Decart & Cerebrium Commit to Empowering Next Million Users With LLM Applications

The Decart and Cerebrium partnership allows you to process 1 million tokens of Llama 2 70B for just $0.50

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Fine-tuning and deploying a Stable Diffusion model Pipeline with Cerebrium

Stable Diffusion is coming up on its one year anniversary after being released by StablilityAI and has since taken the world by storm with no signs of slowing down. A year into the release, we at Cerebrium are still seeing customers investing time and research into developing some phenomenal applications using Stable Diffusion.

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Fine-tuning and deploying a LLaMA-based support bot with Cerebrium and LLamaIndex

Fine-tuning and deploying a LLaMA-based support bot with Cerebrium and LLamaIndex

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Understanding LLaMA: A Deep Dive into Large Language Model Meta AI

LLaMA, or Large Language Model Meta AI, stands as a significant milestone in the research landscape of natural language processing developed by the FAIR team of Meta AI

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Customizing the Prompt for Img2Img Model: Techniques for Guiding Image Generation

This guide is here to help you master the art and science of prompt customization. We'll delve into the role of the text prompt, explore a variety of techniques for customizing it, and discuss strategies for optimizing your prompts for desired outputs. By the end, you'll have a solid understanding of how to communicate effectively with the Img2Img model and guide it towards generating the images you envision.

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Deploying the Prebuilt Img2Img Model with Cerebrium: A Step-by-Step Guide to Image Generation

Img2Img models can generate images from an input image, providing numerous possibilities. The one we're deploying today is the Image-to-Image text-guided generation with Stable Diffusion, a prebuilt model offered by Cerebrium. This model allows us to give a text prompt and an initial image to guide the generation of new images. Let's see how we can do this with Cerebrium.

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Introduction to Stable Diffusion Img2Img: Shaping the Future of Image Generation

This article will specifically focus on the prebuilt version of Stable Diffusion Img2Img available on Cerebrium as a representative example of how Image-to-Image models work. Cerebrium's main function centers around fine-tuning and deploying machine learning models to serverless GPUs. More information is available in the Cerebrium docs.

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Speed up training and inference of GPT-Neo 1.6B by 45+% using DeepSpeed

In this tutorial we are going to be looking at using DeepSpeed speed up fine-tuning and inference of GPT-Neo.

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Improve Stable Diffusion inference by 50%+ with TensorRT or AITemplate

GPUs play an important role in the deployment and inference of ML models, especially in large-scale models.

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Creating AvatarAI.me using Supabase and Cerebrium

@levelsio created AvatarAI.me in November that creates AI generated avatars based on photos of you. You can create profile pictures, pet portraits, professional LinkedIn photos and much more!

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Creating a Whatsapp voicenote transcriber using Whisper in 1 hour

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Why and how to implement a feature store with Feast

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How to monitor the performance of a fine-tuned GPT-Neo Model

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Train models like Stable Diffusion and Bloom (175B) using your own computer

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How to speed up Diffusion to a 2 second inference time 500x improvement

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How does Claude, the new LLM from Anthropic compare to ChatGPT? A serious contender

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SetFit outperforms GPT-3 while being 1600x smaller

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Tracking model versions and experiments with DVC and Dagshub

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Practical ways to measure the success of your ML Models in production

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Monitoring ML Models in Production using Arize — Part (2/2)

Following on from our previous article where we showed you how to setup Arize...

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Meta AI has released a new AI model allowing you to generate videos from text

With the releases of large language and text-to-image (T2I) models such as GPT-3, DALL-E and Stable...

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Monitoring ML Models in Production using Arize — Part (1/2)

Once you have a model that is live and in production, the first thing you want...

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Setting up Python in your Data Science and ML Development Environment

How to best setup your Python environment for machine learning development

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