How to build your own ChatGPT ?

ChatGPT in modern era has become a tool as useful as the human brain as it works like an artificial brain for us humans. In this evolving era of artificial intelligence, having an AI of your own is like a boon. You can create, modify and upgrade your own artificial brain. Sounds like you can be a god now! Yes, if take proper steps using multimodal data you sure can be a god in this evolving era of Web3 development and artificial intelligence but the question remains, HOW?

In this blog, I’ll be preaching you about how to build your own ChatGPT like model, run it on a GPU platform via decentralized computing and use the powerful tools offered by Aptos blockchain and Spheron Network to make it a scalable infrastructure on Aptos Ecosystem. So, tighten your seatbelts dear developer to be a god of your own AI

WHAT TYPE OF AI?

We are going to build a ChatGPT-like AI, which means we’ll be creating an AI capable of generating unique text, images, videos, and more with multimodal capabilities. Models used in creating, deploying, and managing it include:

Aptos Blockchain: It will provide security, transactions and smart contact support through move programming.
Spheron Network: This will help you to deploy and modify your AI model with ease as it provides cost-efficient compute resources.

DEVELOPMENT ENVIRONMENT:

“ANY CREATION NEEDS CERTAIN CONDITIONS AND EVIRONMENT TO BE SUCCESFULLY CREATED.” Same is the case of a fully functioning generative AI. They are as follows -

GPU: A machine must be attached to local GPU Networks, cloud networks like Google Cloud or a decentralized GPU network.
Programming:
1. Python- here I’m using python as the programming language as it is an effective language and easy to use for programming of AI.
2. Trained Models- trained model like GPT3 and GPT4 must be used to train AI or outsourcing of the information could be done via web.
3. PyTorch: library that will be very helpful with the information and researching part regarding the project.

FRAMEWORK: Use clip by OpenAI for creating an interactive model like ChatGPT.

CREATION OF THE AI

Integrating a pre-trained model:

First, you’ll want to load a pre-trained model from Hugging Face Transformers and get it set up to run on a GPU. This step lets you take advantage of an already trained model, so you can focus on fine-tuning it for your needs rather than starting from zero. By configuring it to use a GPU, you’ll ensure your model runs faster and can handle more complex tasks efficiently, making the overall development process smoother and quicker-
```
  from transformers import GPT2LMHeadModel, GPT2Tokenizer
  import torch

  model_name = "gpt2"
  tokenizer = GPT2Tokenizer.from_pretrained(model_name)
  model = GPT2LMHeadModel.from_pretrained(model_name)

  # Load the model onto the GPU
  device = "cuda" if torch.cuda.is_available() else "cpu"
  model.to(device)
```

Add clip model for text and images:

This setup allows your ChatGPT model to work with both text and images smoothly, making it more flexible and capable of handling different kinds of input-

  from transformers import CLIPProcessor, CLIPModel

  # Load the CLIP model for multimodal data
  clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
  processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")

  # Process images and text
  inputs = processor(text=["A picture of a dog"], images=[image], return_tensors="pt", padding=True)
  outputs = clip_model(**inputs)

DO FINE-TUNING OF THE MODEL:

To make the ChatGPT model fit your needs, fine-tuning is really important. PyTorch makes this easy, letting you adjust large models quickly on GPU platforms. This helps ensure the model works well for your specific application-

from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(
    output_dir='./results',
    per_device_train_batch_size=8,
    num_train_epochs=3,
    save_steps=10_000,
    save_total_limit=2,
    logging_dir='./logs',
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
)

trainer.train()

DEPLOY ON APTOS AND SPHERON NETWORK:

AS PER THE MODEL OF OUR AI, let’s deploy it using Spheron Network on the Aptos Blockchain. This integration ensures decentralized deployment with scalable infrastructure and the security benefits of Aptos:

ON APTOS BLOCKCHAIN-

To use the Aptos blockchain, you’ll need to add the Aptos SDK to your backend. This integration gives you access to features like gasless transactions, Aptos keyless, and the ability to deploy smart contracts that help manage user access and interactions.

Here’s a simple example of a tiny contract on Aptos:

module ChatGPTAccessControl {

    public fun grant_access(account: &signer) {
        // Logic to grant access to ChatGPT service
    }

    public fun revoke_access(account: &signer) {
        // Logic to revoke access
    }
}

ON SPHERON-

Spheron makes it easy to deploy your AI system. Here’s a quick rundown of the steps:
1. Connect your GitHub repository: Upload your ChatGPT codebase to GitHub
2. Select your framework: Pick a framework, like Fast API, for a smooth deployment experience.
3. Set up environment variables: This includes things like your API keys, model paths, and other configurations.
4. Deploy: Once everything is set up, Spheron handles the deployment, making sure your AI system is up and running on a decentralized network.

TESTING THE PERFORMANCE:

To make sure your AI model runs smoothly on GPU platforms, you can keep an eye on its performance with tools like TensorBoard or NVIDIA’s Nsight. These tools give you a clear view of how well your model is performing and help spot any slowdowns or issues before they become a problem. Plus, using decentralized platforms like Spheron helps you balance costs while ensuring that your model stays online and accessible, offering both affordability and reliability without sacrificing performance.

CONCLUSION

Creating your own multimodal AI and running it on a decentralized GPU platform lets you process data in real-time and scale your application easily with the help of the Aptos Blockchain and Spheron Network. By using Move programming and smart contracts, you can build an AI application that’s not just powerful but also secure and scalable, even more powerful all up to date than ChatGPT.

If you want to dive deeper into this exciting world of Web3 development, take a look at the official documentation for Aptos Blockchain and Spheron Network. They have plenty of resources to help you explore all the possibilities and make many more exciting and innovative tech.!

Be a god of technology, by creating, innovating and nurturing technology.

#YOUROWNAI

How to build your own ChatGPT with multimodal data and run it on a GPU platform

Building an AI from scratch and deploying on Aptos and Spheron network