vibe-serve

Prepare and open VibeServe pull requests from local repo changes. Use when the user asks to create, open, publish, submit, or draft a PR for this repository, including tasks that need branch hygiene, targeted validation, PR intent reflection, PR template completion, commit/push, or GitHub pull request creation.

Create or update a VibeServe LLM model-serving example bundle for a new Hugging Face model, hardware target, and optimization workload. Use when the user wants to add an example under examples/model-serving, scaffold reference/accuracy_checker/benchmark inputs, adapt existing checker and HTTP benchmark scripts, or start an optimization run from a natural-language workload goal without adding new reusable framework code.

LLM and multimodal serving-system development. Activate when a task touches inference servers, latency / throughput / TTFT / TPOT, KV-cache, batching, attention kernels, CUDA graphs, speculative decoding, structured / grammar-constrained output, quantization, MoE serving, prefix caching, multi-modal (vision/speech/image/video) serving, porting a model to vLLM / SGLang / TensorRT-LLM, or serving on AWS Trainium (Neuron, torch-neuronx, NKI kernels).