It doesn’t have a code interpreter. When I did my previous post creating an SVG, I had to make the SVG in o1 and then switch the chat to 4o make it a downloadable file, and once you do that it won’t let you switch back because o1 doesn’t support code.
LLMS
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Comparing o1 model capabilities versus REPL
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It isn’t important; this task isn’t practical at all; it’s just a compact illustration of the broadened class of tasks o1 can do vs earlier models, e.g. tasks that seem to require a guess-and-check loop with dozens of iterations REPL is still best for many real-world tasks
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User observation about OpenAI model tool access
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Yeah that’s why I said “claims to be” — everything OpenAI releases is technically a black box but I have no reason to doubt them; it seems clear from using it that o1 doesn’t have tool access even when it could use it
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LLM struggles with counting and arithmetic despite code workarounds
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Yeah, it’s actually not a hard problem in code — you just iterate over x values from 0 until you find one that works. What makes it remarkable here is just that counting letters and arithmetic are traditionally hard for LLMs and o1 claims to not be using tools/code
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Guide to Building Production-Ready LLM Applications
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Unlock the Secrets to Building Production-Ready LLMs Start Reading Now https://
tinyurl.com/3dx62nuk Dive into the ultimate guide for developers, engineers, and AI enthusiasts aiming to create scalable and reliable LLM-powered applications! What’s Inside this 470-Page -
Setting Up a Query Engine for LLM Context Retrieval
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& Setting up a query engine The query engine takes a query string & use it to fetch relevant context and then sends them both as a prompt to the LLM to generate a final natural language response. Here's how you set it up:
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Creating Custom Prompt Templates for LLM Context Refinement
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Creating a prompt template A custom prompt template is use to refine the response from LLM & include the context as well:
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Loading Knowledge Base Documents in LlamaIndex for RAG
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& : Loading the knowledge base A knowledge base is a collection of relevant and up-to-date information that serves as a foundation for RAG. In our case it's the docs stored in a directory. Here's how you can load it as document objects in LlamaIndex:
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Building a Local RAG System with LlamaIndex and Ollama
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Before we begin, take a look at what we're about to create!
— Akshay 🚀 (@akshay_pachaar) 7 décembre 2024
Here's what you'll learn:
– @Llama_Index for orchestration
– @qdrant_engine to self-host a vector DB
– @Ollama for locally serving Llama-3.3
– @LightningAI for development & hosting
Let's go! 🚀 pic.twitter.com/qIKJjv0mxJBefore we begin, take a look at what we're about to create! Here's what you'll learn: – @Llama_Index for orchestration
– @qdrant_engine to self-host a vector DB
– @Ollama for locally serving Llama-3.3
– @LightningAI for development & hosting Let's go! -
Building a Local RAG Application with MetaAI’s Llama-3.3
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Let's build a RAG app using MetaAI's Llama-3.3 (100% local):