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AI & LLM Engineering · vLLM · By sickn33
Skills
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| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 1 | Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. | sickn33/ | 47k | 1 repo | ~1.9k | Automated safety check: Pass | Apache-2.0 | yesterday |
| 2 | Curated upstream guidance for Huggingface Community Evals; use when the workflow matches the user goal. | sickn33/ | 47k | 1 repo | ~1.7k | Automated safety check: Pass | MIT | yesterday |
| 3 | Master local LLM inference, model selection, VRAM optimization, and local deployment using Ollama, llama.cpp, vLLM, and LM Studio. | sickn33/ | 47k | 2 repos | ~1.6k | Automated safety check: Pass | MIT | 2 days ago |
| 4 | Deploy and manage vLLM for high-throughput LLM inference. An agent skill from sickn33/agentic-awesome-skills. | sickn33/ | 47k | 2 repos | ~1.7k | Automated safety check: Pass | MIT | 2 days ago |
| 5 | Add and manage evaluation results in Hugging Face model cards. | sickn33/ | 47k | 2 repos | ~418 | Automated safety check: Pass | MIT | 2 days ago |
| 6 | Deploy an API gateway for LLM traffic with load balancing, rate limiting, key management, semantic caching, fallback routing, and cost tracking. | sickn33/ | 47k | 1 repo | ~2.1k | Automated safety check: Pass | MIT | 2 days ago |
| 7 | Auto-scale LLM inference clusters on Kubernetes using KEDA, custom GPU metrics, and horizontal pod autoscaling. | sickn33/ | 47k | 1 repo | ~2.1k | Automated safety check: Pass | MIT | 2 days ago |