Predibase (acquired by Rubrik)

Founded 2021

Fine-tune and serve open-source large language models on managed cloud infrastructure

You can use Predibase to fine-tune and serve open-source large language models on managed cloud infrastructure. It provides parameter-efficient fine-tuning techniques to train custom models quickly on proprietary datasets without managing complex hardware clusters. You can deploy multiple fine-tuned adapters on a single GPU using the LoRAX serving framework to decrease hosting expenses. Engineering teams can benchmark model accuracy, run batch inference jobs, and connect endpoints directly into internal software applications through standard REST APIs, Ludwig configurations, and native Python development kits.

What Predibase (acquired by Rubrik) does

Upload and preprocess training datasets for fine-tuningConfigure parameter-efficient fine-tuning jobs using YAML or PythonDeploy fine-tuned LoRA adapters to active inference endpointsTest model prompt outputs in an interactive developer playgroundMonitor GPU utilization and latency metrics across active deploymentsExecute high-volume batch inference via REST API callsCompare evaluation metrics between base and fine-tuned checkpointsDeploy multiple fine-tuned models on a single GPU using LoRAXFine-tune open-source models using parameter-efficient techniques like LoRA and QLoRAServe models with built-in dynamic adapter switchingTrain custom models using declarative configuration files via LudwigRun batch inference jobs on large datasets with automatic hardware scalingDeploy private endpoints inside dedicated cloud environmentsEvaluate and benchmark fine-tuned models against base checkpoints

Frequently asked

Hugging Face AWS

AWS

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