6. Character AI Talk to pre-programmed characters, from historians to famous people, or create your own. Free to use. http://
Character.ai
LLMS
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Character AI: dialogue with pre-programmed or created characters
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Explain Like I’m Five: Turn complex concepts into easy explanations
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2. Explain Like I'm Five (this may just be a ChatGPT wrapper but UI is a 9/10) An ELI5 machine that turns complex concepts into easy to understand explanations. Choose between various levels of dumbing down – a clean and simple tool. Great for kids. https://
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Most Powerful AI Chatbots of 2023 and Their Capabilities
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AI-powered chatbots have taken the internet by storm lately, but not only can they automate operational tasks but also open a wide array of newer functions. Want to discover the most powerful AI chatbots of 2023 and what they can do? Tap here: https://
bit.ly/3IVF2zA -
Meet Claude: AnthropicAI’s Rival to ChatGPT
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"Meet Claude: @AnthropicAI
's Rival to ChatGPT" Through 40 screenshot examples, we explore the talents and limitations of ChatGPT's first real competitor. My first writing for @Scale_AI
, coauthored with @spencerpapay
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Clip of Andrej’s hand-coded GPT model trained on Shakespeare
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Clip of Andrej's hand-coded GPT model in action, trained on a corpus of Shakespeare text: pic.twitter.com/TpZPHjz3dS
— Riley Goodside (@goodside) 17 janvier 2023Clip of Andrej's hand-coded GPT model in action, trained on a corpus of Shakespeare text:
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Karpathy’s video: building GPT, a Transformer tutorial from scratch
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Great new video from @karpathy
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Exploring prompt optimization and minimization techniques
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There’s an optimization aspect I haven’t investigated fully. Would be curious to come up with a prompt minimizer that could process the natural language version down into its smallest identical prompt
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Training a 10M Parameter GPT Model on Shakespeare in 15 Minutes
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We get a ~10M parameter model trained for about 15 minutes on 1 GPU on all of Shakespeare concatenated into one 1MB file. We then sample infinite fake Shakespeare from our baby GPT. Can you spot which one is real? At only 10M params on 1M characters, from-scratch, I hope so 🙂
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Building Transformers: Self-Attention, Training, and GPT-3 Comparison
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The second ~1hr builds up the Transformer: multi-headed self-attention, MLP, residual connections, layernorms. Then we train one and compare it to OpenAI's GPT-3 (spoiler: ours is around ~10K – 1M times smaller but the ~same neural net) and ChatGPT (i.e. ours is pretraining only)
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Transformer Attention Mechanism: Baseline Model and Message Passing Introduction
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First ~1 hour is 1) establishing a baseline (bigram) language model, and 2) introducing the core "attention" mechanism at the heart of the Transformer as a kind of communication / message passing between nodes in a directed graph.