If you wonder how Large Language Models (LLMs) work and aren’t afraid of getting a bit technical, don’t miss [Brendan Bycroft]’s LLM Visualization. It is an interactively-animated step-by-step walk-through of a GPT large language model complete with animated and interactive 3D block diagram of everything going on under the hood. Check it out!

The demonstration walks through a simple task and shows every step. The task is this: using the nano-gpt model, take a sequence of six letters and put them into alphabetical order.

A GPT model is a highly complex prediction engine, so the whole process begins with tokenizing the input (breaking up words and assigning numerical values to the chunks) and ends with choosing an appropriate output from a list of probabilities. There are of course many more steps in between, and different ways to adjust the model’s behavior. All of these are made quite clear by [Brendan]’s process breakdown.

We’ve previously covered how LLMs work, explained without math which eschews gritty technical details in favor of focusing on functionality, but it’s also nice to see an approach like this one, which embraces the technical elements of exactly what is going on.

We’ve also seen a much higher-level peek at how a modern AI model like Anthropic’s Claude works when it processes requests, extracting human-understandable concepts that illustrate what’s going on under the hood.

At first glance, trying to play chess against a large language model (LLM) seems like a daft idea, as its weighted nodes have, at most, been trained on some chess-adjacent texts. It has no concept of board state, stratagems, or even whatever a ‘rook’ or ‘knight’ piece is. This daftness is indeed demonstrated by [Dynomight] in a recent blog post (Substack version), where the Stockfish chess AI is pitted against a range of LLMs, from a small Llama model to GPT-3.5. Although the outcomes (see featured image) are largely as you’d expect, there is one surprise: the gpt-3.5-turbo-instruct model, which seems quite capable of giving Stockfish a run for its money, albeit on Stockfish’s lower settings.

Each model was given the same query, telling it to be a chess grandmaster, to use standard notation, and to choose its next move. The stark difference between the instruct model and the others calls investigation. OpenAI describes the instruct model as an ‘InstructGPT 3.5 class model’, which leads us to this page on OpenAI’s site and an associated 2022 paper that describes how InstructGPT is effectively the standard GPT LLM model heavily fine-tuned using human feedback.

Ultimately, it seems that instruct models do better with instruction-based queries because they have been programmed that way using extensive tuning. A [Hacker News] thread from last year discusses the Turbo vs Instruct version of GPT 3.5. That thread also uses chess as a comparison point. Meanwhile, ChatGPT is a sibling of InstructGPT, per OpenAI, using Reinforcement Learning from Human Feedback (RLHF), with presumably ChatGPT users now mostly providing said feedback.

OpenAI notes repeatedly that InstructGPT nor ChatGPT provide correct responses all the time. However, within the limited problem space of chess, it would seem that it’s good enough not to bore a dedicated chess AI into digital oblivion.

If you want a digital chess partner, try your Postscript printer. Chess software doesn’t have to be as large as an AI model.

Giskard is an open-source AI model quality testing tool that helps data scientists and engineers build safer, more reliable AI systems. The platform was built by AI engineers for AI engineers. It’s completely open source and designed to help teams and developers build more robust, trustworthy AI models. To use the platform, you can get […]

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Faraday.dev lets you easily run open-source LLMs (chatbots) on your computer. Once you’ve got the program and AI models installed, no internet connection is required to use and interact with the AI LLMs. Faraday.dev supports a wide range of LLaMA-based models, including WizardLM, GPT4-x-Alpaca, Vicuna, Koala, Open Assistant, PygmalionAI, and more. You have the option […]

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Oobabooga is an open-source Gradio web UI for large language models that provides three user-friendly modes for chatting with LLMs: a default two-column view, a notebook-style interface, and a chat interface. This flexibility allows you to interact with the AI models in a way that best suits your needs, whether it’s for writing, analysis, question-answering, […]

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Code Llama is a suite of large language models released by Meta AI for generating and enhancing code. It includes foundation models for general coding, Python specializations, and models tailored for following instructions. Key features include state-of-the-art performance, code infilling, large context support up to 100K tokens, and zero-shot ability to follow instructions for programming […]

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ChatGLM-6B is an open-source, bilingual conversational AI LLM based on the General Language Model (GLM) framework. It has 6.2 billion parameters and can be deployed locally with only 6GB of GPU memory. This model allows for natural language processing in both Chinese and English, question answering, task-oriented dialogue, and easy integration via API and demo […]

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Perplexity AI is an AI chat and search engine that uses advanced technology to provide direct answers to your queries. It delivers accurate answers using large language models and even includes links to citations and related topics. It is available for free via web browser and also on mobile via the Apple App Store. Using […]

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Codestral is a powerful 22B parameter AI model from Mistral AI. This open-weight model is designed specifically for code generation across over 80 programming languages including Python, Java, C++, JavaScript and more. Codestral offers impressive performance, outperforming other models on benchmarks like HumanEval and RepoBench with its large 32k token context window. The model is […]

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Langtail is a platform that helps you develop and deploy LLM-powered applications faster. It provides tools for prompt engineering, testing, observability, and deployment – all in one place. You can collaborate with your team, iterate quickly, and get your LLM apps to production with confidence.

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Mistral AI is a large language model and chat assistant tool. You can access the chatbot via the Mitral website by clicking on “Talk to le Chat“, or if you prefer a local setup then you can download and run the model files on your own hardware. The creators of Mistral describe it as an […]

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Stability AI, the team behind the popular AI art tool Stable Diffusion, has announced the launch of its latest creation: StableLM, a suite of text-generating AI models designed to rival systems like OpenAI’s GPT-4 and ChatGPT. Available in “alpha” on GitHub and Hugging Face, StableLM can generate both code and text and has been trained […]

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