My Journey with LLMs

I’ve always been fascinated by the power of language models. My first real “wow” moment was when I used a local LLM to summarize a huge technical document for a project—saving me hours of manual reading. Since then, I’ve experimented with both local and cloud-based models for everything from code completion to brainstorming blog ideas.

A Comprehensive Guide to Using LLM Models: Local and Cloud-Based Solutions

Large Language Models (LLMs) have become a cornerstone of modern AI research and application. From generating human-like text to aiding in code completion, these models have immense potential. In this post, I will explore various local models such as LLaMA and CodeLLaMA, along with cloud-based models available through Hugging Face. I’ll also delve into uncensored models and practical examples to show how to effectively utilize these models in real-world scenarios.

Setting Up Local LLM Models

Ollama

Ollama is an open-source, ready-to-use tool enabling seamless integration with a language model locally or from your own server.

I’ve installed it through ollama. You can install various open source LLMs through this link https://ollama.com/library. Installation is straightforward. You have to first install ollama and then run these commands depending on your model of choice.

basic commands:

ollama list # to list out all the installed models
ollama pull llama # to download and install the model
ollama info llama # to get model information
ollama delete llama # for deleting a model

LLaMA (Large Language Model Meta AI) is a family of models designed to provide powerful text generation with a reduced computational footprint.

Moreover, the models are perfect for further training on your own datasets. You can create custom models for your specific needs.

Some of the models are insanely large—like Llama3.1 with 405 billion parameters. It takes up 229GB storage with at least 256 GB RAM!

Using Cloud-Based Models with Hugging Face

Loading Models via the transformers Library is a breeze, and I often use it for quick experiments or when I need more compute power than my laptop can provide.

Comparing Local vs. Cloud-Based Models

Performance Metrics: When choosing between local and cloud-based models, consider:

• Latency: Local models usually have lower latency.
• Scalability: Cloud models can handle larger datasets and simultaneous requests.
• Privacy: Local models allow for more control over data privacy.

Personal Tip: If you’re just starting out, try running a small model locally to get a feel for how things work. It’s a great way to learn without racking up cloud costs!

Conclusion

This guide provided a detailed overview of working with local and cloud-based LLMs, including practical examples and setup instructions. Whether you’re looking to build sophisticated chatbots or perform simple text generation, LLMs offer versatile solutions for a variety of tasks.