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Building a Basic PyTorch Transformer

This repository contains a Jupyter Notebook that breaks down the implementation of a Transformer model using PyTorch. It provides a "from-scratch" approach to understanding how modern NLP models process sequential data.

Project Overview

The project follows the architecture introduced in the landmark paper "Attention is All You Need." It focuses on the modular implementation of the encoder and decoder components.

Key Features:

  • Input Embeddings: Converting token IDs into dense vectors scaled by the square root of the model dimension.
  • Positional Encoding: Using sine and cosine functions to inject sequence order information into embeddings.
  • Multi-Head Attention: A custom class implementation that handles linear projections for Queries, Keys, and Values, head splitting, and attention weight computation.
  • Model Inspection: Visualizing the full nn.Transformer object structure, including encoder/decoder layers, normalization, and dropout.

Installation

  1. Clone the repository: 
    git clone https://github.com/Joe-Naz01/transformers.git
cd transformers
conda create -n transformers
conda activate transformers
pip install requirements.txt

About

A deep learning project that implements and explains the fundamental building blocks of the Transformer model using PyTorch. The notebook covers the implementation of Input Embeddings, Positional Encoding, and a custom Multi-Head Attention mechanism, providing a step-by-step guide to how these components transform data.

Topics

artificial-intelligenceattentiondata-scienceinput-embeddingsmachine-learningmulti-head-attentionpositional-encodingself-attentiontransformers

Resources

Readme
Activity

Stars

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Repository files navigation
Building a Basic PyTorch Transformer
This repository contains a Jupyter Notebook that breaks down the implementation of a Transformer model using PyTorch. It provides a "from-scratch" approach to understanding how modern NLP models process sequential data.
Project Overview
The project follows the architecture introduced in the landmark paper "Attention is All You Need." It focuses on the modular implementation of the encoder and decoder components.
Key Features:
  • Input Embeddings: Converting token IDs into dense vectors scaled by the square root of the model dimension.
  • Positional Encoding: Using sine and cosine functions to inject sequence order information into embeddings.
  • Multi-Head Attention: A custom class implementation that handles linear projections for Queries, Keys, and Values, head splitting, and attention weight computation.
  • Model Inspection: Visualizing the full nn.Transformer object structure, including encoder/decoder layers, normalization, and dropout.
Installation
  1. Clone the repository:

    git clone https://github.com/Joe-Naz01/transformers.git
cd transformers
conda create -n transformers
conda activate transformers
pip install requirements.txt
About
A deep learning project that implements and explains the fundamental building blocks of the Transformer model using PyTorch. The notebook covers the implementation of Input Embeddings, Positional Encoding, and a custom Multi-Head Attention mechanism, providing a step-by-step guide to how these components transform data.
Topics
artificial-intelligenceattentiondata-scienceinput-embeddingsmachine-learningmulti-head-attentionpositional-encodingself-attentiontransformers
Resources
Readme
Activity
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0 watching
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
Building a Basic PyTorch Transformer
This repository contains a Jupyter Notebook that breaks down the implementation of a Transformer model using PyTorch. It provides a "from-scratch" approach to understanding how modern NLP models process sequential data.
Project Overview
The project follows the architecture introduced in the landmark paper "Attention is All You Need." It focuses on the modular implementation of the encoder and decoder components.
Key Features:
  • Input Embeddings: Converting token IDs into dense vectors scaled by the square root of the model dimension.
  • Positional Encoding: Using sine and cosine functions to inject sequence order information into embeddings.
  • Multi-Head Attention: A custom class implementation that handles linear projections for Queries, Keys, and Values, head splitting, and attention weight computation.
  • Model Inspection: Visualizing the full nn.Transformer object structure, including encoder/decoder layers, normalization, and dropout.
Installation
  1. Clone the repository:

