible with popular open-source models (e.g., LLaMA, Mistral, Falcon)
- Easily adapts to various Hugging Face-supported transformer architectures
Simple Configuration
- Modular YAML-based configuration files
- Customize data formats, training strategies, and hyperparameters with minimal setup
Evaluation Tools Included
- Built-in scripts for evaluating trained models on instruction-following and preference datasets
- Supports quantitative benchmarking during or after training
Fast and Scalable Training
- Designed for efficiency and scalability on single or multi-GPU setups
- Compatible with popular frameworks like PyTorch and Hugging Face Transformers
How It Works
- Prepare Your Dataset – Format your training data as either supervised examples or preference pairs.
- Choose Training Method – Select between standard supervised fine-tuning or DPO in the config.
- Configure Model and Parameters – Use YAML to define model name, tokenizer, batch size, and other settings.
- Launch Training – Run training scripts via CLI or incorporate into your MLOps stack.
- Evaluate and Deploy – Test model performance on validation datasets and deploy for downstream use.
Use Cases
For AI Researchers
Use Windsurf to experiment with new training strategies or compare supervised vs. preference-based optimization approaches.
For Enterprises and Startups
Fine-tune open-source LLMs to better align with internal use cases, brand tone, or domain knowledge (e.g., legal, medical, customer service).
For Open-Source Contributors
Extend Windsurf for custom tasks and contribute back to the growing community of LLM optimization frameworks.
For ML Engineers
Integrate Windsurf into existing training pipelines for faster experimentation and reproducibility with open-source models.
Pricing Plans
Codeium Windsurf is fully open-source and free to use under the Apache 2.0 license.
- Free Forever
- No licensing fees
- Fully open and auditable source code
- Commercial and non-commercial use allowed
Download or contribute via the official GitHub repository.
Strengths
- Built by Codeium, with real-world experience fine-tuning production-grade LLMs
- Flexible support for both supervised and preference-based training
- Open-source and community-driven
- Minimal setup and straightforward configuration
- Model-agnostic with Hugging Face support for easy adoption
Drawbacks
- Requires ML engineering experience to use effectively
- Assumes familiarity with LLM training workflows and infrastructure
- No built-in UI or orchestration layer (CLI-based only)
- Doesn’t include dataset curation tools—assumes pre-formatted input
Comparison with Other Tools
Compared to other open-source fine-tuning frameworks like Axolotl, TRLLM, or LoRA implementations, Codeium Windsurf is distinct in its built-in support for both supervised fine-tuning and Direct Preference Optimization (DPO)—all in a single framework.
Axolotl excels in fine-tuning with adapters and supports multiple training backends, but Windsurf stands out with its simplicity and focus on quality output through preference learning. TRLLM is tailored for reinforcement learning with human feedback (RLHF), but Windsurf simplifies DPO—a more scalable and compute-efficient alternative.
Furthermore, Windsurf benefits from Codeium’s production experience, making it a practical solution backed by a team actively using it to build real-world AI tools.
Customer Reviews and Testimonials
As an emerging open-source framework, Windsurf has already received attention from the AI and ML community:
- AI developers appreciate its easy setup and strong documentation.
- Research teams value the inclusion of DPO and sample datasets to accelerate experimentation.
- Open-source contributors highlight the modularity and extensibility of the codebase.
User comments on GitHub and forums praise Windsurf for reducing boilerplate in LLM fine-tuning and helping teams bridge the gap between base models and production-ready outputs.
Conclusion
Codeium Windsurf is a powerful and accessible open-source framework for anyone looking to fine-tune large language models with minimal friction. Whether you’re enhancing model behavior with supervised learning or aligning outputs using Direct Preference Optimization, Windsurf gives you the tools to train better-performing LLMs on your own data.
Built with real-world use cases in mind and backed by a trusted AI company, Windsurf is an ideal choice for teams, researchers, and builders working to push the boundaries of AI customization.
Start experimenting today by visiting https://codeium.com/windsurf or check out the GitHub repository.
