LLMs-from-scratch adds Llama 3.2, Qwen support

// 4h agoTUTORIAL

LLMs-from-scratch adds Llama 3.2, Qwen support

Sebastian Raschka's masterclass in transformer architecture now implements Llama 3.2 and Qwen 3.5 from first principles. The project remains the premier resource for developers bridging the gap between high-level APIs and low-level PyTorch implementation.

// ANALYSIS

Building LLMs from scratch is no longer just an academic exercise; it's essential for understanding the efficiency trade-offs in modern production models.

–Support for Llama and Qwen architectures allows developers to swap modern weights into a transparent, hand-coded codebase
–New "Reasoning Model" chapters track the industry's shift toward inference-time scaling and RLVR
–Performance optimizations for Apple Silicon and consumer hardware make local model experimentation accessible to hobbyists
–The accompanying "LLM Architecture Gallery" provides the most up-to-date comparison of model fact sheets in the ecosystem

// TAGS

llmtrainingfine-tuningreasoningopen-sourcellms-from-scratch

DISCOVERED

4h ago

2026-05-13

PUBLISHED

4h ago

2026-05-13

RELEVANCE

9/ 10

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