# amitshekhariitbhu/llm-internals

Learn LLM internals step by step - from tokenization to attention to inference optimization.

Repository: https://github.com/amitshekhariitbhu/llm-internals
Canonical: https://ross.abutalabs.com/products/llm-internals
Homepage: https://outcomeschool.com/program/ai-and-machine-learning
License: Apache-2.0
License Family: permissive
Topics: attention-is-all-you-need, attention-mechanism, large-language-models, learn-llm, llm, llm-internals
Last push: 2026-08-19T04:35:36+00:00

## Health v2 (maintenance only)
Score: 58/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 98, release rhythm 35, longevity 10
- inputs: {"age_days": 143, "days_push": 14, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, young
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1533, forks 174 (observed 2026-08-28T04:04:59.636859+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: active
- function: machine-learning, llm-inference, nlp, developer-tools
- domain: large-language-models, deep-learning, machine-learning, tutorials, education
- platform: cross-platform
- tags: llm-internals, tokenization, attention-mechanism, transformers, bpe, educational-content, blogs-and-videos

## Member repositories
- amitshekhariitbhu/llm-internals (main) score 58

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:59.636859+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:31:14.479527+00:00, confidence not recorded.
  - readme: https://github.com/amitshekhariitbhu/llm-internals (fetched 2026-08-28T04:04:59.636859+00:00, sha 38807aff942a)
  - homepage: https://outcomeschool.com/program/ai-and-machine-learning (fetched 2026-08-29T11:33:34.565325+00:00, sha 7cb7eb502986)
- Data as of 2026-08-30T08:39:29.467469+00:00.