@DanKornas: Stop learning LLMs from disconnected tutorials. LLM from Scratch is a hands-on PyTorch curriculum for builders who want…

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open-source pytorch llm curriculum training fine-tuning alignment

Summary

A hands-on PyTorch curriculum that teaches LLM training from transformer basics through fine-tuning and alignment, including RLHF and GRPO.

Stop learning LLMs from disconnected tutorials. LLM from Scratch is a hands-on PyTorch curriculum for builders who want to understand how LLMs are trained, modernized, and aligned. It helps you move from concepts to implementation by organizing the path from transformer basics to tiny-model training, scaling, fine-tuning, reward modeling, and RLHF. Key features: • End-to-end curriculum – follows pretraining → finetuning → alignment from foundations through RLHF • Transformer from first principles – covers positional embeddings, self-attention, attention heads, MLPs, residuals, LayerNorm, and full blocks • Tiny LLM training loop – includes tokenization, batching, cross-entropy, sampling, validation loss, and a no-Trainer training loop • Modern architecture upgrades – walks through RMSNorm, RoPE, SwiGLU, KV cache, sliding-window attention, and streaming cache ideas • Alignment path included – covers SFT, reward modeling, PPO-style RLHF, and GRPO with concrete training-loop notes It’s open-source (GPL-3.0 license). Link in the reply
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Cached at: 06/03/26, 07:47 AM

Stop learning LLMs from disconnected tutorials.

LLM from Scratch is a hands-on PyTorch curriculum for builders who want to understand how LLMs are trained, modernized, and aligned.

It helps you move from concepts to implementation by organizing the path from transformer basics to tiny-model training, scaling, fine-tuning, reward modeling, and RLHF.

Key features:

• End-to-end curriculum – follows pretraining → finetuning → alignment from foundations through RLHF • Transformer from first principles – covers positional embeddings, self-attention, attention heads, MLPs, residuals, LayerNorm, and full blocks • Tiny LLM training loop – includes tokenization, batching, cross-entropy, sampling, validation loss, and a no-Trainer training loop • Modern architecture upgrades – walks through RMSNorm, RoPE, SwiGLU, KV cache, sliding-window attention, and streaming cache ideas • Alignment path included – covers SFT, reward modeling, PPO-style RLHF, and GRPO with concrete training-loop notes

It’s open-source (GPL-3.0 license).

Link in the reply

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