Supercharge Your Coding with Local LLMs: A Step-by-Step Guide featuring Phi-3 Mini
Author: Rajesh Pandhare
As AI continues to revolutionize the tech industry, Large Language Models (LLMs) are emerging as powerful tools for enhancing coding efficiency and creativity. By running LLMs locally on your own hardware, you can enjoy enhanced privacy, offline availability, and reduced latency compared to cloud-based solutions. In this article, we’ll explore how Kanaka Software is empowering developers with cutting-edge AI solutions, focusing on the Phi-3 Mini model and providing a practical, step-by-step guide to get you started.
Understanding Local LLMs and Their Advantages
LLMs are advanced AI models trained on vast amounts of text data, enabling them to understand and generate human-like text. When applied to coding tasks, LLMs can assist with code generation, autocompletion, and even problem-solving. By running LLMs locally, you can:
Tools like Ollama simplify the management of these models, while extensions like CodeGPT seamlessly integrate them into your development environment.
?
Hardware Considerations and LLM Selection
Before diving into the setup process, it’s essential to understand the hardware requirements for running LLMs locally. While high-end GPUs can handle larger models, most developers can still leverage the power of LLMs on average consumer hardware, such as an M1 Mac or Windows i5 machine with 16GB of RAM.
When selecting an LLM for local deployment, consider factors like model size, performance, and capabilities. Here are five noteworthy models to explore:
For this article, we’ll focus on the Phi-3 Mini model, which strikes a balance between power and efficiency on typical hardware.
?
Software Setup: Step-by-Step Guide
1. Installing Ollama
Ollama is a tool that simplifies the management of local LLMs. Follow these steps to install it on your system:
Windows:
MacOS:
System Requirements:
Additional Notes:
?
2. Downloading and Running the Phi-3 Mini Model
With Ollama installed, you can easily download and run the Phi-3 Mini model:
:> ollama run phi3:3.8b-mini-instruct-4k-fp16
?
领英推荐
3. Installing CodeGPT
CodeGPT is a VSCode extension that enables seamless integration with local LLMs. To install it:
?
4. Configuring CodeGPT for Phi-3 Mini
To connect CodeGPT with the Phi-3 Mini model:
?
Practical Demo: Building a Golang REST API
Let’s put Phi-3 Mini to the test by building a simple REST API in Go that handles CRUD operations for a ‘book’ resource (title, author, ISBN). Here’s the prompt we’ll use:
“Build a simple REST API in Go that handles CRUD operations for a ‘book’ resource (title, author, ISBN).”
Using CodeGPT with Phi-3 Mini
?
Refining and Extending the Generated Code
Review the generated code and modify it as needed. Always remember, this is AI-generated code, so use it as a copilot; the main pilot is you.
Use CodeGPT to interact with Phi-3 Mini for generating additional code snippets or suggestions.
Refine the API by adding error handling, validation, and other features.
Here’s an example of how the generated code might look:
package main
import (
"encoding/json"
"fmt"
"log"
"net/http"
"strconv"
"time"
"github.com/gorilla/mux"
)
type Book struct {
Title string `json:"title"`
Author string `json:"author"`
ISBN string `json:"isbn"`
CreatedAt time.Time `json:"created_at,omitempty"`
}
var books []Book
func getAllBooks(w http.ResponseWriter, r *http.Request) {
w.Header().Set("Content-Type", "application/json")
json.NewEncoder(w).Encode(books)
}
func createBook(w http.ResponseWriter, r *http.Request) {
var newBook Book
_ = json.NewDecoder(r.Body).Decode(&newBook)
newBook.CreatedAt = time.Now()
books = append(books, newBook)
w.Header().Set("Content-Type", "application/json")
json.NewEncoder(w).Encode(newBook)
}
?
func getBook(w http.ResponseWriter, r *http.Request) {
params := mux.Vars(r)
id, _ := strconv.Atoi(params["id"])
for i, book := range books {
if id == i {
json.NewEncoder(w).Encode(book)
return
}
}
http.Error(w, "Book not found", http.StatusNotFound)
}
func updateBook(w http.ResponseWriter, r *http.Request) {
params := mux.Vars(r)
id, _ := strconv.Atoi(params["id"])
var updatedBook Book
_ = json.NewDecoder(r.Body).Decode(&updatedBook)
for i, _ := range books {
if id == i {
books[i] = updatedBook
w.Header().Set("Content-Type", "application/json")
json.NewEncoder(w).Encode(updatedBook)
return
}
}
http.Error(w, "Book not found", http.StatusNotFound)
}
func deleteBook(w http.ResponseWriter, r *http.Request) {
params := mux.Vars(r)
id, _ := strconv.Atoi(params["id"])
for i, _ := range books {
if id == i {
books = append(books[:i], books[i+1:]...)
w.WriteHeader(http.StatusNoContent)
return
}
}
http.Error(w, "Book not found", http.StatusNotFound)
}
func main() {
fmt.Println("Welcome to Book Library")
router := mux.NewRouter()
router.HandleFunc("/books", getAllBooks).Methods("GET")
router.HandleFunc("/books", createBook).Methods("POST")
router.HandleFunc("/books/{id}", getBook).Methods("GET")
router.HandleFunc("/books/{id}", updateBook).Methods("PUT")
router.HandleFunc("/books/{id}", deleteBook).Methods("DELETE")
log.Fatal(http.ListenAndServe(":8080", router))
}
To test code :
curl -X GET https://localhost:8080/books
curl -X POST -H "Content-Type: application/json" -d '{"title":"New Book", "author":"Author Name", "isbn":"1234567890"}' https://localhost:8080/books
curl -X GET https://localhost:8080/books/{id}
curl -X DELETE https://localhost:8080/books/{id}?
Final Thoughts
By leveraging local LLMs like Phi-3 Mini, you can supercharge your coding efficiency and creativity. Kanaka Software is committed to empowering developers with cutting-edge AI solutions, and we encourage you to explore the world of local LLMs and experiment with different models and applications.
If you’re interested in learning more about Kanaka Software or would like to stay updated on our latest developments, please visit our?website ?and follow us on?LinkedIn .
Remember, the future of coding is here, and it’s powered by AI!
LLM Business Transformation Consultant | Data Analytics | Master Six Sigma Black Belt
6 个月There are SLM ( Small Langauage model ) Phi-3 is also SLM , which is use by the organization to train with domain knowledge , or integrate with Knowledge Bank. USE CASES ( Ecommerce Site integrated with SLM ) (Digital Marketer - DM ) Marketing Use case. Use Case: Enhancing the Online Shopping Experience. Scenario: A customer visits an e-commerce website to purchase a new laptop. How the DM can help: ? The customer can use the natural language search bar to ask questions like "What's the difference between an i5 and i7 processor for video editing?" ? The DM provides clear explanations tailored to the customer's query, helping them understand the differences and make an informed choice. ? As the customer browses laptops, the DM recommends personalized options based on their search history and behavior. ? On product pages, the customer can interact with the DM through a chat window, asking questions about specific features, battery life, or compatibility with software they use. ? Before checkout, the DM suggests complementary accessories or extended warranties based on the customer's selected laptop.