unsloth
Fine-tuning & Reinforcement Learning for LLMs. 🦥 Train OpenAI gpt-oss, DeepSeek, Qwen, Llama, Gemma, TTS 2x faster with 70% less VRAM.
💡 Why It Matters
Unsloth addresses the challenge of efficiently fine-tuning large language models (LLMs) by enabling engineers to train models like OpenAI's gpt-oss and Llama significantly faster while reducing VRAM usage. This is particularly beneficial for ML and AI teams who require a production-ready solution that optimises resource consumption without sacrificing performance. The tool is mature and has demonstrated effectiveness in real-world applications, making it suitable for deployment in various projects. However, it may not be the right choice for teams with limited expertise in reinforcement learning or those looking for a simpler, less resource-intensive solution.
🎯 When to Use
Unsloth is a strong choice when teams need to accelerate the training of LLMs while minimising resource overhead. Teams should consider alternatives if they require a more straightforward approach to model training or if they lack the necessary infrastructure to support the tool's capabilities.
👥 Team Fit & Use Cases
This open source tool is ideal for machine learning engineers, data scientists, and AI researchers who are focused on model development and optimisation. It is commonly integrated into products and systems that leverage advanced AI capabilities, such as chatbots, recommendation engines, and natural language processing applications.
🎭 Best For
🏷️ Topics & Ecosystem
📊 Activity
Latest commit: 2026-02-14. Over the past 96 days, this repository gained 4.0k stars (+8.4% growth). Activity data is based on daily RepoPi snapshots of the GitHub repository.