Predibase has released the first Reinforcement Fine-Tuning platform, promising a groundbreaking approach to customizing LLMs using reinforcement learning. Use RFT to train open-source LLMs that outperform GPT-4, even when labeled data is limited.
Tuning LLMs just got 100x easier—no massive datasets, no endless prompt engineering. With Predibase RFT, you can fine-tune models to outperform GPT-4 with just a dozen labeled examples. Yes, really.
💡 Why is this game-changing? ✅ No More Labeling Bottlenecks: Get performance that beats commercial LLMs without massive datasets. ⚡ Rapid Iteration: Go from idea to deployment faster than ever. ⚙️ Turbocharged Inference: See up to 3x faster performance for reasoning models using Turbo LoRA speculative decoding. 🔒 Enterprise-Ready: Deploy in your VPC or on our cloud with full security.
Inspired in part by the GRPO framework behind DeepSeek-R1, we built RFT because we were tired of seeing teams unable to fine-tune models due to a lack of labeled data. Now, AI teams can customize models faster and with higher accuracy without requiring 1,000s of rows of labeled data—and it's already delivering 20%+ better performance than GPT-4 in specialized tasks.
When you say "I can see how my model does out of the box" what's it testing against?
ML engineers with deep expertise. If this tool helps bridge that gap, it’s a great step forward.
Predibase Reinforcement Fine-Tuning's AI platform simplifies fine-tuning LLMs. Great job! 👍
This is fantastic! The ability to fine-tune models with just a handful of examples is a real breakthrough—no more overwhelming data sets. How does Predibase RFT manage niche cases where data is limited or very specific?
This tool makes fine-tuning LLMs so much easier! It's a game-changer for improving model performance. 👍
