In this video, I show you how to fine-tune Google Gemma for your converting natural language question to SQL queries. Google Gemma is a family of open-source, large language models (LLMs) that are designed to be accessible and lightweight.This allows your Google Gemma model to perform much better for your business or personal use case. Give Google Gemma detailed information that it doesn't already have, make it respond in a specific tone/personality, and much more.
▶ Link to the code : [ Ссылка ]
▶ Features of Google Gemma :
Open-source: Anyone can access and use the code for free, which encourages research and development in the field of AI.
Lightweight: Compared to other LLMs like me, Gemma models are smaller and require fewer resources to run, making them suitable for laptops and cloud environments with limited computing power.
State-of-the-art: Despite their size, Gemma models can still perform a wide range of tasks, including text generation, translation, question answering, and code completion.
Safe and responsible: Google has taken steps to ensure that Gemma models are safe and responsible to use, including filtering out sensitive data from the training set and incorporating safeguards against misuse.
▶ Two versions of Google Gemma:
2 billion parameters: This version is ideal for users with limited resources and is still capable of performing many tasks.
7 billion parameters: This version offers better performance but requires more resources to run.
▶ Applications:
Chatbots: Gemma can be used to create chatbots that can engage in conversations with users in a natural way.
Content generation: Gemma can be used to generate different creative text formats, like poems, code, scripts, musical pieces, email, letters, etc.
Research and development: Researchers can use Gemma to experiment with new ideas and applications for LLMs.
▶ Additional resources of Google Gemma:
GitHub repository: [ Ссылка ]
Google AI Blog: [ Ссылка ]
Kaggle: [ Ссылка ]
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▬▬▬▬▬▬ VIDEO CHAPTERS & TIMESTAMPS ▬▬▬▬▬▬
00:00 : Intro to Google Gemma
00:41 : Fine-Tuning Google Gemma for Natural Language to SQL
13:30 : Conclusion
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