    git clone https://github.com/Joe-Naz01/transformers.git
cd transformers
conda create -n transformers
conda activate transformers
pip install requirements.txt
About
A deep learning project that implements and explains the fundamental building blocks of the Transformer model using PyTorch. The notebook covers the implementation of Input Embeddings, Positional Encoding, and a custom Multi-Head Attention mechanism, providing a step-by-step guide to how these components transform data.
Topics
artificial-intelligenceattentiondata-scienceinput-embeddingsmachine-learningmulti-head-attentionpositional-encodingself-attentiontransformers
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
Building a Basic PyTorch Transformer
This repository contains a Jupyter Notebook that breaks down the implementation of a Transformer model using PyTorch. It provides a "from-scratch" approach to understanding how modern NLP models process sequential data.
Project Overview
The project follows the architecture introduced in the landmark paper "Attention is All You Need." It focuses on the modular implementation of the encoder and decoder components.
Key Features:
  • Input Embeddings: Converting token IDs into dense vectors scaled by the square root of the model dimension.
  • Positional Encoding: Using sine and cosine functions to inject sequence order information into embeddings.
  • Multi-Head Attention: A custom class implementation that handles linear projections for Queries, Keys, and Values, head splitting, and attention weight computation.
  • Model Inspection: Visualizing the full nn.Transformer object structure, including encoder/decoder layers, normalization, and dropout.
Installation
  1. Clone the repository:

    git clone https://github.com/Joe-Naz01/transformers.git
cd transformers
conda create -n transformers
conda activate transformers
pip install requirements.txt
About
A deep learning project that implements and explains the fundamental building blocks of the Transformer model using PyTorch. The notebook covers the implementation of Input Embeddings, Positional Encoding, and a custom Multi-Head Attention mechanism, providing a step-by-step guide to how these components transform data.
Topics
artificial-intelligenceattentiondata-scienceinput-embeddingsmachine-learningmulti-head-attentionpositional-encodingself-attentiontransformers
Resources
Readme
Activity
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0 watching
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Repository files navigation
Building a Basic PyTorch Transformer
This repository contains a Jupyter Notebook that breaks down the implementation of a Transformer model using PyTorch. It provides a "from-scratch" approach to understanding how modern NLP models process sequential data.
Project Overview
The project follows the architecture introduced in the landmark paper "Attention is All You Need." It focuses on the modular implementation of the encoder and decoder components.
Key Features:
  • Input Embeddings: Converting token IDs into dense vectors scaled by the square root of the model dimension.
  • Positional Encoding: Using sine and cosine functions to inject sequence order information into embeddings.
  • Multi-Head Attention: A custom class implementation that handles linear projections for Queries, Keys, and Values, head splitting, and attention weight computation.
  • Model Inspection: Visualizing the full nn.Transformer object structure, including encoder/decoder layers, normalization, and dropout.
Installation
  1. Clone the repository:

    git clone https://github.com/Joe-Naz01/transformers.git
cd transformers
conda create -n transformers
conda activate transformers
pip install requirements.txt
About
A deep learning project that implements and explains the fundamental building blocks of the Transformer model using PyTorch. The notebook covers the implementation of Input Embeddings, Positional Encoding, and a custom Multi-Head Attention mechanism, providing a step-by-step guide to how these components transform data.
Topics
artificial-intelligenceattentiondata-scienceinput-embeddingsmachine-learningmulti-head-attentionpositional-encodingself-attentiontransformers
Resources
Readme
Activity
Stars
0 stars
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0 watching
Forks
0 forks
Report repository
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Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
Building a Basic PyTorch Transformer
This repository contains a Jupyter Notebook that breaks down the implementation of a Transformer model using PyTorch. It provides a "from-scratch" approach to understanding how modern NLP models process sequential data.
Project Overview
The project follows the architecture introduced in the landmark paper "Attention is All You Need." It focuses on the modular implementation of the encoder and decoder components.
Key Features:
  • Input Embeddings: Converting token IDs into dense vectors scaled by the square root of the model dimension.
  • Positional Encoding: Using sine and cosine functions to inject sequence order information into embeddings.
  • Multi-Head Attention: A custom class implementation that handles linear projections for Queries, Keys, and Values, head splitting, and attention weight computation.
  • Model Inspection: Visualizing the full nn.Transformer object structure, including encoder/decoder layers, normalization, and dropout.
Installation
  1. Clone the repository:

