Predibase

🚀 Introducing the first end-to-end platform for #Reinforcement Fine-Tuning! With just a dozen labeled examples, you can fine-tune models that outperform OpenAI and #DeepSeek on complex tasks. Built on the GRPO methodology that DeepSeek-R1 popularized, our platform lets you turn any open-source LLM into a reasoning powerhouse. 💡 In our real-world PyTorch to Triton transpilation case study, we achieved a 3x higher accuracy than OpenAI o1 and DeepSeek-R1 when writing GPU code – unlocking smarter, more efficient AI models. 🔗 What’s next? 📖 Read the blog to see how it works. 🎤 Join us for the launch webinar on 3/27 to dive deep into RFT. 🛠️ Try the #RFT Playground to see how it works. All links are in the comments. Let’s redefine what’s possible with fine-tuned AI! 🚀

🧨 DeepSeek is everywhere—except in production. Let’s be real: every AI team’s poked at DeepSeek-R1. But almost no one’s using it for real work. We surveyed 500+ AI professionals and found: 📊 57% have tested it 😬 3% have deployed it 🤷 47% still don’t know if it’s better than other models It’s the classic GenAI problem: 💡 Big promise 🧱 Bigger friction ⏳ Still waiting on proof And yet... 🔧 46% of teams want to customize it with LoRA or distill it down 💼 Most traction? Specialized use cases (not chatbots, sorry) The bottom line: Teams want to believe in DeepSeek. But without benchmarks or tools to make it usable, they’re stuck guessing. Wanna be the team that figures it out first? 👇 Fine-tune it. Deploy it. Own it. Free trial at Predibase. #LLMs #OpenSourceAI #DeepSeek #InfraMatters #InferenceStack #GenAI #LoRA #Distillation #MLOps #ModelServing #Predibase

Supervised fine-tuning is all about #memorizing facts. Reinforcement fine-tuning trains your model to actually #think 🧠 We've been training our own reasoning #LLMs and the biggest unlock has been #RFT (reinforcement fine-tuning). No labeled data. No memorization. Just smarter models through feedback and exploration. Last week Avi Chawla authored a great post comparing SFT vs. RFT along with the amazing diagram below and a notebook on how he used Predibase to turn Qwen-2.5-7b into a reasoning model with our managed RFT platform. Check out his post for the notebook and all the juicy details: https://lnkd.in/gvqdEJMD Highly recommend you join his newsletter Daily Dose of Data Science for lots of great content: https://lnkd.in/ggMx-aGM

Every extra millisecond your #LLM spends “thinking” is $$$ left on the table and results in more users abandoning your AI application. Stop burning #GPU cycles—start squeezing them. Next Thu, April 24 @ 10 AM PT, we’re unveiling our #Inference Engine 2.0 with a series of enhancements that turns any #opensource LLM, regardless of size, into a throughput monster that maximizes GPU output. In less than an hour, you'll see: • Where other inference stacks choke - the real‑world #latency traps that hinder throughput • Head‑to‑head numbers vs. #vLLM and other popular infra solutions for summarization, classification, and chat tasks • Performance knobs that flip like switches incl. speculative decoding, quantization, sub-second cold starts & #autoscaling - ready out of the box • Best practice architecture designs and much more! If “faster, cheaper, simpler” is tagged on your 2025 #AI goals, this webinar is your shortcut. 👉 Save a seat—or get the recording—right here: https://lnkd.in/gB8nj9rB

🐳 AI teams are testing DeepSeek—but nobody agrees on when to use it In our recent survey of 500+ AI professionals, DeepSeek-R1 is getting serious attention—but it's far from mainstream. Here’s what we uncovered: 📊 57% of teams have experimented with DeepSeek-R1 ⚠️ Only 3% have deployed it in production 🤷♂️ Nearly half are unsure how it stacks up to other models And the demand for customization is clear: 🔧 46% want fine-tuning or distillation options 🧪 The takeaway? DeepSeek-R1 has potential—but teams are still figuring out how to unlock it. 🚀 Ready to see if it fits your use case? Start experimenting on Predibase—free trial available. 👇 Link to full survey results in the comments #AI #LLM #DeepSeek #MLOps #Predibase #GenAI #MachineLearning #opensourcellms

Don't miss The Future of #PostTraining LLMs — live tomorrow at the #AI User Conference — in-person in SF and streaming online hosted by the AI User Group. LLMs were supposed to replace the need for #training. But the best AI teams are training them again after #pretraining. Why? Because post-training is where general-purpose models become domain experts. It’s how you move from “kind of useful” to “production-ready AI” Join Predibase CEO, Devvret Rishi, for his mainstage talk tomorrow as he unpacks: 🧠 What post-training really is—and why it matters more than ever 🔧 The latest methods: #SFT, #RFT, DPO, RLHF 🚀 Why the future of AI won’t separate inference and training—but blend them into a continuous loop Whether you’re deploying open-source models or building custom AI workflows, this is the shift you need to understand. 🗓️ Talk Time: Apr 17 @ 2:30pm 📍 Title: The Future of Post-Training LLMs 🎟️ Discounted Tickets for Predibase Friends: https://linktr.ee/AIUC25

🔥 Everyone’s talking about pre-trained #AI models... but the real magic? It happens after! 🎩 🪄 Our founder, Devvret Rishi just published a blog that dives into this very topic of post-training. What you can expect from his write-up: 🧠 What is #PostTraining with a detailed break-down of current techniques 🔑 Why it's the secret sauce behind truly powerful #LLMs 🥊 How teams are using post-training techniques to turn general models into domain-dominating #experts If you're still deploying out-of-the-box models, you're already behind. 👉 Find out what you're missing: https://lnkd.in/g6VuXvmi

🔐 Want to run #Llama4 at blazing fast speeds without sending a single token over the public internet? Now you can—with Predibase, you can deploy Meta Facebook's most advanced #opensource LLM's (Scout and Maverick) directly in your Virtual Private Cloud #VPC with just a few lines of code. And of course you can run it on Predibase's SaaS if you want to get started faster! Why it matters: ✅ #Private by design – No data ever leaves your environment ☁️ Cloud-#agnostic – Run it wherever your stack lives (AWS, GCP and Azure) 🚀 Fully managed – We handle #infra, you get blazing-fast inference 🛡️ Enterprise-ready – Serve and monitor Llama 4 with total control in a secure #SOC2-certified environment. 👉 Read how it works: https://lnkd.in/gMtZ_rS7 Your data. Your models. Your Cloud. Your Rules. Start building with the best open-source LLM—securely in your own environment.

Excited to be part of Google Cloud's inaugural set of partners for their Startup Perks program aimed at helping early stage builders innovate on the Google Cloud with the best data + #AI tools out there! 🎉 Through Startup Perks, #entrepreneurs can access exclusive #benefits and discounts from 20+ leading AI and technology companies including Predibase 🚀 Lots of amazing organizations participating - don't miss out on perks from Confluent Databricks Weights & Biases Mixpanel JFrog Datadog Chronosphere DataStax Mistral AI MongoDB NVIDIA Aiven GitLab Elastic Eleven Labs and more! 👉 Learn more: https://goo.gle/3YqY1Zx Thanks for the shoutout Mary Lynn Stryker!

Building production-grade #GenAI apps requires a robust set of tooling. Yujian Tang put together a nice map of the latest #LLM tech 👇 And of course if you want high speed #serving infra that actually also helps you improve your model quality, then Predibase is the only answer 💪