# InternLM/InternLM-XComposer

InternLM-XComposer2.5-OmniLive: A Comprehensive Multimodal System for Long-term Streaming Video and Audio Interactions

Repository: https://github.com/InternLM/InternLM-XComposer
Canonical: https://ross.abutalabs.com/products/internlm-xcomposer
Language: Python
License: Apache-2.0
License Family: permissive
Topics: chatgpt, visual-language-learning, multi-modality, foundation, gpt-4, instruction-tuning, mllm, multimodal, vision-language-model, language-model, large-language-model, large-vision-language-model, llm, vision-transformer, gpt, supervised-finetuning
Last push: 2025-05-26T15:59:34+00:00

## Health v2 (maintenance only)
Score: 38/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 23, release rhythm 35, longevity 76
- inputs: {"age_days": 1072, "days_push": 464, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2925, forks 174 (observed 2026-08-28T04:07:30.397083+00:00)

## What it is
InternLM-XComposer is a family of large vision-language models from the InternLM team, including XComposer2.5 for long-context multimodal understanding, an OmniLive system for streaming video/audio interaction, and a multimodal reward model. It ships model weights, training code, and evaluation scripts for multimodal comprehension and text-image composition.

## Use cases
- run a vision-language model for image and video understanding
- build a multimodal chatbot that processes streaming video and audio
- fine-tune a large vision-language model with supervised finetuning
- score multimodal model outputs with a reward model
- understand long-context video and high-resolution images with an LLM

## When to choose
- you need an open Apache-2.0 vision-language model with weights and training code
- you want streaming real-time video and audio interaction in one system
- you need long-context or 4K-resolution image understanding

## When to avoid
- you only need text-only LLM inference
- you lack GPU resources for 7B-scale multimodal models
- you need a production-ready managed multimodal API rather than self-hosted models

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, llm-inference, chatbot, nlp, image-processing, audio-processing, video-processing
- domain: large-language-models, machine-learning, computer-vision, artificial-intelligence
- platform: python, cross-platform
- tags: vision-language-model, multimodal, streaming-video, streaming-audio, reward-model, instruction-tuning, foundation-model, internlm, natural-language-processing, audio, video, gpu, linux

## Member repositories
- InternLM/InternLM-XComposer (main) score 38

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:30.397083+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 2026-08-30T07:33:37.208227+00:00, confidence not recorded.
  - readme: https://github.com/InternLM/InternLM-XComposer (fetched 2026-08-28T04:07:30.397083+00:00, sha e8fabc636abb)
- Data as of 2026-08-30T08:39:29.467469+00:00.