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.
TOOLS
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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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ChatGPT Plans Engineers Are Currently Using
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Be honest: for the engineers that follow me, what ChatGPT plan are you currently on?
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Creating a Chat UI Interface with Streamlit for RAG Applications
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The Chat interface We create a UI using Streamlit to provide a chat interface for our RAG application. The code for this & all we discussed so far is shared in the next tweet! Check this out
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Understanding Embedding Models: Text to Numerical Representation
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The embedding model Embedding is a meaningful representation of text in form of numbers. The embedding model is responsible for creating embeddings for the document chunks & user queries.
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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):