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Joined 2 years ago
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Cake day: June 18th, 2023

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  • Checkout Notesnook. I’ve tried most of the ones you’ve listed and have been really enjoying how well it works compared to the competition considering its end-to-end encrypted.

    A few features:

    • Clients and server are open source.
    • End-to-end encrypted note syncing.
    • You can publish public notes.
    • You can publish privates notes that require a password to view.
    • You can self-host the sync server.
    • You can self-host the publishing server.
    • Full offline mode.
    • At rest encryption.
    • Multi-platform clients with feature parity (Android, iOS, Linux, Windows, MacOS, Web).
    • Most if not all of the general features you’d expect from a notes taking application.

    One thing I really like about the project is how open they are about what they’re doing, why they’re doing it and what the future holds. It’s been great seeing their roadmap (https://notesnook.com/roadmap/) and seeing promised features land with new ones being added, and I’ve only been using it for less than a year now!


  • I was also not sure what this meant, so I asked Google’s Gemini, and I think this clears it up for me:


    This means that the creators of Llama 3.3 have chosen to release only the version of the model that has been fine-tuned for following instructions. They are not making the original, “pretrained” version available.

    Here’s a breakdown of why this is significant:

    • Pretrained models: These are large language models (LLMs) trained on a massive dataset of text and code. They have learned to predict the next word in a sequence, and in doing so, have developed a broad understanding of language and a wide range of general knowledge. However, they may not be very good at following instructions or performing specific tasks.
    • Instruction-tuned models: These models are further trained on a dataset of instructions and desired outputs. This fine-tuning process teaches them to follow instructions more effectively, generate more relevant and helpful responses, and perform specific tasks with greater accuracy.

    In the case of Llama 3.3 70B, you only have access to the model that has already been optimized for following instructions and engaging in dialogue. You cannot access the initial pretrained model that was used as the foundation for this instruction-tuned version.

    Possible reasons why Meta (the creators of Llama) might have made this decision:

    • Focus on specific use cases: By releasing only the instruction-tuned model, Meta might be encouraging developers to use Llama 3.3 for assistant-like chat applications and other tasks where following instructions is crucial.
    • Competitive advantage: The pretrained model might be considered more valuable intellectual property, and Meta may want to keep it private to maintain a competitive advantage.
    • Safety and responsibility: Releasing the pretrained model could potentially lead to its misuse for generating harmful or misleading content. By releasing only the instruction-tuned version, Meta might be trying to mitigate these risks.

    Ultimately, the decision to release only the instruction-tuned model reflects Meta’s strategic goals for Llama 3.3 and their approach to responsible AI development.