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InternLM

Open-source LLM ecosystem from Shanghai AI Lab with InternLM models, chat demos, and tools for research and deployment.

CodingResearch

How it works / How to use

Open-source LLM ecosystem from Shanghai AI Lab with InternLM models, chat demos, and tools for research and deployment.

  1. Create an account and API key in InternLM following the official docs.
  2. Pick the model endpoint that matches latency, cost, and capability needs.
  3. Prototype with a short prompt and log token usage.
  4. Add retries, timeouts, and content filters appropriate to your application.

How to prompt

InternLM targets academic and research-oriented language tasks.

Summarize the methodology and key results of this machine learning paper in plain language for a graduate seminar.

Best output tips

  • Set spend alerts where the dashboard allows.
  • Cache stable completions when safe to reduce cost.
  • Rotate API keys on a regular schedule.
  • Verify current plans and limits on the official InternLM website.

Try this AI

Try InternLM

Product Details

Pricing, features, limits and latest updates

InternLM

Open-source LLM ecosystem from Shanghai AI Lab with InternLM models, chat demos, and tools for research and deployment.

No listed price

Pricing Plans

No listed plans.

Key Features

Chat

chat workflow (internlm.intern-ai.org.cn).

API

api workflow (internlm.intern-ai.org.cn).

Open Source

open source workflow (internlm.intern-ai.org.cn).

Ideas / Prompt experiences

Share a prompt that worked for you. Username and email are shown with your submission. External links are not allowed.

Example prompt

Research literature summary

Prompt

Summarize the methodology and key results of this machine learning paper in plain language for a graduate seminar.

Short explanation

InternLM targets academic and research-oriented language tasks.

Example prompt

Deploy a local chatbot

Prompt

Outline steps to fine-tune InternLM on a domain FAQ dataset and serve it with a Gradio chat interface.

Short explanation

Open models invite self-hosted customization for specialized domains.

Share your experience

Required fields are marked. Variation, result, and explanation are optional.

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