InternLM/xtuner

Health v2 · maintenance only

Adoption not part of the score

5183 stars · 445 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

XTuner is an open-source LLM training engine from InternLM designed for fine-tuning ultra-large-scale Mixture-of-Experts (MoE) models, with dropless training and long-sequence support. It also serves as a flexible toolkit for parameter-efficient fine-tuning of large language and multimodal models.

Use cases

• fine-tune a large language model on custom data
• train 200B-scale MoE models efficiently
• fine-tune multimodal models like InternVL or Qwen-VL
• train LLMs with 64k sequence lengths
• run parameter-efficient fine-tuning like LoRA
• reinforcement learning training for LLMs

When to choose

• you need to fine-tune large or MoE-based LLMs like DeepSeek-V3, Qwen3-MoE, or Kimi-K2
• you want memory-efficient long-sequence training without complex 3D parallelism
• you need a flexible open-source toolkit supporting both SFT and RL training
• you work with multimodal models such as InternVL or Qwen3-VL

When to avoid

• you only need inference or serving rather than training
• you need traditional dense-model distributed training with full 3D parallelism at extreme scale
• you want a no-code GUI training tool

Facets

library · maturity active

llm-training machine-learning deep-learning rag large-language-models deep-learning machine-learning artificial-intelligence python moe fine-tuning parameter-efficient-fine-tuning multimodal reinforcement-learning deepseek-v3 qwen3 internvl long-context gpu linux docker

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markdown · JSON · MCP: product_card(name="InternLM/xtuner")