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Data synthesis for SOTA LLMs

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Nous Research has been pumping out some of the best open access LLMs using SOTA data synthesis techniques. Their Hermes family of models is incredibly popular! In this episode, Karan from Nous talks about the origins of Nous as a distributed collective of LLM researchers. We also get into fine-tuning strategies and why data synthesis works so well.

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  • Read Write Own – Read, Write, Own: Building the Next Era of the Internet—a new book from entrepreneur and investor Chris Dixon—explores one possible solution to the internet’s authenticity problem: Blockchains. From AI that tracks its source material to generative programs that compensate—rather than cannibalize—creators. It’s a call to action for a more open, transparent, and democratic internet. One that opens the black box of AI, tracks the origins we see online, and much more. Order your copy of Read, Write, Own today at readwriteown.com
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Featuring:

Show Notes:

Something missing or broken? PRs welcome!

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Chapter

1. Welcome to Practical AI (Dance Party!) (00:00:00)

2. Karan Malhotra (00:00:43)

Chapter image

3. Origins of Nous Research (00:01:57)

4. What is synthetic data (00:10:24)

5. Effects of model licensing (00:16:47)

6. Map of Nous (00:22:23)

7. How is Nous organized? (00:26:45)

9. Fine Tuning advice (00:31:48)

10. Stuff to look for (00:35:00)

11. What's next? (00:40:45)

12. Thank you! (00:45:03)

13. Outro (Dance Party!) (00:46:00)

Chapter image

287 episode

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iconBagikan
 
Manage episode 399613971 series 2385063
Konten disediakan oleh Changelog Media. Semua konten podcast termasuk episode, grafik, dan deskripsi podcast diunggah dan disediakan langsung oleh Changelog Media atau mitra platform podcast mereka. Jika Anda yakin seseorang menggunakan karya berhak cipta Anda tanpa izin, Anda dapat mengikuti proses yang diuraikan di sini https://id.player.fm/legal.

Nous Research has been pumping out some of the best open access LLMs using SOTA data synthesis techniques. Their Hermes family of models is incredibly popular! In this episode, Karan from Nous talks about the origins of Nous as a distributed collective of LLM researchers. We also get into fine-tuning strategies and why data synthesis works so well.

Leave us a comment

Changelog++ members save 2 minutes on this episode because they made the ads disappear. Join today!

Sponsors:

  • Read Write Own – Read, Write, Own: Building the Next Era of the Internet—a new book from entrepreneur and investor Chris Dixon—explores one possible solution to the internet’s authenticity problem: Blockchains. From AI that tracks its source material to generative programs that compensate—rather than cannibalize—creators. It’s a call to action for a more open, transparent, and democratic internet. One that opens the black box of AI, tracks the origins we see online, and much more. Order your copy of Read, Write, Own today at readwriteown.com
  • Fly.ioThe home of Changelog.com — Deploy your apps and databases close to your users. In minutes you can run your Ruby, Go, Node, Deno, Python, or Elixir app (and databases!) all over the world. No ops required. Learn more at fly.io/changelog and check out the speedrun in their docs.

Featuring:

Show Notes:

Something missing or broken? PRs welcome!

  continue reading

Chapter

1. Welcome to Practical AI (Dance Party!) (00:00:00)

2. Karan Malhotra (00:00:43)

Chapter image

3. Origins of Nous Research (00:01:57)

4. What is synthetic data (00:10:24)

5. Effects of model licensing (00:16:47)

6. Map of Nous (00:22:23)

7. How is Nous organized? (00:26:45)

9. Fine Tuning advice (00:31:48)

10. Stuff to look for (00:35:00)

11. What's next? (00:40:45)

12. Thank you! (00:45:03)

13. Outro (Dance Party!) (00:46:00)

Chapter image

287 episode

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