Predibase

Predibase is the fastest, most efficient way to fine-tune and serve open-source #LLMs. 🔥 As the first platform designed to help #engineers productionize open-source AI, Predibase makes it easy to customize LLMs on scalable managed infra in your cloud—at a fraction of the cost of commercial LLMs. Don't believe us? Try it out for free! Fine-tune and serve Llama-2 with our two week free trial: https://pbase.ai/3SnGq2z

Top 3 challenges ML engineers face putting #SLMs into production: 1️⃣ Performance Bottlenecks: poor #latency = slow response times = bad customer experiences. 2️⃣ Engineering Complexity: building and managing scalable and reliable serving #infra for open-source SLMs is resource-intensive and requires deep LLMOps expertise. 3️⃣ High Infra Costs: always-on deployments that don't #autoscale up/down blow through budgets as use cases and traffic grows. How do you overcome these challenges? 🤔 Check out this article in Marktechpost Media Inc. for our playbook: https://lnkd.in/gdpGkGmQ

Fine-tuning #SLMs is the easy part. Putting them into #production and hitting SLAs is much more complex. Joins us to learn how to optimize inference for your fine-tuned models: 💣 💥 Landmines to avoid when producitionizing SLMs 🚀 How to 4x #throughput with Turbo LoRA, Spec Decoding and FP8 🔄 What it takes to build #reliable infra w/ multi-region load balancing and auto-failovers 🚦 How to scale for production traffic with #autoscaling GPUs Save your spot: https://lnkd.in/gMkcAmfX

Open-source #SLMs are closing the gap with large commercial models like #GPT4 💡 Fine-tune those SLMs and they destroy commercial LLMs -> in our benchmarks across 30 tasks fine-tuned Meta Facebook's #llama3.1 outperforms GPT-4 by nearly 20% 😵 🥊 What's even more amazing is that #finetuned open-source SLMs can outperform fine-tuned commercial models like GPT-4o-mini 💰 ➡ See our latest #benchmarks for Llama-3.1 and more on our finding index: https://lnkd.in/gPa-tkrb. ➡ And try out fine-tuning and serving your own SLMs with $25 in free #credits: https://lnkd.in/gTdE8gxh

Over 10,000 #SLMs have been fine-tuned on Predibase! 🎉 Why do leading AI companies like Checkr, Inc., Nubank and Upstage fine-tune and #serve models on Predibase? 🎯 Better Accuracy: Fine-tuned models on Predibase beat #GPT4 by 5-20% (see our leaderboard: https://lnkd.in/gGp5z5hn) 🏎 Faster Inference: Unlock 2-4x speed improvements with #TurboLoRA, FP8, and more 💰 Cost Efficient: #autoscaling GPUs and multi-LoRA serving with LoRAX Get started with $25 in free credits: pbase.ai/getstarted

Over 10,000 #SLMs have been fine-tuned on Predibase! 🎉 Why do leading AI companies like Checkr, Inc., Nubank and Upstage fine-tune and #serve models on Predibase? 🎯 Better Accuracy: Fine-tuned models on Predibase beat #GPT4 by 5-20% (see our leaderboard: https://lnkd.in/gGp5z5hn) 🏎 Faster Inference: Unlock 2-4x speed improvements with #TurboLoRA, FP8, and more 💰 Cost Efficient: #autoscaling GPUs and multi-LoRA serving with LoRAX Get started with $25 in free credits: pbase.ai/getstarted

Speed matters when it comes to #inference -> better throughput means reduced #latency and cost ⚡ Save your spot for our upcoming webinar to learn how you can optimize your #SLM deployments to improve throughput by 4x with #FP8 and Turbo LoRA, our new fine-tuning technique that improves both model quality and throughput (and you can't get it anywhere else!) Register here: https://lnkd.in/gMkcAmfX

#CodeGen is a popular use case for LLMs, but you don't need a huge or proprietary model to get good performance. Check out this #tutorial — sample data + notebook included — to learn how to fine-tune a small open-source model (#SLM) LLama-3-8b for SQL generation, achieving a 170%-450% lift over #base model performance. This was all done with a few lines of code and #synthetic data. Happy fine-tuning! https://lnkd.in/erWkgGRT

Which #slm should I fine-tune? How does it perform for my use case? How much improvement can I get over #GPT4? We get lot of questions like these and to help you answer them, we've created the Fine-tuning Leaderboard: https://lnkd.in/gGp5z5hn. We #finetuned 20+ models on 30 tasks with comparisons to GPT4 and 4o-mini. We even fine-tuned 4o-mini for a real apples to apples comparison (surprise: many #OSS models are better 😎) You can even filter by model size, model name and task. Check it out!

The Predibase Inference Engine's GPU autoscaling offers a practical way to reduce deployment costs for serving small language models. Always-on GPU setups incur expenses whether they’re needed or not, while autoscaling adjusts resources based on demand. For example, an enterprise workload that would cost over $213,000/year with an always-on deployment can drop to under $155,000/year with autoscaling—saving nearly 30% with no impact on performance. (And both options are still more affordable than running fine-tuned GPT-4o-mini.) Autoscaling ensures you get the performance you need when you need it, without paying for idle infrastructure. Learn more about how our Inference Engine can streamline your SLM deployments: https://pbase.ai/3YqHu8x