    git clone https://github.com/Joe-Naz01/transformers.git
cd transformers
conda create -n transformers
conda activate transformers
pip install requirements.txt
About
A deep learning project that implements and explains the fundamental building blocks of the Transformer model using PyTorch. The notebook covers the implementation of Input Embeddings, Positional Encoding, and a custom Multi-Head Attention mechanism, providing a step-by-step guide to how these components transform data.
Topics
artificial-intelligenceattentiondata-scienceinput-embeddingsmachine-learningmulti-head-attentionpositional-encodingself-attentiontransformers
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
Building a Basic PyTorch Transformer
This repository contains a Jupyter Notebook that breaks down the implementation of a Transformer model using PyTorch. It provides a "from-scratch" approach to understanding how modern NLP models process sequential data.
Project Overview
The project follows the architecture introduced in the landmark paper "Attention is All You Need." It focuses on the modular implementation of the encoder and decoder components.
Key Features:
  • Input Embeddings: Converting token IDs into dense vectors scaled by the square root of the model dimension.
  • Positional Encoding: Using sine and cosine functions to inject sequence order information into embeddings.
  • Multi-Head Attention: A custom class implementation that handles linear projections for Queries, Keys, and Values, head splitting, and attention weight computation.
  • Model Inspection: Visualizing the full nn.Transformer object structure, including encoder/decoder layers, normalization, and dropout.
Installation
  1. Clone the repository:

    git clone https://github.com/Joe-Naz01/transformers.git
cd transformers
conda create -n transformers
conda activate transformers
pip install requirements.txt
About
A deep learning project that implements and explains the fundamental building blocks of the Transformer model using PyTorch. The notebook covers the implementation of Input Embeddings, Positional Encoding, and a custom Multi-Head Attention mechanism, providing a step-by-step guide to how these components transform data.
Topics
artificial-intelligenceattentiondata-scienceinput-embeddingsmachine-learningmulti-head-attentionpositional-encodingself-attentiontransformers
Resources
Readme
Activity
Stars
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0 watching
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Repository files navigation
Building a Basic PyTorch Transformer
This repository contains a Jupyter Notebook that breaks down the implementation of a Transformer model using PyTorch. It provides a "from-scratch" approach to understanding how modern NLP models process sequential data.
Project Overview
The project follows the architecture introduced in the landmark paper "Attention is All You Need." It focuses on the modular implementation of the encoder and decoder components.
Key Features:
  • Input Embeddings: Converting token IDs into dense vectors scaled by the square root of the model dimension.
  • Positional Encoding: Using sine and cosine functions to inject sequence order information into embeddings.
  • Multi-Head Attention: A custom class implementation that handles linear projections for Queries, Keys, and Values, head splitting, and attention weight computation.
  • Model Inspection: Visualizing the full nn.Transformer object structure, including encoder/decoder layers, normalization, and dropout.
Installation
  1. Clone the repository:

    git clone https://github.com/Joe-Naz01/transformers.git
cd transformers
conda create -n transformers
conda activate transformers
pip install requirements.txt
About
A deep learning project that implements and explains the fundamental building blocks of the Transformer model using PyTorch. The notebook covers the implementation of Input Embeddings, Positional Encoding, and a custom Multi-Head Attention mechanism, providing a step-by-step guide to how these components transform data.
Topics
artificial-intelligenceattentiondata-scienceinput-embeddingsmachine-learningmulti-head-attentionpositional-encodingself-attentiontransformers
Resources
Readme
Activity
Stars
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