Hello! I’m Mohamed Ashraf, and today I’ll show you how to leverage LLMs (Large Language Models) to supercharge your daily software development. Let’s dive in!

Starting

What is an LLM?

Definition: LLM stands for Large Language Model. These AI models are trained on vast amounts of text data to understand and generate human-like language. Using deep learning techniques and neural networks, LLMs perform a wide range of natural language processing (NLP) tasks, such as text completion, translation, summarization, and more.

History

  1. Early Beginnings:
    • In the 1950s, simple statistical methods for language processing were developed.
    • Early NLP efforts involved rule-based systems and basic probabilistic models like n-grams, predicting words based on a fixed number of preceding words.
  2. Neural Networks and Word Embeddings (1990s - 2010s):
    • The 1990s saw the rise of neural networks for language modeling.
    • Word embeddings like Word2Vec and GloVe emerged in the early 2010s, representing words as vectors, capturing semantic relationships.
  3. The Rise of Deep Learning (2014 - 2017):
    • Recurrent Neural Networks (RNNs), especially Long Short-Term Memory (LSTM) networks, improved sequential data handling.
    • The Transformer architecture, introduced by Vaswani et al. in 2017, revolutionized NLP by processing entire sentences simultaneously.
  4. Large Language Models and Transfer Learning (2018 - Present):
    • OpenAI’s GPT set a new standard for LLMs in 2018. GPT-2 and GPT-3 followed, each larger and more capable.
    • These models demonstrated the power of pre-training on vast datasets and fine-tuning on specific tasks, known as transfer learning.
    • Other notable LLMs include Google’s BERT, T5, and more recently, GPT-4.

Usage of LLMs

  1. Text Generation and Completion: For creative writing, drafting emails, and generating code.
  2. Translation and Summarization: Translating text between languages and summarizing documents.
  3. Chatbots and Virtual Assistants: Powering conversational agents for customer support and task assistance.
  4. Content Creation and Editing: Assisting in writing, creating marketing content, and editing text.
  5. Research and Information Retrieval: Generating literature reviews, extracting information, and answering complex questions.
  6. Education and Training: Providing personalized tutoring, generating educational materials, and aiding language learning.

Latest Developments

  1. GPT-4 and Beyond: Enhanced performance in language understanding and generation.
  2. Multimodal Models: Combining text with other data types, like images (e.g., CLIP by OpenAI).
  3. Ethical AI and Bias Mitigation: Reducing biases and making models more ethical and fair.
  4. Real-time and Low-latency Models: Deploying LLMs in real-time applications with reduced latency.
  5. Specialized LLMs: Fine-tuning models for specific industries, such as legal (e.g., CaseText) and medical (e.g., PubMedBERT).

Getting Started

Let’s make use of these amazing LLM features to automate some of our tasks.

Minimum Requirements

  • Apple/Nvidia GPU with 4GB+ VRAM. If the GPU is incompatible, the model will use the CPU (slower).
  • 500MB Storage for the program and more for the model.
  • OS: Linux, Mac, Windows (Beta).

Downloading

  1. Download the Ollama program based on your OS.
  2. Pick a model from the Ollama library.
    Ensure the model’s VRAM requirement matches your GPU’s VRAM to avoid performance issues.
post-gpucpussd-bottleneck

Tip: Focus on a model that meets your needs rather than one that tries to do everything.

Recommended Models:

  • starcoder2:3b (for 4GB VRAM)
  • codellama:7b (for 8GB VRAM)

To download the model, run:

ollama pull <model_name>

To start the model, run:

ollama run <model_name>

Usage

  1. Download the Continue extension for VSCode.
  2. Ensure your .continue/config.json includes the model you downloaded.
"models": [
  {
    "title": "Ollama",
    "provider": "ollama",
    "model": "<model_name>"
  }
]

After restarting VSCode, the extension will reload, parse your files, and start your LLM.

post-continue-indexing

You can now write directly to your model

ollama-gen

Or let the model help you with autocompletion.

ollama-complete

You can also highlight code and press Ctrl+Shift+I to interact with the model.

Thanks for reading! Enjoy integrating LLMs into your daily development workflow! 🚀

(c) 2024, MoWx-04-06