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Docker · By Orchestra-Research
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| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 1 | Deploys LLMs with vLLM for high-throughput serving, covering the OpenAI-compatible server, offline batch inference, monitoring and a Docker rollout. | Orchestra-Research/ | 13k | 5 repos | ~2.3k | Automated safety check: Pass | MIT | 3 mo ago |
| 2 | Benchmarks code generation models with the BigCode Evaluation Harness across HumanEval, MBPP, MultiPL-E and other suites using pass@k metrics. | Orchestra-Research/ | 13k | 4 repos | ~2.9k | Automated safety check: Pass | MIT | 3 mo ago |
| 3 | Guides reinforcement-learning post-training of LLMs with slime, which pairs Megatron-LM training with SGLang rollouts, including GRPO runs on GLM, Qwen3 and Llama 3 models. | Orchestra-Research/ | 13k | 4 repos | ~2.8k | Automated safety check: Pass | MIT | 3 mo ago |
| 4 | Optimizes and serves LLMs on NVIDIA GPUs with TensorRT-LLM, covering quantization, in-flight batching, multi-GPU parallelism and the trtllm-serve command. | Orchestra-Research/ | 13k | 4 repos | ~1.3k | Automated safety check: Pass | MIT | 3 mo ago |
| 5 | Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. | Orchestra-Research/ | 13k | 2 repos | ~3.1k | Automated safety check: Pass | MIT | 3 mo ago |
| 6 | Sets up large-scale LLM training with NVIDIA Megatron-Core, choosing tensor, pipeline, data, context and expert parallelism for a given model size and GPU count. | Orchestra-Research/ | 13k | 2 repos | ~2.4k | Automated safety check: Pass | MIT | 3 mo ago |
