Built by AI leaders from Uber, Google, Apple and Amazon. Developed and deployed with the world’s leading organizations.

Bigger Isn’t Always Better

Fine-tune smaller task-specific LLMs that outperform bloated alternatives from commercial vendors. Don’t pay for what you don’t need.

Model Accuracy for JSON Generation

Efficient Fine-Tuning and Serving

Train and deploy task-specific open-source models in record time and under budget.

First-class fine-tuning experience

Predibase offers state-of-the-art fine-tuning techniques out of the box such as quantization, low-rank adaptation, and memory-efficient distributed training to ensure your fine-tuning jobs are fast and efficient—even on commodity GPUs.

The most cost-effective serving infra

Our unique serving infra–LoRAX–lets you cost-effectively serve many fine-tuned adapters on a single GPU in dedicated deployments. We provide free serverless inference up to 1M tokens per day / 10M tokens per month for prototyping, evaluation, and experimental use cases.

Fine-tuning, built for enterprise

Start owning and stop renting your LLMs. Predibase is built with enterprise-grade security and is SOC-2 compliant. Enterprise and VPC customers can download and export their trained models at any time, ensuring you always retain control of your IP.

The fastest way to fine-tune and deploy any open-source LLM

Fine-tune and serve any open-source LLM. Our proven, scalable infrastructure is available through either serverless fine-tuned endpoints or within your environment’s virtual private cloud.

Try Any Open Source LLM in an Instant

Stop spending hours wrestling with complex model deployments before you’ve even started fine-tuning. Deploy and query the latest open-source pre-trained LLM—like Llama-2, Mistral and Zephyr—so you can test and evaluate the best base model for your use case. Scalable managed infrastructure in your VPC or Predibase cloud enables you to achieve this in minutes with just a few lines of code.

# Deploy an LLM from HuggingFace
pb.deployments.create(
    name="my-llama-3-70b-deployment",
    description="Deployment of Llama 3 70b in Predibase Cloud",
    config=DeploymentConfig(
        base_model="llama-3-70b",
    )
)

# Prompt the deployed LLM
client = pb.deployments.client("my-llama-3-70b-deployment")
client.generate("Write an algorithm in Java to reverse the words in a string.")    

Efficiently Fine-tune Models for Your Task

No more out-of-memory errors or costly training jobs. Fine-tune any open-source LLM on the most readily available GPUs using Predibase’s optimized training system. We automatically apply optimizations such as quantization, low-rank adaptation, and memory-efficient distributed training combined with right-sized compute to ensure your jobs are successfully trained as efficiently as possible.

# Kick off the fine-tune job
adapter = pb.adapters.create(
    config=FinetuningConfig(
        base_model: "llama-3-70b",
        epochs: 3,
        learning_rate: 0.0002,
    ),
    dataset=my_dataset,
    repo="my_adapter",
    description='Fine-tune llama-3-70b with my dataset for my task.',
)

Dynamically Serve Many Fine-tuned LLMs In One Deployment

Our scalable serving infra automatically scales up and down to meet the demands of your production environment. Dynamically serve many fine-tuned LLMs together for over 100x cost reduction versus dedicated deployments with our novel LoRA Exchange (LoRAX) architecture. Load and query them in seconds.

Read more about LoRAX.

# Prompt your fine-tuned adapter instantly
client.generate(
        "Write an algorithm in Java to reverse the words in a string.",
        adapter_id="my_adapter/3", 
)
Enric Logo Color

By switching from OpenAI to Predibase we’ve been able to fine-tune and serve many specialized open-source models in real-time, saving us over $1 million annually, while creating engaging experiences for our audiences. Best of all we own the models.

Andres Restrepo, Founder and CEO, Enric.ai

Built on Proven Open-Source Technology

LoRAX

LoRAX (LoRA eXchange) enables users to serve thousands of fine-tuned LLMs on a single GPU, dramatically reducing the cost of serving without compromising on throughput or latency.

Ludwig

Ludwig is a declarative framework to develop, train, fine-tune, and deploy state-of-the-art deep learning and large language models. Ludwig puts AI in the hands of all engineers without requiring low-level code.

Use Cases

Predibase lets you fine-tune any open-source LLM for your task-specific use case.

Classification

Classification

Automate the labor-intensive process of manually categorizing documents, content, messages, and more.

Information Extraction

Information Extraction

Extract structured information from unstructured text for downstream tasks.

Customer Sentiment

Customer Sentiment

Use an LLM to understand how your customers feel about your products or services.

Customer Support

Customer Support

Automatically classify support issues, generate a customer response, and save your organization time and money.

Code Generation

Code Generation

Automate code generation with an LLM to significantly reduce the burden of tasks like code completion or docstring generation.

Named Entity Recognition

Named Entity Recognition

Identify predefined categories of objects in a body of text for inline term definitions or enhancing question and answering systems.

Many More

Many More

Predibase can support your LLM use case, no matter how complex. Contact us to learn more about how we can help you with AI today.

Ready to efficiently fine-tune and serve your own LLM?