vLLM · By vllm-project

Diagnose and optimize vLLM Omni diffusion workloads, especially Wan/Qwen/Flux-style image and video generation.

vllm-project/vllm-omni

Runs the end-to-end vLLM Ascend release process: opens the release checklist and feedback issues, scans for release-blocking bugs and test coverage gaps, and generates release notes and announcements.

vllm-project/vllm-ascend

Self-check your branch before creating a PR — catch dead code, prevent new model-specific Python examples, verify accuracy/perf claims, validate PR title format, and confirm merge readiness.

vllm-project/vllm-omni

Work on vLLM-Omni quantization for diffusion, autoregressive, omni, or multi-stage models.

vllm-project/vllm-omni

Adapts and debugs Hugging Face or local models to run on vLLM with Ascend NPU, validates them by serving, and delivers the result as one signed commit.

vllm-project/vllm-ascend

Review pull requests and local branches for vllm-project/vllm-omni with a frozen snapshot, module-design ownership, feature-design overlays, targeted validation, and concise evidence-backed findings.

vllm-project/vllm-omni

Add a new diffusion model (text-to-image, text-to-video, image-to-video, text-to-audio, image editing) to vLLM-Omni, including native non-Diffusers ports, reference-parity validation, Cache-DiT…

vllm-project/vllm-omni

Add or update an in-repository vLLM-Omni model recipe with verified task, input, output, hardware, command, feature, and validation contracts.

vllm-project/vllm-omni

Integrate a new text-to-speech model into vLLM-Omni from HuggingFace reference implementation through production-ready serving with streaming and CUDA graph acceleration.

vllm-project/vllm-omni

Run vLLM performance benchmark using synthetic random data to measure throughput, TTFT (Time to First Token), TPOT (Time per Output Token), and other key performance metrics.

vllm-project/vllm-skills

Find evidence-backed simplification candidates in vLLM-Omni and, when requested, turn them into focused proposals or code changes.

vllm-project/vllm-omni

Benchmark vLLM or OpenAI-compatible serving endpoints using vllm bench serve.

vllm-project/vllm-skills

Productionize a vLLM-Omni diffusion model after its Day-0 vertical slice works.

vllm-project/vllm-omni

Deploy vLLM to Kubernetes (K8s) with GPU support, health probes, and OpenAI-compatible API endpoint.

vllm-project/vllm-skills

Generate and run tests for vllm-project/vllm-omni with CI-aligned levels and markers; wire new tests into Buildkite (test-ready.yml for L1/L2, test-merge.yml for L3, test-nightly.yml for L4).

vllm-project/vllm-omni

Quick install and deploy vLLM, start serving with a simple LLM, and test OpenAI API.

vllm-project/vllm-skills

This is a skill for benchmarking the efficiency of automatic prefix caching in vLLM using fixed prompts, real-world datasets, or synthetic prefix/suffix patterns.

vllm-project/vllm-skills

Upgrade vllm-omni NPU model runners (OmniNPUModelRunner, NPUARModelRunner, NPUGenerationModelRunner) to align with the latest vllm-ascend NPUModelRunner while preserving omni-specific logic.

vllm-project/vllm-omni

Deploy vLLM using Docker (pre-built images or build-from-source) with NVIDIA GPU support and run the OpenAI-compatible server.

vllm-project/vllm-skills