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NVIDIA's enterprise-grade platform with container-first architecture for reproducible benchmarking.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33332,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fevaluation-nemo-evaluator\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000011","运维与部署",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53],"nemo evaluator","Evaluation","evaluator","evaluation nemo evaluator","Reproducible","when","hpc","harnesses","Scalable","platform","local","across","launcher","Safety","Enterprise","llms","Slurm","Multi-Backend","MMLU","NVIDIA","NeMo","HumanEval","cloud","benchmarking","architecture","research","docker","2026-11-10 10:12:30",false,"---\nname: evaluation-nemo-evaluator\ndescription: Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluation on local Docker, Slurm HPC, or cloud platforms. NVIDIA's enterprise-grade platform with container-first architecture for reproducible benchmarking.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ntags: [Evaluation, NeMo, NVIDIA, Benchmarking, MMLU, HumanEval, Multi-Backend, Slurm, Docker, Reproducible, Enterprise]\ndependencies: [nemo-evaluator-launcher>=0.1.25, docker]\n---\n\n# NeMo Evaluator SDK - Enterprise LLM Benchmarking\n\n## Quick Start\n\nNeMo Evaluator SDK evaluates LLMs across 100+ benchmarks from 18+ harnesses using containerized, reproducible evaluation with multi-backend execution (local Docker, Slurm HPC, Lepton cloud).\n\n**Installation**:\n```bash\npip install nemo-evaluator-launcher\n```\n\n**Set API key and run evaluation**:\n```bash\nexport NGC_API_KEY=nvapi-your-key-here\n\n# Create minimal config\ncat > config.yaml \u003C\u003C 'EOF'\ndefaults:\n  - execution: local\n  - deployment: none\n  - _self_\n\nexecution:\n  output_dir: .\u002Fresults\n\ntarget:\n  api_endpoint:\n    model_id: meta\u002Fllama-3.1-8b-instruct\n    url: https:\u002F\u002Fintegrate.api.nvidia.com\u002Fv1\u002Fchat\u002Fcompletions\n    api_key_name: NGC_API_KEY\n\nevaluation:\n  tasks:\n    - name: ifeval\nEOF\n\n# Run evaluation\nnemo-evaluator-launcher run --config-dir . --config-name config\n```\n\n**View available tasks**:\n```bash\nnemo-evaluator-launcher ls tasks\n```\n\n## Common Workflows\n\n### Workflow 1: Evaluate Model on Standard Benchmarks\n\nRun core academic benchmarks (MMLU, GSM8K, IFEval) on any OpenAI-compatible endpoint.\n\n**Checklist**:\n```\nStandard Evaluation:\n- [ ] Step 1: Configure API endpoint\n- [ ] Step 2: Select benchmarks\n- [ ] Step 3: Run evaluation\n- [ ] Step 4: Check results\n```\n\n**Step 1: Configure API endpoint**\n\n```yaml\n# config.yaml\ndefaults:\n  - execution: local\n  - deployment: none\n  - _self_\n\nexecution:\n  output_dir: .\u002Fresults\n\ntarget:\n  api_endpoint:\n    model_id: meta\u002Fllama-3.1-8b-instruct\n    url: https:\u002F\u002Fintegrate.api.nvidia.com\u002Fv1\u002Fchat\u002Fcompletions\n    api_key_name: NGC_API_KEY\n```\n\nFor self-hosted endpoints (vLLM, TRT-LLM):\n```yaml\ntarget:\n  api_endpoint:\n    model_id: my-model\n    url: http:\u002F\u002Flocalhost:8000\u002Fv1\u002Fchat\u002Fcompletions\n    api_key_name: \"\"  # No key needed for local\n```\n\n**Step 2: Select benchmarks**\n\nAdd tasks to your config:\n```yaml\nevaluation:\n  tasks:\n    - name: ifeval           # Instruction following\n    - name: gpqa_diamond     # Graduate-level QA\n      env_vars:\n        HF_TOKEN: HF_TOKEN   # Some tasks need HF token\n    - name: gsm8k_cot_instruct  # Math reasoning\n    - name: humaneval        # Code generation\n```\n\n**Step 3: Run evaluation**\n\n```bash\n# Run with config file\nnemo-evaluator-launcher run \\\n  --config-dir . \\\n  --config-name config\n\n# Override output directory\nnemo-evaluator-launcher run \\\n  --config-dir . \\\n  --config-name config \\\n  -o execution.output_dir=.