协作头脑风暴
brainstorming
在实现前澄清需求、探索设计选择,并形成清晰的开发方向。
inference【开发与工程】Operate and troubleshoot OpenAssistant inference server, workers, text client, safety server, model configs, websocket work protocol, and SSE chat flows.
独立 GitHub 快照 · 自动分类 · 以原作者文档为准
Use this sub-skill when a task is about the OpenAssistant local inference stack: the FastAPI inference server, websocket workers, text client, safety server, model configuration selection, chat streaming, plugin calls, GPU sizing, or lightweight _lorem/distilgpt2 smoke checks.
references/api-reference.md._lorem, distilgpt2, OpenAssistant SFT/RLHF variants, quantization, or GPU memory sizing: read references/model-configs.md and run scripts/check_inference_config.py when a checkout is available.references/workflows.md.references/troubleshooting.md.These checks do not download models or start services:
python scripts/check_inference_config.py --repo-root <repo-root> --model-config _lorem
python scripts/check_inference_config.py --repo-root <repo-root> --model-config distilgpt2 --json
python scripts/check_inference_config.py --repo-root <repo-root> --list
Use _lorem when the user needs a CPU-only logical smoke path. Use distilgpt2 only when a tiny Hugging Face model download/cache is acceptable or already available. Real OpenAssistant model configs may require large downloads and GPU memory.
website sub-skill.backend sub-skill.