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Merge transformer-training into dev
Complete Milestone 05 - 2017 Transformer implementation Major Features: - TinyTalks interactive dashboard with rich CLI - Complete gradient flow fixes (13 tests passing) - Multiple training examples (5-min, 10-min, levels 1-2) - Milestone celebration card (perceptron style) - Comprehensive documentation Gradient Flow Fixes: - Fixed reshape, matmul (3D), embedding, sqrt, mean, sub, div, GELU - All transformer components now fully differentiable - Hybrid attention approach for educational clarity + gradients Training Results: - 10-min training: 96.6% loss improvement, 62.5% accuracy - 5-min training: 97.8% loss improvement, 66.7% accuracy - Working chatbot with coherent responses Files Added: - tinytalks_dashboard.py (main demo) - tinytalks_chatbot.py, tinytalks_dataset.py - level1_memorization.py, level2_patterns.py - Comprehensive docs and test suites Ready for student use 2>&1
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tinytorch/text/tokenization.py
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tinytorch/text/tokenization.py
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# ╔═══════════════════════════════════════════════════════════════════════════════╗
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# ║ 🚨 CRITICAL WARNING 🚨 ║
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# ║ AUTOGENERATED! DO NOT EDIT! ║
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# ║ ║
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# ║ This file is AUTOMATICALLY GENERATED from source modules. ║
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# ║ ANY CHANGES MADE HERE WILL BE LOST when modules are re-exported! ║
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# ║ ║
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# ║ ✅ TO EDIT: modules/source/XX_tokenization/tokenization_dev.py ║
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# ║ ✅ TO EXPORT: Run 'tito module complete <module_name>' ║
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# ║ ║
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# ║ 🛡️ STUDENT PROTECTION: This file contains optimized implementations. ║
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# ║ Editing it directly may break module functionality and training. ║
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# ║ ║
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# ║ 🎓 LEARNING TIP: Work in modules/source/ - that's where real development ║
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# ║ happens! The tinytorch/ directory is just the compiled output. ║
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# ╚═══════════════════════════════════════════════════════════════════════════════╝
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# AUTOGENERATED! DO NOT EDIT! File to edit: ../../modules/source/10_tokenization/tokenization_dev.ipynb.
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# %% auto 0
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__all__ = ['Tokenizer', 'CharTokenizer', 'BPETokenizer']
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# %% ../../modules/source/10_tokenization/tokenization_dev.ipynb 0
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#| default_exp text.tokenization
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#| export
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import numpy as np
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from typing import List, Dict, Tuple, Optional, Set
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import json
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import re
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from collections import defaultdict, Counter
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# %% ../../modules/source/10_tokenization/tokenization_dev.ipynb 3
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import numpy as np
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