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Introducing Compute Orchestration

Foundation Models

Fine-tune for higher accuracy at a fraction of the cost

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What are Foundation Models?

Foundation models, also known as base models or pre-trained models, are massive models that have been trained on vast amounts of data. These models are a starting point for many Vision and NLP tasks, such as image generation,  question answering, text generation, translation, sentiment analysis, and more.

Clarifai makes it easy to fine-tune foundation models for specific tasks
What are Foundation Models

Leverage foundation models, speed development, increase accuracy and reduce cost

Rapidly deploy models yourself

Use our UI or our SDKs to upload your own model or choose from thousands of the world’s best models in our community. Once a model is uploaded, it’s automatically available for any amount of production traffic. Clarifai solves the ML Ops headaches for you so you can focus on building value. 

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Transfer Learn on top of FMs

Harness the power of any foundation model's embeddings as the base for your own fine-tuned model. Fine-tuned models train an order of magnitude faster and cheaper for domain-specific tasks.  

Automate labeling with GPT3.5 and other FMs

Use the outputs of foundation models in workflows to automate tasks. For example, while GPT 3.5 can complete labeling tasks easily, the process of taking its outputs and applying them to thousands of data points is laborious. Clarifai can create labeling automation.

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Easily try and evaluate multiple models

Foundation models wrapped in the Clarifai API can be easily swapped out or fine-tuned again using the same data. We provide a common interface for all foundation models we offer, and it's a simple process to evaluate new ones for your use cases.

Segment and vectorize large documents

The largest LLMs can only handle up to 20,000 words of factual information for answering questions. With Clarifai, you can provide 100's of pages of information for your models to use.

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Add prompt templates to simplify repeat LLMs use cases

Many tasks you’ll need LLMs for are repeatable. By making these part of your Clarifai application, you won’t need to re-enter prompts the same way every time.

Bring the power of sustainable generative AI to your enterprise

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Data Privacy
Enterprises have proprietary data and unique needs. With Clarifai, you securely control where you are deployed and how your data is accessed or used to train a model.
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Collaboration with AI Experts
Partner with AI researchers and specialists to develop customized solutions tailored to specific business needs, ensuring smooth integration of generative AI technology.
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Prioritizing AI-Ready Infrastructure
Build your projects on a robust, AI-ready infrastructure, designed to seamlessly integrate and optimize the performance of foundation models.
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Establishing Ethical Guidelines
Adhere to rigorous ethical guidelines to ensure responsible and fair use of foundation models, upholding trust and integrity in AI applications.
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Clarifai: Your End-to-End AI Solution

Together with our patented AI platform, Clarifai provides a solution including data preparation, model development, and operationalization so you build and deploy models at scale.
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Platform
AI Platform
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Automated data labeling
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Model training & evaluation
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Data search & management

Resources

AI Foundation Models: Choosing The Best Model For Your Use Case
AI Foundation Models: Choosing The Best Model For Your Use Case
AI is rapidly advancing through foundation models, master linguists at their core, forming the bedrock for diverse AI applications.
Generative AI Foundation Models & Applications
Generative AI Foundation Models & Applications
Learn how to accelerate your Generative AI development, increase accuracy, and reduce costs with Clarifai
Top 10 Open Source Large Language Models In 2023
Top 10 Open Source Large Language Models In 2023
Generative transformer models represent a breakthrough that has given rise to an era of generative AI.

Clarifai’s patented AI platform accelerates model building and operationalization at scale.