Tutorial

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Tutorial is an Open Source research & education tool that provides comprehensive learning resources for large language models. It offers practical guides and challenges for model fine-tuning, deployment, and evaluation.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is Tutorial?

Tutorial, developed by InternLM, is an open-source learning platform designed for developers to master large language models (LLMs) and vision-language models (VLMs). It offers a structured curriculum through 'camps' with challenges, documentation, and video tutorials covering foundational topics like Linux, Python, and Git, as well as advanced concepts such as prompt engineering, RAG implementation, model fine-tuning with XTuner, and deployment with LMDeploy. The platform also includes practical applications like building intelligent agents with Lagent and multi-modal model deployment. Participants can earn computational resources and certificates upon completing advanced challenges, fostering a community-driven learning environment.

Best used for

Ideal for developers and researchers who need to gain practical experience with large language models, implement advanced AI techniques like RAG, and fine-tune models for specific applications. Especially valuable for those seeking structured learning paths and hands-on challenges in the LLM/VLM domain.

Common actions

learn large models

fine-tune models

deploy models

evaluate models

build AI applications

workflowsautomated workflowlow-code/no-codeopen-sourceface swappingdeepfake"AI Agents"github copilotcollaboration

Capabilities

Key features

Structured learning camps
Practical challenges
Video tutorials
Model fine-tuning guides
Model deployment practices
Model evaluation

Target Audience

AI developersmachine learning engineersstudents

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of content does the InternLM/Tutorial offer?

The tutorial offers a comprehensive curriculum on large language models (LLMs) and vision-language models (VLMs), including foundational skills, prompt engineering, RAG implementation, model fine-tuning, deployment, and evaluation. It's structured into 'camps' with tasks, documentation, and videos.

Are there any incentives for completing the challenges?

Yes, participants can earn computational resources (算力点) for completing challenges, with higher rewards for advanced levels. Completing the 'advanced island' challenges also qualifies participants for a completion certificate.

How can I participate in the 'Shusheng Co-learning Program'?

You can join by filling out a questionnaire to enroll in the practical camp. You can then generate a unique sharing poster to invite others. Successfully inviting more participants can unlock additional computational resources and storage on the InternStudio platform.

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Subcategories trending in Research & Education

Academic Research Study Assistants Knowledge Management Course Creation Scientific Computing Summarization

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Also listed in

This tool also appears in

Coding & Development › Open Source & Models

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