\u002Fmy_results\n\n# Limit samples for quick testing\nnemo-evaluator-launcher run \\\n  --config-dir . \\\n  --config-name config \\\n  -o +evaluation.nemo_evaluator_config.config.params.limit_samples=10\n```\n\n**Step 4: Check results**\n\n```bash\n# Check job status\nnemo-evaluator-launcher status \u003Cinvocation_id>\n\n# List all runs\nnemo-evaluator-launcher ls runs\n\n# View results\ncat results\u002F\u003Cinvocation_id>\u002F\u003Ctask>\u002Fartifacts\u002Fresults.yml\n```\n\n### Workflow 2: Run Evaluation on Slurm HPC Cluster\n\nExecute large-scale evaluation on HPC infrastructure.\n\n**Checklist**:\n```\nSlurm Evaluation:\n- [ ] Step 1: Configure Slurm settings\n- [ ] Step 2: Set up model deployment\n- [ ] Step 3: Launch evaluation\n- [ ] Step 4: Monitor job status\n```\n\n**Step 1: Configure Slurm settings**\n\n```yaml\n# slurm_config.yaml\ndefaults:\n  - execution: slurm\n  - deployment: vllm\n  - _self_\n\nexecution:\n  hostname: cluster.example.com\n  account: my_slurm_account\n  partition: gpu\n  output_dir: \u002Fshared\u002Fresults\n  walltime: \"04:00:00\"\n  nodes: 1\n  gpus_per_node: 8\n```\n\n**Step 2: Set up model deployment**\n\n```yaml\ndeployment:\n  checkpoint_path: \u002Fshared\u002Fmodels\u002Fllama-3.1-8b\n  tensor_parallel_size: 2\n  data_parallel_size: 4\n  max_model_len: 4096\n\ntarget:\n  api_endpoint:\n    model_id: llama-3.1-8b\n    # URL auto-generated by deployment\n```\n\n**Step 3: Launch evaluation**\n\n```bash\nnemo-evaluator-launcher run \\\n  --config-dir . \\\n  --config-name slurm_config\n```\n\n**Step 4: Monitor job status**\n\n```bash\n# Check status (queries sacct)\nnemo-evaluator-launcher status \u003Cinvocation_id>\n\n# View detailed info\nnemo-evaluator-launcher info \u003Cinvocation_id>\n\n# Kill if needed\nnemo-evaluator-launcher kill \u003Cinvocation_id>\n```\n\n### Workflow 3: Compare Multiple Models\n\nBenchmark multiple models on the same tasks for comparison.\n\n**Checklist**:\n```\nModel Comparison:\n- [ ] Step 1: Create base config\n- [ ] Step 2: Run evaluations with overrides\n- [ ] Step 3: Export and compare results\n```\n\n**Step 1: Create base config**\n\n```yaml\n# base_eval.yaml\ndefaults:\n  - execution: local\n  - deployment: none\n  - _self_\n\nexecution:\n  output_dir: .\u002Fcomparison_results\n\nevaluation:\n  nemo_evaluator_config:\n    config:\n      params:\n        temperature: 0.01\n        parallelism: 4\n  tasks:\n    - name: mmlu_pro\n    - name: gsm8k_cot_instruct\n    - name: ifeval\n```\n\n**Step 2: Run evaluations with model overrides**\n\n```bash\n# Evaluate Llama 3.1 8B\nnemo-evaluator-launcher run \\\n  --config-dir . \\\n  --config-name base_eval \\\n  -o target.api_endpoint.model_id=meta\u002Fllama-3.1-8b-instruct \\\n  -o target.api_endpoint.url=https:\u002F\u002Fintegrate.api.nvidia.com\u002Fv1\u002Fchat\u002Fcompletions\n\n# Evaluate Mistral 7B\nnemo-evaluator-launcher run \\\n  --config-dir . \\\n  --config-name base_eval \\\n  -o target.api_endpoint.model_id=mistralai\u002Fmistral-7b-instruct-v0.3 \\\n  -o target.api_endpoint.url=https:\u002F\u002Fintegrate.api.nvidia.com\u002Fv1\u002Fchat\u002Fcompletions\n```\n\n**Step 3: Export and compare**\n\n```bash\n# Export to MLflow\nnemo-evaluator-launcher export \u003Cinvocation_id_1> --dest mlflow\nnemo-evaluator-launcher export \u003Cinvocation_id_2> --dest mlflow\n\n# Export to local JSON\nnemo-evaluator-launcher export \u003Cinvocation_id> --dest local --format json\n\n# Export to Weights & Biases\nnemo-evaluator-launcher export \u003Cinvocation_id> --dest wandb\n```\n\n### Workflow 4: Safety and Vision-Language Evaluation\n\nEvaluate models on safety benchmarks and VLM tasks.