Building a GPT Model from Scratch Using PyTorch and Python: A Neural Network Transformer Deep Learning Project #chatgpt

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Create GPT From Scratch – Neural Network Transformer Deep Learning

Creating GPT From Scratch

Generative Pre-trained Transformer (GPT) is a state-of-the-art deep learning model for natural language processing tasks. In this article, we will discuss how to create a GPT model from scratch using PyTorch and Python programming languages.

Requirements

  • PyTorch
  • Python

Steps to Create GPT From Scratch

  1. Install PyTorch: First, make sure you have PyTorch installed on your system. You can use pip to install PyTorch by running the following command:
  2. pip install torch torchvision

  3. Import Libraries: Import the necessary libraries in your Python script:
  4. import torch
    import torch.nn as nn
    import torch.optim as optim
    import numpy as np
    from torch.nn.utils import clip_grad_norm_
    from collections import Counter

  5. Create GPT Model: Define the GPT model architecture using the Transformer architecture and self-attention mechanism.
  6. Training GPT Model: Train the GPT model using a large corpus of text data. You can use techniques such as gradient clipping and learning rate scheduling to improve training stability and convergence.
  7. Generate Text: Once the GPT model is trained, you can use it to generate text by providing a prompt and allowing the model to predict the next word.

Conclusion

In conclusion, creating a GPT model from scratch involves implementing the Transformer architecture and training the model on a large corpus of text data. By following the steps outlined in this article, you can build and train your own GPT model using PyTorch and Python.

Tags

#pytorch #python #chatgpt

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@brainxyz
11 months ago

Please checkout the source code in the description.
Some people like fast paced videos others like slow pacing but the best way to learn is to do it yourself. Pause the video at each step and follow the instructions. If you stuck at any point, compare your code with the source code provided in "tutorials" folder. The GitHub link is in the description.
Best wishes

@starplatinum3305
11 months ago

I HAVE TO SUB TO THIS DUDE, THIS DUDE IS CRAZY 😧

@ElonNusk234
11 months ago

Awesome ! what software did you use for animations ?

@yudelko
11 months ago

what is the econmical return(gain) of building gpt from scratch?

@amparoconsuelo9451
11 months ago

If I will order from Amazon a GPT assembly kit, what would it deliver me? How much would the kit cost?

@hassanmehmood8934
11 months ago

Thanks for creating this wonder full explanation.
Best video i ever seen on youtube.
Thanks @brainxyz superb content with great explanation.
kindly create more videos like this.

@madankd
11 months ago

Amazing content man it's gold

@mrm3875
11 months ago

Thx

@hawrotaha2647
11 months ago

May God bless you
Keep up bro

@agentzero3209
11 months ago

This is a good example of free high quality educational content available for free online.

@calabisan
11 months ago

I'm speechless. No one else has managed to sprint from light switches to the leading edge of technology in less than an hour, creating the impression as if it were a leisurely walk – very paradoxical. Overall, it was incredibly well done! Many thanks!

@bartoszstyperek6306
11 months ago

I do not get the 34:14 part. You said we have to 'pass inputs with various context lengths' , but you changed the sampled output ys length, from [i+ins+1:i+ins+1] to [i+1:i+ins+1] range :
ys = torch.stack([data[i+1:i+ins+1] for i in b]) .
so from [cat] -> [s] we got to [cat] -> [ats] . I could use some extra explanation, what is going on here .

@The-Martian73
11 months ago

Please Hunar keep doing this, and do never stop … your videos are legendary and iconic .

@fcf8269
11 months ago

Great video, but the tone and the pacing is what kill it

@blutoo1363
11 months ago

This video is the fucking bomb. Crisp editing, top notch humour and clear understanding. Hope you continue to bless us.

@skilz8098
11 months ago

From a programming and engineering perspective I do find these topics quite interesting.

However, from a consumer standpoint where the use of GPUs became a viable option in both A.I. programming as well as with Bitcoin Mining… these scalpers are responsible for driving the cost of GPUs to an all time high causing them to be ridiculously overpriced for the conventional gaming markets and causing them to be almost unaffordable for the basic consumer. Instead of high end GPUs costing a moderate few hundred dollars, they're now in the thousands.

This observational rant has nothing to do with this videos quality content and presentation. This video is actually quite good and the hard work, dedication and time invested is much appreciated, and it was quite enjoyable.

It's just that due to these miners and large corporations buying up all of the GPUs on launch in mass bulk causing GPU availability to become scarce and almost unaffordable the GPU manufacturers have or are taking advantage of this by raising their MSRP values keeping them at an all time high without even considering to lower them making them the new standard MSRP.

So instead of buying a top of the line GPU for about $500, a mid level GPU for about $200-300 and an entry level GPU for about $100… your newer entry level GPUs are closer to about $300-500, your mid level are now about $500 – $800, and your top of the line GPUs are now over $1k and some are even as high as $2k. GPUs now cost almost as much as the rest of the entire system. A good high end gaming rig use to only cost about $1,200 – $2,000 and now they're closer to $3-4k or even more.

How can we resolve this current issue? We need manufacturers to make dedicated cards that are designed specifically for these task. We need dedicated cards just for A.I. systems, and dedicated cards just for Mining. And let the miners, and large corporations to foot the bill for these more expensive and dedicated task cards while leaving the Gaming Markets and their GPU prices and availability alone. These current GPU prices (MSRPs) need to be dropped by either 1/2 or even 2/3rds of their current MSRP values!

Just an astute observation from a consumer in disgust. As for the material of this video and the content quality of it and the engineer within, excellent job sir! Keep up the good work!

@matiabem2346
11 months ago

itsbetter explanation 10q

@jpr9734
11 months ago

very good!!!

@user-me9em9fy4k
11 months ago

You've successfully helped me break through a mental wall and I cannot thank you enough.

@galbalandroid
11 months ago

Thanks for this amazing video! I gonna be watching it few more times just to make sure I understand the things good enough!
What is the code editor you're using?