evaluation-nemo-evaluator

Topics in the source

• NeMo Evaluator SDK - Enterprise LLM Benchmarking
• Quick Start
• Common Workflows
• Workflow 1: Evaluate Model on Standard Benchmarks
• Workflow 2: Run Evaluation on Slurm HPC Cluster
• Workflow 3: Compare Multiple Models
• Workflow 4: Safety and Vision-Language Evaluation
• When to Use vs Alternatives

NeMo Evaluator SDK evaluates LLMs across 100+ benchmarks from 18+ harnesses using containerized, reproducible evaluation with multi-backend execution (local Docker, Slurm HPC, Lepton cloud). Installation: Set API key and run evaluation: View available tasks: Run core academic benchmarks (MMLU, GSM8K, IFEval) on any OpenAI-compatible endpoint. Checklist: Step 1: Configure API endpoint For self-hosted endpoints (vLLM, TRT-LLM): Step 2: Select benchmarks Add tasks to your config: Step 3: Run evaluation Step 4: Check results Execute large-scale evaluation on HPC infrastructure. Checklist: Step 1: Configure Slurm settings Step 2: Set up model deployment Step 3: Launch evaluation Step 4: Monitor job status Benchmark multiple models on the same tasks for comparison. Checklist: Step 1: Create base config Step 2: Run evaluations with model overrides Step 3: Export and compare Evaluate models on safety benchmarks and VLM tasks. Checklist: Step 1: Configure safety tasks Step 2: Configure VLM tasks Use NeMo Evaluator when: Need 100+ benchmarks from 18+ harnesses in one platform Running evaluations on Slurm HPC clusters or cloud Requiring reproducible containerized evaluation Evaluating against

This is an excerpt. Refer to the complete source and its current requirements.