vllm-omni-quantization
vllm-omni-quantization is an MLOps & model serving skill for AI agents, published by hsliuustc0106 in the vllm-omni-skills repository on GitHub. Skillspace rates vllm-omni-quantization C (71/100) and it passed the safety scan; the rating is based on static analysis only.
Use when working on vLLM-Omni quantization for autoregressive, diffusion, or multi-stage omni models, choosing methods such as `awq`, `gptq`, `fp8`, `int8`, `gguf`, or ModelOpt checkpoints, adding quantized model support, or debugging memory, loader, quality, or performance issues.
- Grade
- C
- Quality
- 71 / 100
- Safety
- Passed
- GitHub stars
- 82
Unproven: rated by static analysis only, not yet compared head-to-head.
Our two independent classifiers differ on this skill's category, so treat the placement as provisional.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 94 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- No issues found
- License
- Apache-2.0
- Adoption
- Adopted by 0 other repositories, in 0 collections
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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