In this episode of Syntax, Wes and Scott talk about understanding the integration of different components in AI models, the choice between traditional models and Language Learning Models (LLM), the relevance of the Hugging Face library, demystify Llama, discuss spaces in AI, and highlight available services.

Show Notes

• 00:25:20 Welcome
• 00:55:00 Syntax Brought to you by Sentry
• 01:17:00 Understanding how the pieces fit together
• 02:31:18 Models or LLM?
• 04:43:22 What about Hugging Face?
• 08:05:18 What’s Llama?
• 08:51:15 What are spaces?
• 09:29:06 Services available to you
• 12:26:16 What are tokens in AI?
• 17:38:18 What is temperature with AI?
• 20:33:08 Using top_p
• 21:06:00 Using fine-tuning to extend existing models
• 22:11:19 Prompts are what you send to the model
• 23:17:00 Streaming
• 24:48:17 Embeddings
• 27:34:17 OpenAI maintains Evals
• 28:40:14 Different libraries for working with AI

• Hugging Face
• Creator of Swift, Tesla Autopilot & Tensorflow. New AI language Mojo with Chris Lattner
• LLaMA
• Spaces - Hugging Face
• OpenAI
• Anthropic \ Introducing Claude
• Replicate
• Fireworks Console
• tokenizer.dev/">gpt-tokenizer playground
• openai/tiktoken: tiktoken is a fast BPE tokeniser for use with OpenAI’s models.
• Supper Club × OpenAI, Future of programming, LLMs, and Math with Andrey Mishchenko
• Raycast Pro
• Amazon SageMaker (AMS SSPS)
• openai/evals
• LangChain
• PyTorch
• TensorFlow
• ai - npm

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