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Code Switching

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Innehåll tillhandahållet av Turpentine. Allt poddinnehåll inklusive avsnitt, grafik och podcastbeskrivningar laddas upp och tillhandahålls direkt av Turpentine eller deras podcastplattformspartner. Om du tror att någon använder ditt upphovsrättsskyddade verk utan din tillåtelse kan du följa processen som beskrivs här https://sv.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:

🎙️ Unpack Pricing - Dive into the dark arts of SaaS pricing with Metronome CEO Scott Woody and tech leaders. Learn how strategic pricing drives explosive revenue growth in today's biggest companies like Snowflake, Cockroach Labs, Dropbox and more.

Apple: https://podcasts.apple.com/us/podcast/id1765716600

Spotify: https://open.spotify.com/show/38DK3W1Fq1xxQalhDSueFg

--

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 episoder

Artwork
iconDela
 
Manage episode 423123933 series 3572102
Innehåll tillhandahållet av Turpentine. Allt poddinnehåll inklusive avsnitt, grafik och podcastbeskrivningar laddas upp och tillhandahålls direkt av Turpentine eller deras podcastplattformspartner. Om du tror att någon använder ditt upphovsrättsskyddade verk utan din tillåtelse kan du följa processen som beskrivs här https://sv.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:

🎙️ Unpack Pricing - Dive into the dark arts of SaaS pricing with Metronome CEO Scott Woody and tech leaders. Learn how strategic pricing drives explosive revenue growth in today's biggest companies like Snowflake, Cockroach Labs, Dropbox and more.

Apple: https://podcasts.apple.com/us/podcast/id1765716600

Spotify: https://open.spotify.com/show/38DK3W1Fq1xxQalhDSueFg

--

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 episoder

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