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Code Switching

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Manage episode 423123933 series 3572102
Content provided by Turpentine. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Turpentine or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

In this episode of Emergent Behavior, @8teapi talks with Justin Junyang Lin, Chief Evangelist Officer of Alibaba Qwen Project. Joined by guest host Eugene Cheah, CEO of Recursal.AI, they talk about how Alibaba's Qwen 2 tackles multilingual challenges, including code-switching and the unique complexities of Chinese data.

🔥 Apply to join over 400 founders and Execs in the Turpentine Network: https://hmplogxqz0y.typeform.com/to/JCkphVqj

Explore the impact of open-source LLMs like Alibaba's Qwen 2, and how it's driving innovation in AI development.

RECOMMENDED PODCAST:

Patrick McKenzie (@patio11) talks to experts who understand the complicated but not unknowable systems we rely on. You might be surprised at how quickly Patrick and his guests can put you in the top 1% of understanding for stock trading, tech hiring, and more.

Spotify: https://open.spotify.com/show/3Mos4VE3figVXleHDqfXOH

Apple: https://podcasts.apple.com/id1753399812https://podcasts.apple.com/id1753399812

–

FOLLOW ON X:

@8teAPi (Ate)

@JustinLin610 (Junyang)

@picocreator (Eugene)

@TurpentineMedia

--

LINKS:

Alibaba Qwen Project:

https://www.alibabacloud.com/en/solutions/generative-ai/qwen?_p_lc=1

--

TIMESTAMPS:

(00:00) Introduction

(04:36) Qwen's Development Journey

(08:00) Data Curation & Coding Capabilities

(11:00) The Role of Evaluation

(14:00) Evolution of Pre-training and Evaluation

(17:00) Open Source vs. Commercial Groups

(22:00) Data Contamination

(24:00) Model Sizing and Computational Constraints

(28:00) Multi-lingual Capabilities

(31:00) Tokenizers and Language-Specific Considerations

(34:00) Code Switching and Data Filtering

(38:00) Code Switching, Dialects, and Model Size

(42:00) User Feedback and Model Development

(46:00) Challenges with Chinese Datasets

(52:00) Language Variation and Team Development

(58:00) Hiring and Team Dynamics

(1:03:00) Diversity and Production Considerations

(1:07:00) Production Impact and Collaboration

(1:13:00) Wrap

  continue reading

16 episodes

Artwork
iconShare
 
Manage episode 423123933 series 3572102
Content provided by Turpentine. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Turpentine or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

In this episode of Emergent Behavior, @8teapi talks with Justin Junyang Lin, Chief Evangelist Officer of Alibaba Qwen Project. Joined by guest host Eugene Cheah, CEO of Recursal.AI, they talk about how Alibaba's Qwen 2 tackles multilingual challenges, including code-switching and the unique complexities of Chinese data.

🔥 Apply to join over 400 founders and Execs in the Turpentine Network: https://hmplogxqz0y.typeform.com/to/JCkphVqj

Explore the impact of open-source LLMs like Alibaba's Qwen 2, and how it's driving innovation in AI development.

RECOMMENDED PODCAST:

Patrick McKenzie (@patio11) talks to experts who understand the complicated but not unknowable systems we rely on. You might be surprised at how quickly Patrick and his guests can put you in the top 1% of understanding for stock trading, tech hiring, and more.

Spotify: https://open.spotify.com/show/3Mos4VE3figVXleHDqfXOH

Apple: https://podcasts.apple.com/id1753399812https://podcasts.apple.com/id1753399812

–

FOLLOW ON X:

@8teAPi (Ate)

@JustinLin610 (Junyang)

@picocreator (Eugene)

@TurpentineMedia

--

LINKS:

Alibaba Qwen Project:

https://www.alibabacloud.com/en/solutions/generative-ai/qwen?_p_lc=1

--

TIMESTAMPS:

(00:00) Introduction

(04:36) Qwen's Development Journey

(08:00) Data Curation & Coding Capabilities

(11:00) The Role of Evaluation

(14:00) Evolution of Pre-training and Evaluation

(17:00) Open Source vs. Commercial Groups

(22:00) Data Contamination

(24:00) Model Sizing and Computational Constraints

(28:00) Multi-lingual Capabilities

(31:00) Tokenizers and Language-Specific Considerations

(34:00) Code Switching and Data Filtering

(38:00) Code Switching, Dialects, and Model Size

(42:00) User Feedback and Model Development

(46:00) Challenges with Chinese Datasets

(52:00) Language Variation and Team Development

(58:00) Hiring and Team Dynamics

(1:03:00) Diversity and Production Considerations

(1:07:00) Production Impact and Collaboration

(1:13:00) Wrap

  continue reading

16 episodes

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