\n\n**Checklist**:\n```\nSafety\u002FVLM Evaluation:\n- [ ] Step 1: Configure safety tasks\n- [ ] Step 2: Set up VLM tasks (if applicable)\n- [ ] Step 3: Run evaluation\n```\n\n**Step 1: Configure safety tasks**\n\n```yaml\nevaluation:\n  tasks:\n    - name: aegis              # Safety harness\n    - name: wildguard          # Safety classification\n    - name: garak              # Security probing\n```\n\n**Step 2: Configure VLM tasks**\n\n```yaml\n# For vision-language models\ntarget:\n  api_endpoint:\n    type: vlm  # Vision-language endpoint\n    model_id: nvidia\u002Fllama-3.2-90b-vision-instruct\n    url: https:\u002F\u002Fintegrate.api.nvidia.com\u002Fv1\u002Fchat\u002Fcompletions\n\nevaluation:\n  tasks:\n    - name: ocrbench           # OCR evaluation\n    - name: chartqa            # Chart understanding\n    - name: mmmu               # Multimodal understanding\n```\n\n## When to Use vs Alternatives\n\n**Use NeMo Evaluator when:**\n- Need **100+ benchmarks** from 18+ harnesses in one platform\n- Running evaluations on **Slurm HPC clusters** or cloud\n- Requiring **reproducible** containerized evaluation\n- Evaluating against **OpenAI-compatible APIs** (vLLM, TRT-LLM, NIMs)\n- Need **enterprise-grade** evaluation with result export (MLflow, W&B)\n\n**Use alternatives instead:**\n- **lm-evaluation-harness**: Simpler setup for quick local evaluation\n- **bigcode-evaluation-harness**: Focused only on code benchmarks\n- **HELM**: Stanford's broader evaluation (fairness, efficiency)\n- **Custom scripts**: Highly specialized domain evaluation\n\n## Supported Harnesses and Tasks\n\n| Harness | Task Count | Categories |\n|---------|-----------|------------|\n| `lm-evaluation-harness` | 60+ | MMLU, GSM8K, HellaSwag, ARC |\n| `simple-evals` | 20+ | GPQA, MATH, AIME |\n| `bigcode-evaluation-harness` | 25+ | HumanEval, MBPP, MultiPL-E |\n| `safety-harness` | 3 | Aegis, WildGuard |\n| `garak` | 1 | Security probing |\n| `vlmevalkit` | 6+ | OCRBench, ChartQA, MMMU |\n| `bfcl` | 6 | Function calling v2\u002Fv3 |\n| `mtbench` | 2 | Multi-turn conversation |\n| `livecodebench` | 10+ | Live coding evaluation |\n| `helm` | 15 | Medical domain |\n| `nemo-skills` | 8 | Math, science, agentic |\n\n## Common Issues\n\n**Issue: Container pull fails**\n\nEnsure NGC credentials are configured:\n```bash\ndocker login nvcr.io -u '$oauthtoken' -p $NGC_API_KEY\n```\n\n**Issue: Task requires environment variable**\n\nSome tasks need HF_TOKEN or JUDGE_API_KEY:\n```yaml\nevaluation:\n  tasks:\n    - name: gpqa_diamond\n      env_vars:\n        HF_TOKEN: HF_TOKEN  # Maps env var name to env var\n```\n\n**Issue: Evaluation timeout**\n\nIncrease parallelism or reduce samples:\n```bash\n-o +evaluation.nemo_evaluator_config.config.params.parallelism=8\n-o +evaluation.nemo_evaluator_config.config.params.limit_samples=100\n```\n\n**Issue: Slurm job not starting**\n\nCheck Slurm account and partition:\n```yaml\nexecution:\n  account: correct_account\n  partition: gpu\n  qos: normal  # May need specific QOS\n```\n\n**Issue: Different results than expected**\n\nVerify configuration