We made it— 300 episodes of This Is Woman’s Work ! And we’re marking this milestone by giving you something that could seriously change the game in your business or career: the skill of pitching yourself effectively. Whether you’re dreaming of being a podcast guest, landing a speaking gig, signing a client, or just asking for what you want with confidence—you’re already pitching yourself, every day. But are you doing it well? In this milestone episode, Nicole breaks down exactly how to pitch yourself to be a podcast guest … and actually hear “yes.” With hundreds of pitches landing in her inbox each month, she shares what makes a guest stand out (or get deleted), the biggest mistakes people make, and why podcast guesting is still one of the most powerful ways to grow your reach, authority, and influence. In This Episode, We Cover: ✅ Why we all need to pitch ourselves—and how to do it without feeling gross ✅ The step-by-step process for landing guest spots on podcasts (and more) ✅ A breakdown of the 3 podcast levels: Practice, Peer, and A-List—and how to approach each ✅ The must-haves of a successful podcast pitch (including real examples) ✅ How to craft a pitch that gets read, gets remembered, and gets results Whether you’re new to pitching or want to level up your game, this episode gives you the exact strategy Nicole and her team use to land guest spots on dozens of podcasts every year. Because your voice deserves to be heard. And the world needs what only you can bring. 🎁 Get the FREE Podcast Pitch Checklist + Additional Information on your Practice Group, Peer Group, and A-List Group Strategies: https://nicolekalil.com/podcast 📥 Download The Podcast Pitch Checklist Here Related Podcast Episodes: Shameless and Strategic: How to Brag About Yourself with Tiffany Houser | 298 How To Write & Publish A Book with Michelle Savage | 279 How To Land Your TED Talk and Skyrocket Your Personal Brand with Ashley Stahl | 250 Share the Love: If you found this episode insightful, please share it with a friend, tag us on social media, and leave a review on your favorite podcast platform! 🔗 Subscribe & Review: Apple Podcasts | Spotify | Amazon Music…
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Fine-Tuning AI Models: Unlocking the Potential of Llama 2, Code Llama, and OpenHermes
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Manage episode 440702883 series 3601678
Contenido proporcionado por wail. Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente wail o su socio de plataforma de podcast. Si cree que alguien está utilizando su trabajo protegido por derechos de autor sin su permiso, puede seguir el proceso descrito aquí https://es.player.fm/legal.
In this episode, we dive deep into the world of fine-tuning AI language models, breaking down the processes and techniques behind optimizing models like Llama 2, Code Llama, and OpenHermes. We'll explore the critical role of high-quality instruction datasets and walk you through a step-by-step guide on fine-tuning Llama 2 using Google Colab. Learn about the key libraries, parameters, and how to go beyond notebooks with more advanced scripts.
We also take a closer look at the fine-tuning of Code Llama with the Axolotl tool, covering everything from setting up a cloud-based GPU service to merging the trained model and uploading it to Hugging Face. Whether you're just starting with AI models or looking to level up your game, this episode has you covered.
Finally, we'll explore Direct Preference Optimization (DPO), a cutting-edge technique that significantly improved the performance of OpenHermes-2.5. DPO, a variation of Reinforcement Learning from Human Feedback (RLHF), shows how preference data can help models generate more accurate and relevant answers. Tune in for practical insights, code snippets, and tips to help you explore and optimize AI models.
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This feed was archived on January 21, 2025 14:14 (
Why? Feed inactivo status. Nuestros servidores no pudieron recuperar un podcast válido durante un período sostenido.
What now? You might be able to find a more up-to-date version using the search function. This series will no longer be checked for updates. If you believe this to be in error, please check if the publisher's feed link below is valid and contact support to request the feed be restored or if you have any other concerns about this.
Manage episode 440702883 series 3601678
Contenido proporcionado por wail. Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente wail o su socio de plataforma de podcast. Si cree que alguien está utilizando su trabajo protegido por derechos de autor sin su permiso, puede seguir el proceso descrito aquí https://es.player.fm/legal.
In this episode, we dive deep into the world of fine-tuning AI language models, breaking down the processes and techniques behind optimizing models like Llama 2, Code Llama, and OpenHermes. We'll explore the critical role of high-quality instruction datasets and walk you through a step-by-step guide on fine-tuning Llama 2 using Google Colab. Learn about the key libraries, parameters, and how to go beyond notebooks with more advanced scripts.
We also take a closer look at the fine-tuning of Code Llama with the Axolotl tool, covering everything from setting up a cloud-based GPU service to merging the trained model and uploading it to Hugging Face. Whether you're just starting with AI models or looking to level up your game, this episode has you covered.
Finally, we'll explore Direct Preference Optimization (DPO), a cutting-edge technique that significantly improved the performance of OpenHermes-2.5. DPO, a variation of Reinforcement Learning from Human Feedback (RLHF), shows how preference data can help models generate more accurate and relevant answers. Tune in for practical insights, code snippets, and tips to help you explore and optimize AI models.
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Intro to Generative AI
In this episode, we dive deep into the world of fine-tuning AI language models, breaking down the processes and techniques behind optimizing models like Llama 2, Code Llama, and OpenHermes. We'll explore the critical role of high-quality instruction datasets and walk you through a step-by-step guide on fine-tuning Llama 2 using Google Colab. Learn about the key libraries, parameters, and how to go beyond notebooks with more advanced scripts. We also take a closer look at the fine-tuning of Code Llama with the Axolotl tool, covering everything from setting up a cloud-based GPU service to merging the trained model and uploading it to Hugging Face. Whether you're just starting with AI models or looking to level up your game, this episode has you covered. Finally, we'll explore Direct Preference Optimization (DPO), a cutting-edge technique that significantly improved the performance of OpenHermes-2.5. DPO, a variation of Reinforcement Learning from Human Feedback (RLHF), shows how preference data can help models generate more accurate and relevant answers. Tune in for practical insights, code snippets, and tips to help you explore and optimize AI models.…
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