Week3 - I have just completed the course, excited to put my knowledge into practice!

Course Q&A Generative AI with Large Language Models
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  • Week 3 I have just completed the course, excited to put my knowledge into practice. please help me how can I start my LLM journey. I’m excited to take forward whatever I have learnt so far and put it into practice

  • I have completed all the quiz and labs.
    Thanks

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Hi Naveed, welcome to the community!

Congratulations on completing the course! Here is my suggestion. Before jumping into projects, briefly review the key topics from the course:

  • Attention Mechanisms such as transformers and self-attention.
  • Pre-training and fine-tuning approaches.
  • Prompt engineering for better performance and output from your model.

Pick a specific use case that excites you and aligns with your goals. Some project ideas could include:

  • Build a text summarizer using pre-trained models to summarize long articles or reports.
  • Develop a chatbot by fine-tuning an LLM for customer support, personal assistants, or Q&A systems.

Start by experimenting with pre-trained models from the Hugging Face library or OpenAI’s GPT:

  • Hugging Face Transformers provides many pre-trained LLMs that can be used quickly.
  • Use Google Colab to run and experiment with models without needing a powerful local machine.
  • LangChain is great for integrating LLMs with external tools or building complex systems like chatbots.

Fine-tune a model to your specific use case:

  • Collect a dataset related to your problem (e.g., customer reviews, domain-specific articles).
  • Fine-tune a smaller model on that dataset to specialize the model’s output for your needs. You can use Hugging Face to do this, as it makes fine-tuning models easy, even on smaller datasets.
  • Experiment with different tasks such as text generation, classification, or translation.

After familiarizing yourself with pre-trained models, consider creating your first practical application, like a chatbot. Mastering prompt engineering is crucial. Try out few-shot learning, chained prompts, and prompt customization and refinement. Lastly, collaborating with others or contributing to open-source language model projects can greatly accelerate your learning.

Hope this helps!

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Hello Nayid,
Thank you so much for your input. I really appreciate your swift response. I will work on it and get back to you for further assistance.

Thanks
Naveed Pasha

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