{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/8"},{"label":"Testing","url":"https://skillfed.io/packages/category/software-development-testing/5"}],"enrichment":{"capability":"NeMo Evaluator runs standardized benchmarks against language models through a configurable framework with built-in scoring, proxy interception, and multi-format result reporting.","skillfed_tags":["llm-evaluation","benchmarking","model-testing"],"use_cases":["Run mmlu or gsm8k against a model API to measure instruction-following and math reasoning performance.","Intercept and cache LLM API calls during iterative benchmark development to reduce API costs.","Execute code-based benchmarks (humaneval) in isolated Docker containers with automatic sandbox management.","Compare baseline vs. candidate model runs with statistical deltas and pass/fail flips using nel compare.","Enforce multi-benchmark quality gates with explicit pass/fail policies before production deployment.","Export evaluation results to experiment trackers for team collaboration and result tracking."],"what_it_does":"NeMo Evaluator is an LLM evaluation framework that runs standardized benchmarks (mmlu, mmlu_pro, gpqa, gsm8k, math500, mgsm, drop, triviaqa, humaneval, simpleqa, healthbench, pinchbench, xstest, terminal-bench-hard, terminal-bench-v1, nmp_harbor) against language models and produces scored results. It sits between your test harness and an LLM API, intercepting requests to cache responses, limit turns, modify payloads, and inject system messages\u2014all without external proxy infrastructure. You define benchmarks in YAML or Python, choose a solver type (simple, harbor, tool_calling, gym_delegation, openclaw, container), and run evaluations locally or on SLURM clusters with Docker sandboxes for code execution.\n\nThe framework handles scoring (multichoice_regex, numeric_match, answer_line, fuzzy_match, code_sandbox, needs_judge), result aggregation, and export to experiment trackers or Inspect AI format. It ships with CLI commands to run benchmarks, merge sharded results, generate reports, compare runs, and enforce quality gates. Optional extras add symbolic math, statistical analysis, Harbor agent support, and Inspect export.","worth_installing":"Yes, if you need to benchmark language models against standard datasets. Low install friction, active maintenance, no security vulnerabilities, and permissive license. Requires Python 3.12 or 3.13, an LLM endpoint, and familiarity with YAML configuration. Best suited for teams evaluating model performance systematically; less relevant for single ad-hoc inference tasks."},"id":"nemo-evaluator","links":{"html":"https://skillfed.io/packages/nemo-evaluator","md":"https://skillfed.io/packages/nemo-evaluator.md","pypi":"https://pypi.org/project/nemo-evaluator/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-03","license_spdx":null,"license_treatment":"permissive","name":"nemo-evaluator","python_support":"supports_current","summary":"NeMo Evaluator \u2014 benchmark environments, pluggable solvers, interceptor proxy, and decision-grade scoring for LLMs"},"popularity":{"monthly_downloads":161980,"position":10618,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.3.0"}