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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