Skip to content
EN
English 简体中文 soon 日本語 soon

书生(InternLM)

Open-source InternLM platform

Visit official site

What InternLM is

InternLM is an open-source large model ecosystem published by the Shanghai Artificial Intelligence Laboratory. The platform brings together the InternLM3 language models and InternVL multimodal models, supports long context and chained inference, and exposes the whole stack through standard APIs plus the LMDeploy option for private deployment.

What you can do with it

  • Deploy models on your own infrastructure
  • Call the models through an OpenAI-compatible SDK or CLI
  • Build dialogue, document parsing and code assistance features
  • Develop image understanding applications with InternVL
  • Manage API tokens and access centrally

Who it is for

  • Developers who want open models with self-hosting
  • Enterprise teams building internal knowledge services
  • Researchers working on multimodal models
  • Educators running training programmes

What to watch out for

  • Self-hosting carries fine-tuning, evaluation and security work on your side
  • Review the licence and model card before commercial use
  • Context limits and quality differ per model release; test with your own tasks
  • Internal deployments still need controls over who can upload data

Pros & cons

✓ What we like

  • Open weights with an established deployment toolchain
  • OpenAI-compatible interface lowers migration cost
  • Covers language and multimodal models

! What to watch out for

  • Self-hosting requires real engineering effort
  • Quality varies across model versions
  • Governance of internal deployments is on you

FAQ

Who publishes it?

The Shanghai Artificial Intelligence Laboratory publishes the platform and models.

Can it run privately?

Yes. LMDeploy is offered as the self-hosted deployment route alongside standard APIs.

Is it compatible with existing tooling?

The platform states compatibility with the OpenAI SDK and CLI, plus Python examples.

Last reviewed: 2026-09-14

More AI chatbot tools

View all →

How we review