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LLM for Programmers

  1. LLM as Code Assistant

  2. Create LLM based product

    • Using 3rd party LLM (ChatGPT, Gemini, Claude, etc.)

    • Using local LLM

    • Examples:

      • Chat application (multimodal: text, audio and image generation)
      • Youtube video summarizer
      • Retrieval-Augmented Generation

1. LLM as Code Assistant

  • Using 3rd party app (ChatGPT/Gemini etc.)

  • Using code editor/IDE plugins (Copilot/Cody/Codium etc.)

Bug fixing

def add_item(item, shopping_list=[]):
    shopping_list.append(item)
    return shopping_list


list1 = add_item("apples")  # ["apples"]
list2 = add_item("bananas")  # ["bananas"]

print("List 1:", list1)
print("List 2:", list2)

Code generation

Prompt examples:

Write a python code that tracks the CPU usage of a particular process (by PID) and graph it using matplotlib library. Track it for 30 seconds. And save the chart in chart.png file.

Library/framework exploration

Prompt examples:

  • Describe to me the top 5 most frequently used <library> functions and give me some examples.
  • What are the core fundamental concepts of <framework>, explain to me as someone who never used it before.

Using Code Editor/IDE plugins

2. Create LLM Based Product

  • Using 3rd party / closed weight LLM
  • Using local LLM
  • Usage examples:
    • Chat application (multimodal: text, audio and image generation)
    • Youtube video summarizer
    • Retrieval-Augmented Generation

2a. Using 3rd party / closed weight LLM

Closed weight LLM examples:

Using OpenAI API

See the notebook

Using OpenAI API

  • model selection
  • messages parameter (system, user, assistant)
  • temperature
  • max_tokens
  • stream

2b. Using Local LLM

2b. Using Local LLM

  • LLM is just files.
  • LLM is just a git folder.

See the available open-weight LLMs here

Anatomy of LLM

  • Base Model (foundation model), e.g: Llama-3, Phi-3, OpenELM, Mistral
  • Parameter size, e.g: 7B, 8B, 70B
  • Context size, e.g: 262k, 1048k
  • Fine-tuned data: e.g: instruct, chat, chinese-chat

See leaderboard here

Deploying LLM using Ollama

  1. Install ollama
  2. Run server: ollama serve
  3. Pull model: ollama pull llama3

See the notebook

2c. LLM Usage Example - Chat Application with Audio and Image Generation

Run the app:

cd app
go run ./cmd/*

Open this address

2c. LLM Usage Example - Youtube Summarizer

We are using:

  • OpenAI API / Ollama (Summary generation)
  • whisper.cpp (Audio transcription, audio -> text)
  • yt-dlp (Youtube video downloader)

See the notebook

then open this address

2c. RAG

See the notebook