No GPU, No Problem: How I Got Meta-Llama 3 Running Locally with Just a Few Lines of Code

 

Purpose of the Script

This Python script initializes and runs a local Large Language Model (LLM) using the gpt4all library. It is designed to work entirely on CPU, making it ideal for laptops or machines without a dedicated GPU. The script loads a quantized GGUF model, checks for its presence locally, and starts a chat session to generate a response to a prompt.

Step-by-Step Breakdown

1. Import Libraries

 

 

from gpt4all import GPT4All
import os

• gpt4all: A Python library for running LLMs locally.
• os: Used for file path operations and setting environment variables.

2. Force CPU Mode

 

• This line sets an environment variable to disable GPU usage, forcing the model to run on CPU only.
• It's particularly useful for systems that do not have a compatible GPU or when you want to avoid GPU acceleration for simplicity or compatibility reasons.

Observed Behavior

Even with this setting, the system attempted to load GPU-related libraries and returned the following errors:

 

 

Failed to load llamamodel-mainline-cuda-avxonly.dll: LoadLibraryExW failed with error 0x7e
Failed to load llamamodel-mainline-cuda.dll: LoadLibraryExW failed with error 0x7e

• These errors indicate that the system tried to load CUDA (GPU) DLLs, but couldn't find them or failed due to missing dependencies.
• Error 0x7e typically means the DLL or one of its dependencies is missing.

Despite the Errors

• The script continued to work correctly and successfully generated a response to your query.
• This shows that the gpt4all library has a fallback mechanism that allows it to proceed with CPU execution even if GPU-related components fail to load.

3. Set Model Path

 

 

custom_model_dir = "./models"
model_filename = "Meta-Llama-3-8B-Instruct.Q4_0.gguf"
model_path = os.path.join(custom_model_dir, model_filename)

• Specifies the directory and filename of the model.
• Combines them into a full path for loading.

4. Check if Model Exists

 

 

if not os.path.isfile(model_path):
    print(f"Model file not found at: {model_path}")
    print("GPT4All will attempt to download it into the custom directory.")
    model = GPT4All(model_filename, model_path=custom_model_dir)
else:
    print(f"Model file found at: {model_path}")
    model = GPT4All(model_filename, model_path=custom_model_dir, allow_download=False)

• If the model file is missing, it prints a message and allows automatic download.
• If the model file exists, it loads it directly without downloading.

5. Start Chat Session

 

 

with model.chat_session():
    response = model.generate("How can I run LLMs efficiently on my laptop?", max_tokens=512)
    print(response)

• Opens a chat session with the model.
• Sends a prompt and prints the model's response.
• Limits the output to 512 tokens.

Why GGUF Format?

• GGUF is a modern binary format designed for efficient local inference.
• It bundles model weights, tokenizer, and metadata into a single file.
• Supports quantization (e.g., Q4), which reduces memory usage and speeds up inference.
• Ideal for CPU-only environments, which is why it's used in this script.

Summary

This script:

• Runs a quantized LLM locally using the GGUF format.
• Requires no GPU, making it lightweight and portable.
• Automatically downloads the model if not found.
• Starts a chat session and generates a response to a user-defined prompt.