Predibase
The GenAI Platform for Productionizing Open-Source LLMs
264 followers
Predibase is the first platform for reinforcement fine-tuning and the fastest way to customize and serve small open-source models that outperform GPT-4—all within your cloud. Fine-tune any model for your use case and deploy on serverless infrastructure that scales for demanding workloads. Trusted by enterprises like Checkr, Nubank, and Qualcomm, Predibase is built on open-source foundations and deployable in your private cloud, keeping your data and models fully under your control.
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.
Curious to see it in action?
👉 Join our launch webinar: https://go.predibase.com/introducing-first-reinforcement-fine-tuning-platform-on-predibase
👉 Request a demo and see how fast you can deploy your own models! https://predibase.com/request-a-...
We’re super excited to hear what you think! Drop your questions, feedback, or just say hi. 🚀🔥
Predibase
@wve @masump Hi Masum! Turbo LoRA trains speculative decoding heads alongside LoRA weights. The LoRA weights improve task performance, while the speculative heads predict multiple tokens in advance, allowing the model to generate up to 4 tokens per forward pass. This gives you the quality of LoRA with 3-4x the throughput at inference time.
Here’s our blog post on Turbo LoRA: https://predibase.com/blog/turbo-lora
Hope this helps!
Fable Wizard
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?
@jonurbonas That's where the reward functions come in! You can craft reward functions to steer your model's performance and teach it to recognize "what good looks like". So even if you only have a handful of good examples, you can start training your model just with reward functions. Check out more on our blog! https://predibase.com/blog/introducing-reinforcement-fine-tuning-on-predibase
Predibase
@jonurbonas To add to Will’s answer, we’ve come up with a special process to do SFT based warmups in cases where the tasks are very specific to give the base some knowledge about the task so it can use that as a starting point for RFT!
ThreeDee
This tool makes fine-tuning LLMs so much easier! It's a game-changer for improving model performance. 👍