Chapters (19)
- 0:00Intro & Demo
- 1:46Qwen 3 Architecture
- 2:36Prerequisites
- 4:01Code Setup & Imports
- 5:26Model Configuration
- 8:26Qwen 3 Specifics
- 12:24Training Hyperparameters
- 17:18Grouped Query Attention Logic
- 18:56Muon Optimizer Explained
- 29:02Data Loading & Tokenization
- 32:37RoPE Positional Embeddings
- 36:56Self-Attention Code
- 44:28Feed-Forward & SwiGLU
- 47:36Building the Final Model
- 52:34Evaluation & Optimizer Setup
- 54:08The Training Loop
- 55:43Running the Training
- 58:38Inference & Text Generation
- 1:00:51Final Results
Show the creator's full description
Lean how to create an LLM from scratch. In this tutorial you will build Qwen 3, one line at a time. Watch gradients flow, models learn, and AI come alive in real-time.
Code on Google Colab - https://colab.research.google.com/drive/12ndGn_mI7R1GTbGS8I2EvajW50esJRRk?usp=sharing
GitHub - https://gist.github.com/vukrosic/94dc965a22b0892042f44fed25918598
⭐️ Contents ⭐️
⌨ (0:00:00) Intro & Demo
⌨ (0:01:46) Qwen 3 Architecture
⌨ (0:02:36) Prerequisites
⌨ (0:04:01) Code Setup & Imports
⌨ (0:05:26) Model Configuration
⌨ (0:08:26) Qwen 3 Specifics
⌨ (0:12:24) Training Hyperparameters
⌨ (0:17:18) Grouped Query Attention Logic
⌨ (0:18:56) Muon Optimizer Explained
⌨ (0:29:02) Data Loading & Tokenization
⌨ (0:32:37) RoPE Positional Embeddings
⌨ (0:36:56) Self-Attention Code
⌨ (0:44:28) Feed-Forward & SwiGLU
⌨ (0:47:36) Building the Final Model
⌨ (0:52:34) Evaluation & Optimizer Setup
⌨ (0:54:08) The Training Loop
⌨ (0:55:43) Running the Training
⌨ (0:58:38) Inference & Text Generation
⌨ (1:00:51) Final Results
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