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Capabilities of LLMs 🤯
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Daniel Whitenack and Chris Benson

· 38 Minutes

PodcastPractical AI

Capabilities of LLMs 🤯

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Large Language Model (LLM) capabilities have reached new heights and are nothing short of mind-blowing! However, with so many advancements happening at once, it can be overwhelming to keep up with all the latest developments. To help us navigate through this complex terrain, we’ve invited Raj - one of the most adept at explaining State-of-the-Art (SOTA) AI in practical terms - to join us on the podcast.

Raj discusses several intriguing topics such as in-context learning, reasoning, LLM options, and related tooling. But that’s not all! We also hear from Raj about the rapidly growing data science and AI community on TikTok.

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Featuring:

  • Rajiv Shah – Website, GitHub, LinkedIn, X
  • Chris Benson – Website, GitHub, LinkedIn, X
  • Daniel Whitenack – Website, GitHub, X

Show Notes:

  • Solving AI Tasks with ChatGPT and its Friends in HuggingFace | GitHub
  • Generative Agents: Interactive Simulacra of Human Behavior
  • Wolfram ChatGPT
  • Comparing LLMs
  • LangChain
  • Learn about LLMs: 
    • Emergence and reasoning in large language models (Jason Wei)
    • Sparks of Artificial General Intelligence
  • Learning Prompting
  • Getting Started with Transformers: 
    • Transformers course (free)
    • Tasks at Hugging Face
  • Training your own LLM Models: 
    • Efficient Large Language Model training with LoRA and Hugging Face
    • PEFT (Parameter-Efficient Fine-Tuning)
  • Dolly blog post
  • Illustrating Reinforcement Learning from Human Feedback

Upcoming Events: 

  • Register for upcoming webinars here!

Original source

This story was published by Practical AI and written by Daniel Whitenack and Chris Benson. SyncAI.news shows a preview; the complete article is on the publisher's site.

Read the full story on share.transistor.fm

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