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
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LLaMA
Spaces - Hugging Face
OpenAI
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Replicate
Fireworks Console
gpt-tokenizer playground
openai/tiktoken: tiktoken is a fast BPE tokeniser for use with OpenAI’s models.
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Raycast Pro
Amazon SageMaker (AMS SSPS)
openai/evals
LangChain
PyTorch
TensorFlow
ai - npm
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