# km1994/LLMs_interview_notes

该仓库主要记录 大模型（LLMs） 算法工程师相关的面试题

Repository: https://github.com/km1994/LLMs_interview_notes
Canonical: https://ross.abutalabs.com/products/llms_interview_notes
License: Apache-2.0
License Family: permissive
Last push: 2024-12-26T14:14:05+00:00

## Health v2 (maintenance only)
Score: 28/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 77
- inputs: {"age_days": 1082, "days_push": 615, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2601, forks 171 (observed 2026-08-28T04:07:03.381586+00:00)

## What it is
A curated collection of interview questions and study notes for large language model (LLM) algorithm engineer positions, compiled from the authors' personal interview experience. It covers topics like transformer architecture, attention variants, layer normalization, and training objectives, with answers hosted externally.

## Use cases
- prepare for LLM algorithm engineer interviews
- study large language model fundamentals
- review transformer and attention mechanism concepts
- find common LLM interview questions with answers
- brush up on deep learning theory before job interviews

## When to choose
- you are interviewing for an LLM or NLP algorithm engineer role
- you want a structured question bank covering LLM theory and architecture details
- you prefer community-curated interview notes in Chinese

## When to avoid
- you need runnable code or a software library rather than study notes
- you want fully self-contained answers, since many link to an external paid community
- you need up-to-date coverage of models released after late 2024

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, developer-tools
- domain: large-language-models, machine-learning, tutorials, education
- platform: cross-platform
- tags: llm-interview, interview-preparation, study-notes, chinese-language, algorithm-engineer

## Member repositories
- km1994/LLMs_interview_notes (main) score 28

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:03.381586+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 2026-08-30T02:21:10.592634+00:00, confidence not recorded.
  - readme: https://github.com/km1994/LLMs_interview_notes (fetched 2026-08-28T04:07:03.381586+00:00, sha ddbea9838069)
- Data as of 2026-08-30T08:39:29.467469+00:00.