# InternLM/Tutorial

LLM&VLM Tutorial

Repository: https://github.com/InternLM/Tutorial
Canonical: https://ross.abutalabs.com/products/tutorial
Language: Python
License Family: other
Last push: 2026-04-22T08:39:08+00:00

## Health v2 (maintenance only)
Score: 62/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 78, release rhythm 35, longevity 71
- inputs: {"age_days": 994, "days_push": 133, "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 1972, forks 1469 (observed 2026-08-28T04:06:00.950172+00:00)

## What it is
A hands-on tutorial repository for the InternLM large language model ecosystem, structured as a multi-level challenge camp with tasks, docs, and videos. It covers Linux/Python/Git basics, LLM demos, prompt engineering, RAG with LlamaIndex, fine-tuning with XTuner, evaluation with OpenCompass, agent building with Lagent, deployment with LMDeploy, and multimodal InternVL.

## Use cases
- learn how to run InternLM on 8GB GPU
- practice RAG with LlamaIndex and InternLM
- fine-tune an LLM with XTuner
- evaluate LLMs with OpenCompass
- deploy quantized LLMs with LMDeploy
- build custom AI agents with Lagent
- deploy multimodal InternVL models
- learn Linux, Python, and Git basics for AI work

## When to choose
- you want structured hands-on exercises for the InternLM ecosystem
- you are a beginner learning LLM deployment, fine-tuning, and RAG
- you prefer tutorial content with tasks, docs, and videos

## When to avoid
- you need production-ready software rather than learning material
- you use models or toolchains outside the InternLM ecosystem
- you need a licensed or formally maintained library

## Facets
- artifact type: learning-resource
- maturity: active
- function: llm-inference, rag, prompt-engineering, machine-learning, llm-training
- domain: large-language-models, tutorials, artificial-intelligence, education, deep-learning
- platform: python
- tags: internlm, tutorial, hands-on-camp, fine-tuning, model-deployment, multimodal, agents, opencompass, xtuner, lmdeploy, linux, gpu

## Member repositories
- InternLM/Tutorial (main) score 62

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:00.950172+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 2026-08-30T03:05:00.448736+00:00, confidence not recorded.
  - readme: https://github.com/InternLM/Tutorial (fetched 2026-08-28T04:06:00.950172+00:00, sha d132c8004301)
- Data as of 2026-08-30T08:39:29.467469+00:00.