You can achieve better performance using fewer compute resources by fine-tuning smaller open-source LLMs with task-specific data. We’ve used this approach to innovate customer service and believe that Predibase offers the most intuitive platform for efficiently fine-tuning LLMs for specialized AI applications.
Sami Ghoche, CTO & Co-Founder
How it works
Powerfully Efficient Fine-tuning
Fine-tune smaller faster task-specific models without comprising performance.
Configurable Model Training
Built on top of Ludwig, the popular open-source declarative ML framework, Predibase makes it easy to fine-tune popular open-source models like Llama-2, Mistral, and Falcon through a configuration-driven approach. Simply specify the base model, dataset, and prompt template, and the system handles the rest. Advanced users can adjust any parameter such as learning rate or temperature with a single command.
# Specify a Huggingface LLM to fine-tune llm = pc.LLM("hf://meta-llama/Llama-2-13b-hf") # Kick off the fine-tune job job = llm.finetune( prompt_template=prompt_template, target="output", dataset="s3_bucket/code_alpaca", repo="finetune-code-alpaca" ) # Stream training logs and metrics job.get()
Cost-Effective Model Serving
Put 100s of fine-tuned models into production for the cost of serving one.
Dynamic Model Serving with LoRAX
Reduce deployment costs by over 100x by dynamically serving many fine-tuned LLMs on a shared set of GPU resources. Our novel open-source LoRA Exchange (LoRAX) serving architecture provides a series of optimizations —such as Dynamic Adapter Loading, Tiered Weight Caching, and Continuous Multi-Adapter Batching—right out-of-the-box so you can serve 100s of fine-tuned LLMs at fraction of the cost.
Read more about LoRAX.
We see clear potential to improve the outcomes of our conservation efforts using customized open-source LLMs to help our teams generate insights and learnings from our large corpus of project reports. It has been a pleasure to work with Predibase on this initiative.
Dave Thau, Global Data and Technology Lead Scientist, WWF
Secure Private Deployments
Build in your environment and serve anywhere on world class serverless cloud infra.
Flexible, Secure Deployments
Deploy models within your private cloud environment or the secure Predibase AI cloud with support across regions and all major cloud providers including Azure, AWS, and GCP. No need to share your data or surrender model ownership.
Read the Report
Insights from 150 developers and data scientists on the top challenges with LLMs and how to overcome.
Watch the Webinar
Learn when and how to efficiently fine-tune open-source LLMs on your data in your own cloud including best practices like LoRA.
Read the Blog
Learn about this new innovative deployment approach that dynamically serves many fine-tuned LLMs on a single GPU at no additional cost.