# wyf3/llm_related

复现大模型相关算法及一些学习记录

Repository: https://github.com/wyf3/llm_related
Canonical: https://ross.abutalabs.com/products/llm_related
Language: Python
License Family: other
Last push: 2026-07-02T16:44:56+00:00

## Health v2 (maintenance only)
Score: 64/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 90, release rhythm 35, longevity 54
- inputs: {"age_days": 759, "days_push": 62, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3505, forks 471 (observed 2026-08-28T04:08:07.613116+00:00)

## What it is
A collection of Python implementations reproducing large language model algorithms, along with the author's learning notes. It serves as an educational resource for understanding LLM-related techniques from scratch.

## Use cases
- learn how transformer and LLM algorithms work internally
- reproduce LLM training algorithms from papers
- study notes for large language model fundamentals
- find reference Python implementations of LLM techniques
- prepare for machine learning interviews on LLM topics

## When to choose
- you want readable, educational implementations of LLM algorithms rather than production code
- you are learning LLM fundamentals and want worked examples in Python
- you want to study algorithm reproductions alongside explanatory notes

## When to avoid
- you need a production-ready LLM training or inference framework
- you require a maintained library with a license, releases, or API guarantees
- you need optimized, scalable training pipelines for real workloads

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, llm-training, llm-inference, deep-learning
- domain: large-language-models, machine-learning, deep-learning, tutorials
- platform: python
- tags: llm-algorithms, reproduction, study-notes, educational

## Member repositories
- wyf3/llm_related (main) score 64

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:07.613116+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 2026-08-29T18:35:46.597525+00:00, confidence not recorded.
  - readme: https://github.com/wyf3/llm_related (fetched 2026-08-28T04:08:07.613116+00:00, sha 82a8e576c0e7)
- Data as of 2026-08-30T08:39:29.467469+00:00.