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Local Llama 3.2 (3B) Tutorial - Summarization, Structured Text Extraction, and Data Labelling

Venelin Valkov via YouTube

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Overview

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Explore the capabilities of Meta AI's Llama 3.2 (3B) model in this comprehensive tutorial video. Learn how to set up and run the model using Ollama, and dive into practical applications such as data labeling, text summarization, structured data extraction, and question-answering. Follow along with Jupyter Notebook demonstrations and discover how to leverage this local language model for various natural language processing tasks. Gain insights into the model's performance and potential use cases, from creating LinkedIn posts to extracting information from tables. Perfect for developers and AI enthusiasts looking to harness the power of edge-optimized language models.

Syllabus

- Welcome
- Text tutorial on MLExpert.io
- Llama 3.2 on Ollama
- Download and run Llama 3.2 3B
- Jupyter Notebook setup
- Coding
- Labelling data
- Text summarization
- LinkedIn post
- Structured data extraction
- Rag/Question-answering
- Table data extraction
- Conclusion

Taught by

Venelin Valkov

Reviews

3.0 rating, based on 1 Class Central review

Start your review of Local Llama 3.2 (3B) Tutorial - Summarization, Structured Text Extraction, and Data Labelling

  • Profile image for Esom Nwonu
    Esom Nwonu
    I think the course was vague. It did not have a focal point, and it felt like the modules were loosely connected, lacking a clear progression or overarching theme. While some topics were interesting on their own, the absence of a central narrative made it difficult to understand how each part contributed to the overall learning objective. I often found myself wondering what the key takeaway was supposed to be, and whether I was actually building toward a specific skill or outcome.
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