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
@predibase
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Fine-tuned Open-source SLMs Outperform GPT-4 by 20%
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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
's #llama3.1 outperforms GPT-4 by nearly 20% What's even more amazing is that -
10000 SLMs Fine-tuned on Predibase Platform
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Over 10,000 #SLMs have been fine-tuned on Predibase! 🎉
— Predibase by Rubrik (@predibase) 23 octobre 2024
Why do leading AI companies like #Checkr, #Nubank and #Upstage fine-tune and #serve models on Predibase?
🎯 Better Accuracy: Fine-tuned models on Predibase beat hashtag#GPT4 by 5-20% (see our leaderboard:… pic.twitter.com/KR6blwhg9QOver 10,000 #SLMs have been fine-tuned on Predibase! Why do leading AI companies like #Checkr, #Nubank and #Upstage fine-tune and #serve models on Predibase? Better Accuracy: Fine-tuned models on Predibase beat hashtag#GPT4 by 5-20% (see our leaderboard:
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Optimize SLM Inference Speed with FP8 and Turbo LoRA
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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 #finetuning technique that
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Fine-tune Llama-3-8b for SQL Code Generation with Small Models
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#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
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Fine-tuning Leaderboard: Which SLM to choose for your use case?
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Which #slm should I fine-tune?
— Predibase by Rubrik (@predibase) 18 octobre 2024
How does it perform for my use case?
How much improvement can I get over hashtag #GPT4?
We get lot of questions like these and to help you answer them, we've created the Fine-tuning Leaderboard: https://t.co/fqCTI4thOY
We #finetuned 20+ models… pic.twitter.com/AQ2sLHWV5fWhich #slm should I fine-tune? How does it perform for my use case? How much improvement can I get over hashtag #GPT4? We get lot of questions like these and to help you answer them, we've created the Fine-tuning Leaderboard: https://
predibase.com/fine-tuning-le
aderboard
… We #finetuned 20+ models -
GPU Autoscaling Reduces Deployment Costs by 30%
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Our Inference Engine's #GPU autoscaling can cut deployment costs by 30%. Instead of paying for idle resources with always-on setups, autoscaling matches GPU use to real-time demand. Smarter infrastructure means better performance without overspending. #AI https://
pbase.ai/4864xc1 -
Predibase Inference Engine: 4x Faster, 50% Cost Reduction
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Introducing the Predibase Inference Engine: 4x faster; 50% the cost. The best serving platform for fine-tuned #SLMs! Accelerate deployments with Turbo LoRA, FP8, and GPU autoscaling. Serve 100s of adapters on one GPU. Learn more: https://
pbase.ai/3NmLpwO #AI #infrastructure #LLM -
Small Language Models and Agentic Systems: The Future of GenAI
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Small language models (SLMs) and agentic systems are the future of GenAI. Why? Unlike massive models, SLMs are lightweight, faster, and more cost-effective. And when fine-tuned, SLMs outperform larger LLMs like GPT-4. Paired with agentic systems, fine-tuned SLMs can
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Checkr Optimizes Hiring with Fine-Tuned Open Source Small Models
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@checkr , Inc. is changing the way companies hire with #AI-driven automation and their doing it with small models (#SLMs)! Check out our latest case study to hear how Vlad Bukhin and the Checkr team fine-tuned small #opensource models that are more accurate, 30x #faster