---

name: "model-serving" description: "Plan DeepAnalyze model download, vLLM serving, Docker GPU launch, quantization, and tokenizer extension." metadata: disco-role: operating disable-model-invocation: true license: MIT

model-serving

Use this sub-skill for deployment planning around DeepAnalyze-8B and its model-adjacent setup.

Use this when

• Choosing a public model source for a local checkpoint.
• Picking a vLLM launch recipe from GPU memory and desired context length.
• Deciding whether to enable FP8 KV cache.
• Planning Docker GPU startup for model serving.
• Planning quantization or tokenizer tag extension before training.
• Using a remote API-key based model service instead of local serving.

Do not use this when

• The task is about API calls, files, or client code after the server exists → use api-and-clients.
• The task is about WebUI provider selection or frontend setup → use interactive-frontends.
• The task is about SFT, RL, or benchmark runs → use training-and-evaluation.

Operating flow

1. Read references/vllm-and-docker.md for the deployment matrix and command shape.
2. Use scripts/vllm_command_builder.py to print a dry-run launch command from the GPU-memory target.
3. Use references/model-customization.md and scripts/quantization_command_builder.py for copy-safe quantization planning.
4. Use scripts/token_tag_plan.py before any training run that starts from DeepSeek-R1-0528-Qwen3-8B and needs the DeepAnalyze tag set.
5. Use references/cloud-api-usage.md for remote API-key access hygiene.
6. Use references/troubleshooting.md for Windows, VRAM, missing path, FP8, bitsandbytes, and font issues.

Safety rules

• Default to dry-run command output; do not mutate a checkpoint unless the caller explicitly asks for that action.
• Never embed API keys in code, prompts, or image builds.
• Treat source scripts as evidence for planning, not as something to run automatically in this runtime skill.
• Keep any command templates self-contained and avoid linking out to source-repo documentation.