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Deepseek-R1

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About Event

The whole world is talking about DeepSeek. It reached #1 on various Apple and Google app stores around the world.

Join AI Makerspace to hear about the concepts and code of R1; the first-ever Large Reasoning Model (LRM) released from the DeepSeek team.

We have some high-level questions to answer, including:

  • What is DeepSeek’s history of models and suite of models today including DeepSeek-V3 (deepseek-chat) and DeepSeek-R1 (deepseek-reasoner)?

  • Should we be surprised about this release of a Chain of Thought-powered Reasoner?

  • How does DeepSeek-R1 compare to leading CoT-style models in the industry, including OpenAI’s o1, o3, Qwen QwQ, or Google’s Gemini Flash Thinking?

  • Can we expect Anthropic, Meta, Mistral, and others to follow suit and release their own reasoning models, now that the game appears to have changed?

Perhaps more importantly, we want to dig into the paper and the technical details.

  • Based on what we know about o1, how is DeepSeek R1 different?

  • What was the role of DeepSeek-R1-Zero along the path to the development of DeepSeek-R1, which was “trained via large-scale Reinforcement Learning (RL) without Supervised Fine Tuning (SFT),” but encountered “challenges such as poor readability and language mixing?” What can we glean from taking a close look at the training template form the paper?

  • What does it mean that DeepSeek-R1 incorporated multi-stage training and cold-start data before RL?

  • What role did SFT play during training, and why did DeepSeek-R1 need two different rounds of RL?

  • How was DeepSeek-R1 used to distill reasoning capabilities down to a set of 800k samples that could be used for fine-tuning Small Language Models in sizes ranging from 1.5B to 70B?

Of course, expect lots of vibe checking of the reasoning models we can use out of the box.

It should be a dope event, so join us live to get your questions answered about what’s really going on with DeepSeek-R1!

📚 You’ll learn:

  • How DeepSeek R1 was trained, and what it’s capabilities are today compared to other leading reasoning LRMs

  • How DeepSeek-R1-Zero set the stage for the next generation of model, and where it fell short

  • How distillation was used to provide the world with a suite of small, performant, reasoning models

  • What all this means for the future of LRMs and building, shipping, and sharing production LLM and LRM applications!

🤓 Who should attend the event:

  • Aspiring AI Engineers & Leaders who want to understand and build with leading LRMs

Speakers:

  • Dr. Greg” Loughnane is the Co-Founder & CEO of AI Makerspace, where he is an instructor for their AI Engineering Bootcamp. Since 2021, he has built and led industry-leading Machine Learning education programs.  Previously, he worked as an AI product manager, a university professor teaching AI, an AI consultant and startup advisor, and an ML researcher.  He loves trail running and is based in Dayton, Ohio.

  • Chris “The Wiz” Alexiuk is the Co-Founder & CTO at AI Makerspace, where he is an instructor for their AI Engineering Bootcamp. During the day, he is also a Developer Advocate at NVIDIA. Previously, he was a Founding Machine Learning Engineer, Data Scientist, and ML curriculum developer and instructor. He’s a YouTube content creator YouTube who’s motto is “Build, build, build!” He loves Dungeons & Dragons and is based in Toronto, Canada.

Follow AI Makerspace on LinkedIn and YouTube to stay updated about workshops, new courses, and corporate training opportunities.

Avatar for Public AIM Events!
Presented by
Public AIM Events!
Hosted By
109 Went