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

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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