# fufankeji/LLMs-Technology-Community-Beyondata

《赋范大模型技术社区》是针对各阶大模型学习者量身打造的基于各类大模型，包括环境设置、本地部署、高效微调、开发实战等技能在内的全流程指导！

Repository: https://github.com/fufankeji/LLMs-Technology-Community-Beyondata
Canonical: https://ross.abutalabs.com/products/llms-technology-community-beyondata
License Family: other
Last push: 2025-10-11T08:52:21+00:00

## Health v2 (maintenance only)
Score: 42/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 46, release rhythm 35, longevity 43
- inputs: {"age_days": 602, "days_push": 326, "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 1009, forks 160 (observed 2026-08-28T04:03:12.800464+00:00)

## What it is
A Chinese-language LLM technology community repository offering full-pipeline tutorials for large language models, covering environment setup, local deployment, efficient fine-tuning, RAG, Agent development, and MCP tools. Content is hosted as linked guides (mostly on Feishu wiki) targeting learners from beginner to enterprise-level project practice.

## Use cases
- learn how to deploy open-source LLMs locally
- fine-tune models like Qwen3 or GPT-OSS
- build RAG systems with multimodal PDF retrieval
- get started with LangChain and LangGraph for agent development
- deploy and use MCP tools for AI agents
- find structured LLM learning path from beginner to enterprise projects

## When to choose
- you want curated, step-by-step Chinese-language LLM tutorials
- you need guidance spanning deployment, fine-tuning, RAG, and agents in one place
- you are a beginner transitioning into LLM development

## When to avoid
- you need runnable production software rather than tutorial links
- you require English-language documentation
- you want an MIT/Apache-licensed codebase (no license is provided)

## Facets
- artifact type: learning-resource
- maturity: active
- function: llm-inference, llm-training, rag, agent-framework, mcp, prompt-engineering
- domain: large-language-models, tutorials, artificial-intelligence
- platform: python, cross-platform
- tags: llm-tutorials, fine-tuning, langchain, langgraph, dify, local-deployment, chinese-language, community, ai-agents, retrieval-augmented-generation

## Member repositories
- fufankeji/LLMs-Technology-Community-Beyondata (main) score 42

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:12.800464+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 2026-08-30T07:12:02.937647+00:00, confidence not recorded.
  - readme: https://github.com/fufankeji/LLMs-Technology-Community-Beyondata (fetched 2026-08-28T04:03:12.800464+00:00, sha 614a159cdc9a)
- Data as of 2026-08-30T08:39:29.467469+00:00.