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Accessing and Understanding LLMs: From Cloud to Local

Introduction:​

We've explored the capabilities and challenges of LLMs. Now, let's dive into the practical aspects of accessing and using these models, both in the cloud and on your local devices.

1. LLM Inference Providers: Cloud-Based Access​

  • Together AI:​

    • A platform that hosts a variety of open-source LLMs.
    • Provides a user-friendly "playground" for interacting with these models.
    • A great resource for exploring different LLMs and their capabilities.

  • Hyperbolic:​

    • A platform that focuses on providing access to base LLMs, such as Llama 3.
    • Offers a valuable resource for developers who want to work with base models for fine-tuning or customization.

  • Why Inference Providers?​

    • These platforms provide convenient access to powerful LLMs without requiring users to host them themselves.
    • They handle the complexities of model deployment and scaling.

2. Running LLMs Locally: LM Studio​

  • LM Studio:​

    • An application that allows users to run LLMs on their local computers.
    • Supports a variety of LLMs, including distilled and lower-precision versions of larger models.
    • Enables offline access and greater control over LLM usage.

  • Local Processing:​

    • Running LLMs locally allows for faster response times and increased privacy.
    • However, it requires sufficient hardware resources, such as a powerful GPU.

  • Considerations:​

    • Smaller models or models that have been distilled are more likely to run well on local machines.
    • Lower precision model versions also allow for smaller footprint on your hardware.

3. Understanding the Technology: From Query to Response​

  • The Process:​

    • When you enter a query into a platform like ChatGPT, your text is first tokenized.
    • The tokens are then fed into the LLM, which generates a response based on its trained parameters.
    • The response is then decoded from tokens back into human-readable text.

  • Behind the Scenes:​

    • LLMs are complex neural networks that process vast amounts of data.
    • They use techniques like attention mechanisms to understand the relationships between words and generate coherent responses.
    • The model is fixed, and the only thing that changes is the context window.

  • Context Windows:​

    • The context window is the memory of the model. It uses the context window to understand the conversation.

4. Key Takeaways:​

  • LLMs can be accessed through cloud-based inference providers or run locally on your computer.
  • The choice of platform depends on your needs, resources, and technical expertise.
  • Understanding the underlying technology helps you use LLMs more effectively and responsibly.