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How do we know if Llama 3 is actually any better with Maxime Labonne

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Manage episode 414191815 series 3519364
Contenuto fornito da Justin Macorin and Bradley Arsenault. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Justin Macorin and Bradley Arsenault o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.

If you are anything like us, for 36 hours your Twitter and LinkedIn feeds were set ablaze by claims that Llama 3 is the greatest LLM since sliced bread. But how do we know that is actually the case? How do we separate the hype and noise from the reality? On what basis can we credibly claim that one LLM is better than another?
On this episode of The Prompt Desk, Justin Macorin and I brought on LLM expert Maxime Labonne, who has developed the open-source package LLM-autoeval. In the episode, he shares with us the techniques researchers are using to compare and contrast different LLM models.
Check out the LLM-autoeval Github here: https://github.com/mlabonne/llm-autoeval
Find Maxime Labonne on LinkedIn here: https://www.linkedin.com/in/maxime-labonne/
Follow Maxime on Twitter here: https://twitter.com/maximelabonne?lang=en

Continue listening to The Prompt Desk Podcast for everything LLM & GPT, Prompt Engineering, Generative AI, and LLM Security.
Check out PromptDesk.ai for an open-source prompt management tool.
Check out Brad’s AI Consultancy at bradleyarsenault.me
Add Justin Macorin and Bradley Arsenault on LinkedIn.
Please fill out our listener survey here to help us create a better podcast: https://docs.google.com/forms/d/e/1FAIpQLSfNjWlWyg8zROYmGX745a56AtagX_7cS16jyhjV2u_ebgc-tw/viewform?usp=sf_link


Hosted by Ausha. See ausha.co/privacy-policy for more information.

  continue reading

52 episodi

Artwork
iconCondividi
 
Manage episode 414191815 series 3519364
Contenuto fornito da Justin Macorin and Bradley Arsenault. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Justin Macorin and Bradley Arsenault o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.

If you are anything like us, for 36 hours your Twitter and LinkedIn feeds were set ablaze by claims that Llama 3 is the greatest LLM since sliced bread. But how do we know that is actually the case? How do we separate the hype and noise from the reality? On what basis can we credibly claim that one LLM is better than another?
On this episode of The Prompt Desk, Justin Macorin and I brought on LLM expert Maxime Labonne, who has developed the open-source package LLM-autoeval. In the episode, he shares with us the techniques researchers are using to compare and contrast different LLM models.
Check out the LLM-autoeval Github here: https://github.com/mlabonne/llm-autoeval
Find Maxime Labonne on LinkedIn here: https://www.linkedin.com/in/maxime-labonne/
Follow Maxime on Twitter here: https://twitter.com/maximelabonne?lang=en

Continue listening to The Prompt Desk Podcast for everything LLM & GPT, Prompt Engineering, Generative AI, and LLM Security.
Check out PromptDesk.ai for an open-source prompt management tool.
Check out Brad’s AI Consultancy at bradleyarsenault.me
Add Justin Macorin and Bradley Arsenault on LinkedIn.
Please fill out our listener survey here to help us create a better podcast: https://docs.google.com/forms/d/e/1FAIpQLSfNjWlWyg8zROYmGX745a56AtagX_7cS16jyhjV2u_ebgc-tw/viewform?usp=sf_link


Hosted by Ausha. See ausha.co/privacy-policy for more information.

  continue reading

52 episodi

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