Style your Kaggle Notebook

Style your Kaggle Notebook

I decided to restart Kaggle after two years, and to get myself pumped up, I started by designing the style for my notebook.
If you’re getting tired of Markdown, give it a try!

Styled Notebook Sample

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Modify the local bolt.new interface to allow input of the API key

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Modify the local bolt.new interface to allow input of the API key In bolt.new , the API key can be configured using environment variables, but this time, we will modify it to allow input of the API key directly from the interface. Modification Details Sidebar We will enable API key input directly from the sidebar. In the sidebar, which currently displays chat history, we add a new form at the top for entering the API key. To achieve this, modify the file bolt.new/app/components/sidebar/Menu.client.tsx . First, import the function to handle API key input: import { ApiKeyInput } from '~/components/sidebar/ApiKeyInput'; The bolt.new/app/components/sidebar/ApiKeyInput.tsx file will be created later. Next, add a form for entering the API key within the menu. ... return ( <motion.div ref={menuRef} initial="closed" animate={open ? 'open' : 'closed'} variants={menuVariants...
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Quick Paper Overview: More Agents Is All You Need

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Quick Paper Overview: More Agents Is All You Need I found this paper fascinating, so I’d like to provide a quick overview of More Agents is All You Need . 1. What’s It About? By leveraging multiple LLMs in a simple sampling-and-voting approach, this study demonstrates significant performance improvements in LLMs. The performance boost is observed across tasks of varying difficulty, with particularly notable gains in more challenging tasks. While both small and large LLMs benefit, smaller models exhibit the most pronounced improvements. This method can also be combined with existing techniques like Chain-of-Thought (CoT), further enhancing performance in certain tasks. 2. What is Differences from Prior Work In previous research, methods like CoT-SC applied voting to diverse answers generated by chain-of-thought prompts, particularly for reasoning tasks. This study, however, explores whether simply increasing the number of agents (LLMs) without relying...
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How to Run stable-diffusion-3.5-large-turbo on Google Colab

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ow to Run stable-diffusion-3.5-large-turbo on Google Colab stable-diffusion-3.5-large-turbo is a high-precision text-to-image model. This guide will explain how to set up and run the model on Google Colab. Prerequisites Visit Huggingface . To use stable-diffusion-3.5-large-turbo, you need a Huggingface account. If you don’t already have one, please create an account. Once signed up, you’ll see the following screen: Enter the required information, and you’ll gain access to the model immediately. If you wish to download and use the model, you’ll need an access token. Create one from your account page: Navigate to your account page via the profile icon in the upper-right corner, go to the Access Token tab, and create a token by selecting Create new token . Running the Code Install Required Libraries First, install the necessary libraries in Google Colab: ! pip install --quiet -U transformers The -U option updates the library to its latest versio...
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