amitshekhariitbhu/llm-internals resource

Health v2 · maintenance only

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1533 stars · 174 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A curated collection of blogs and videos teaching how large language models work internally, from tokenization and BPE to attention math and inference optimization. It is maintained by Amit Shekhar of Outcome School and grows over time with new topics.

Use cases

• learn how llms work internally
• understand tokenization and byte pair encoding
• learn the math behind attention q k v
• understand transformer architecture step by step
• learn inference optimization like kv cache and paged attention
• prepare for ai engineer interviews
• transition from software engineer to llm engineer

When to choose

• you want conceptual, step-by-step explanations of LLM internals with worked numeric examples
• you prefer learning through blogs and videos rather than code
• you are a developer transitioning into AI/ML roles and need fundamentals

When to avoid

• you need a runnable library or code framework for building LLM applications
• you want hands-on training notebooks or exercises rather than explanations
• you need comprehensive coverage of every LLM topic today, as the series is still growing

Facets

learning-resource · maturity active

machine-learning llm-inference nlp developer-tools large-language-models deep-learning machine-learning tutorials education cross-platform llm-internals tokenization attention-mechanism transformers bpe educational-content blogs-and-videos

Member repositories

For agents

markdown · JSON · MCP: product_card(name="amitshekhariitbhu/llm-internals")