รู้จักกับ Local LLM และการใช้งานผ่าน Ollama API

Introduction

This tutorial explores the world of Local Large Language Models (LLMs) and how to utilize them through the Ollama API. It aims to provide insights into LLMs, their applications, and practical steps to implement them on your own machine. By the end of this guide, you will have a solid understanding of how to work with Local LLMs and connect them via APIs for various programming tasks.

Step 1: Understanding LLMs and Their Applications

• Learn what LLMs are: These are advanced models capable of understanding and generating human-like text.
• Distinguish between different types of LLMs:
  • Instruct LLMs: Designed to follow instructions given in natural language.
  • LLMs + API: These models can be accessed via application programming interfaces to integrate their capabilities into software applications.

Step 2: Exploring Hugging Face and LM Studio

• Hugging Face: A platform where you can experiment with various LLMs and prompts.
• LM Studio:
  • An interface provided by Hugging Face for testing prompts with LLMs.
  • Use LM Studio to familiarize yourself with how LLMs respond to different inputs.

Step 3: Getting Started with Ollama

• Introduction to Ollama:

  • A tool for running Local LLMs on your machine.
  • Offers a simple command-line interface for accessing LLM capabilities.

• Basic Ollama commands:

  • Install Ollama using the package manager of your choice.
  • Common command to run a model:

    ollama run <model_name>
    

Step 4: Connecting Local LLMs via API

• Setting up the Local LLM to be accessible via an API:

  • Load a model on your local machine.
  • Create an API that interacts with the LLM using a framework like FastAPI.

• Example FastAPI setup:

  from fastapi import FastAPI
  app = FastAPI()
  
  @app.get("/predict")
  def predict(input_text: str):
      # Call your LLM processing function here
      return {"response": "Your LLM output"}
  

Step 5: Utilizing LM Studio and Deepseek

• Learn how to use LM Studio for testing:
  • Input prompts and analyze responses from the LLM.
• Explore Deepseek:
  • A tool that enhances the interaction with LLMs and helps refine the prompts.

Step 6: Implementing Local LLMs in Programming

• Integrate Local LLMs with programming tools:
  • Use Aider or Continue for coding assistance.
  • Aider can help you generate code snippets based on prompts.
  • Continue can assist in expanding on existing code.

Step 7: Experimenting with Ollama API

• Set up the Ollama API:
  • Connect the Ollama API with your local programming environment.
  • Test the integration by sending requests to the API and receiving responses from the LLM.

Conclusion

In this tutorial, we covered the essentials of Local LLMs, how to set them up using Ollama, and various practical applications for programming. By understanding these concepts, you can harness the power of LLMs for personal projects or professional tasks. To deepen your knowledge, consider exploring further resources and engaging with communities focused on AI and programming.