---

name: omnilive description: "Guide InternLM-XComposer2.5-OmniLive audio, video-memory, runnable service entrypoints, and benchmark workflows with explicit execution gates." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

OmniLive Sub-skill

Use this sub-skill when the task is specifically about InternLM-XComposer2.5-OmniLive (IXC2.5-OL): audio understanding, OmniLive model directory layout, image/VLM smoke planning with the OmniLive base component, long/streaming video QA with memory, online SRS/FastAPI/Gradio deployments, or OmniLive audio/video benchmarks.

For generic InternLM-XComposer 2.5 image/video/chat/composition inference that does not depend on OmniLive audio, memory, SRS, or OmniLive checkpoints, route through the sibling model-inference sub-skill instead.

Operating checklist

1. Identify the requested OmniLive workflow:
   • audio ASR/classification;
   • base VLM image/video sanity check;
   • memory-backed video QA;
   • SRS + JavaScript frontend + FastAPI backend service;
   • Gradio frontend + three FastAPI backend processes;
   • audio/video/streaming benchmark planning.
2. Validate the local model root before writing commands. A complete downloaded OmniLive root is expected to expose component directories named audio/, base/, adapter/, memory/, and merge_lora/. The merge_lora/ component is an output of merging base/ with adapter/; memory video QA and online backends should not be planned against only base/ + adapter/.
3. Use the bundled helper scripts/check_omnilive_layout.py to inspect model layout safely. It never imports torch, starts services, downloads models, or reads an original source checkout.
4. Use the bundled helper scripts/render_service_plan.py to print a deployment plan for SRS or Gradio services. It renders commands and configuration edits but does not start listeners.
5. For approved local execution, use the repaired self-contained entrypoint bundles rather than source checkout files:
   • entrypoints/omnilive-examples/ for audio ASR/classification, base VLM image QA, base/ + adapter/ LoRA merge, and memory-backed video QA;
   • entrypoints/omnilive-gradio/ for the Gradio frontend plus FastAPI backend trio;
   • entrypoints/omnilive-srs/ for the SRS Docker launcher, JavaScript frontend, and FastAPI backend package.
6. Load the nearest detailed reference before producing a plan:
   • references/workflows.md for audio/base/memory examples and model arguments;
   • references/service-deployment.md for SRS, FastAPI, frontend, LAN IP, remote backend, VAD, and Gradio flags;
   • references/benchmark-workflows.md for audio/video benchmark layouts and launcher patterns;
   • references/troubleshooting.md for dependency, model layout, network, VAD, and threshold failures.

Bundled scripts and entrypoints

Safe planners/checkers:

• scripts/check_omnilive_layout.py
• scripts/render_service_plan.py

Runnable self-contained entrypoints:

• entrypoints/omnilive-examples/infer_audio.py
• entrypoints/omnilive-examples/infer_llm_base.py
• entrypoints/omnilive-examples/merge_lora.py
• entrypoints/omnilive-examples/infer_llm_with_memory.py
• entrypoints/omnilive-gradio/backend.sh
• entrypoints/omnilive-gradio/backend_vs.py
• entrypoints/omnilive-gradio/backend_llm.py
• entrypoints/omnilive-gradio/backend.py
• entrypoints/omnilive-gradio/frontend.py
• entrypoints/omnilive-gradio/launch_frontend.sh
• entrypoints/omnilive-srs/run_srs_docker.sh
• entrypoints/omnilive-srs/run_backend.sh
• entrypoints/omnilive-srs/run_frontend_dev.sh

Read the bundle README.md files before execution. These entrypoints are real CUDA/model/service workloads; keep validation and approval gates explicit.

Boundaries

• Include OmniLive-specific audio, memory/video, online-demo, and benchmark workflows.
• Do not describe third-party Melo TTS internals beyond dependency and failure notes.
• Do not start SRS, FastAPI, Gradio, Node, torch, Swift, LMDeploy, or benchmark jobs during planning/validation unless the user explicitly asks from a Researcher session with the required environment.
• Do not assume the original repository checkout is available. Provide self-contained plans and ask the user for their model root, service app location, dataset root, and host/network details when execution paths are needed.