# wdndev/llama3-from-scratch-zh

从零实现一个 llama3 中文版

Repository: https://github.com/wdndev/llama3-from-scratch-zh
Canonical: https://ross.abutalabs.com/products/llama3-from-scratch-zh
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2024-06-12T15:56:19+00:00

## Health v2 (maintenance only)
Score: 24/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 59
- inputs: {"age_days": 832, "days_push": 812, "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 1054, forks 97 (observed 2026-08-28T04:03:24.056268+00:00)

## What it is
A Chinese-language Jupyter Notebook tutorial that implements the Llama3 8B model from scratch, translating naklecha's llama3-from-scratch guide. It walks through loading model weights, tokenization, and matrix operations to run inference manually.

## Use cases
- learn how llama3 works internally
- implement a transformer from scratch
- understand llm inference step by step
- study matrix operations in large language models
- learn llama3 architecture in chinese

## When to choose
- you want a guided, line-by-line educational walkthrough of Llama3 internals
- you prefer Chinese-language explanations
- you want a low-memory 2-layer variant for learning without heavy hardware

## When to avoid
- you need a production-ready LLM inference engine
- you need correct full-model inference outputs
- you want a BPE tokenizer implementation

## Facets
- artifact type: learning-resource
- maturity: stable
- function: llm-inference, machine-learning, deep-learning
- domain: large-language-models, deep-learning, tutorials
- platform: python, cross-platform
- tags: llama3, from-scratch, jupyter-notebook, chinese-translation, transformer-implementation, educational, natural-language-processing

## Member repositories
- wdndev/llama3-from-scratch-zh (main) score 24

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:24.056268+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 2026-08-30T06:59:03.916287+00:00, confidence not recorded.
  - readme: https://github.com/wdndev/llama3-from-scratch-zh (fetched 2026-08-28T04:03:24.056268+00:00, sha c5e0c9a86ead)
- Data as of 2026-08-30T08:39:29.467469+00:00.