matches reported settings:\n```yaml\nevaluation:\n  nemo_evaluator_config:\n    config:\n      params:\n        temperature: 0.0  # Deterministic\n        num_fewshot: 5    # Check paper's fewshot count\n```\n\n## CLI Reference\n\n| Command | Description |\n|---------|-------------|\n| `run` | Execute evaluation with config |\n| `status \u003Cid>` | Check job status |\n| `info \u003Cid>` | View detailed job info |\n| `ls tasks` | List available benchmarks |\n| `ls runs` | List all invocations |\n| `export \u003Cid>` | Export results (mlflow\u002Fwandb\u002Flocal) |\n| `kill \u003Cid>` | Terminate running job |\n\n## Configuration Override Examples\n\n```bash\n# Override model endpoint\n-o target.api_endpoint.model_id=my-model\n-o target.api_endpoint.url=http:\u002F\u002Flocalhost:8000\u002Fv1\u002Fchat\u002Fcompletions\n\n# Add evaluation parameters\n-o +evaluation.nemo_evaluator_config.config.params.temperature=0.5\n-o +evaluation.nemo_evaluator_config.config.params.parallelism=8\n-o +evaluation.nemo_evaluator_config.config.params.limit_samples=50\n\n# Change execution settings\n-o execution.output_dir=\u002Fcustom\u002Fpath\n-o execution.mode=parallel\n\n# Dynamically set tasks\n-o 'evaluation.tasks=[{name: ifeval}, {name: gsm8k}]'\n```\n\n## Python API Usage\n\nFor programmatic evaluation without the CLI:\n\n```python\nfrom nemo_evaluator.core.evaluate import evaluate\nfrom nemo_evaluator.api.api_dataclasses import (\n    EvaluationConfig,\n    EvaluationTarget,\n    ApiEndpoint,\n    EndpointType,\n    ConfigParams\n)\n\n# Configure evaluation\neval_config = EvaluationConfig(\n    type=\"mmlu_pro\",\n    output_dir=\".\u002Fresults\",\n    params=ConfigParams(\n        limit_samples=10,\n        temperature=0.0,\n        max_new_tokens=1024,\n        parallelism=4\n    )\n)\n\n# Configure target endpoint\ntarget_config = EvaluationTarget(\n    api_endpoint=ApiEndpoint(\n        model_id=\"meta\u002Fllama-3.1-8b-instruct\",\n        url=\"https:\u002F\u002Fintegrate.api.nvidia.com\u002Fv1\u002Fchat\u002Fcompletions\",\n        type=EndpointType.CHAT,\n        api_key=\"nvapi-your-key-here\"\n    )\n)\n\n# Run evaluation\nresult = evaluate(eval_cfg=eval_config, target_cfg=target_config)\n```\n\n## Advanced Topics\n\n**Multi-backend execution**: See [references\u002Fexecution-backends.md](references\u002Fexecution-backends.md)\n**Configuration deep-dive**: See [references\u002Fconfiguration.md](references\u002Fconfiguration.md)\n**Adapter and interceptor system**: See [references\u002Fadapter-system.md](references\u002Fadapter-system.md)\n**Custom benchmark integration**: See [references\u002Fcustom-benchmarks.md](references\u002Fcustom-benchmarks.md)\n\n## Requirements\n\n- **Python**: 3.10-3.13\n- **Docker**: Required for local execution\n- **NGC API Key**: For pulling containers and using NVIDIA Build\n- **HF_TOKEN**: Required for some benchmarks (GPQA, MMLU)\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002FNVIDIA-NeMo\u002FEvaluator\n- **NGC Containers**: nvcr.io\u002Fnvidia\u002Feval-factory\u002F\n- **NVIDIA Build**: https:\u002F\u002Fbuild.nvidia.com (free hosted models)\n- **Documentation**: https:\u002F\u002Fgithub.com\u002FNVIDIA-NeMo\u002FEvaluator\u002Ftree\u002Fmain\u002Fdocs\n","# NeMo Evaluator SDK - Enterprise LLM Benchmarking\n\n## Quick Start\n\nNeMo Evaluator SDK evaluates LLMs across 100+ benchmarks from 18+ harnesses using containerized, reproducible evaluation with multi-backend execution (local Docker, Slurm HPC, Lepton cloud).\n\n**Installation**:\n```bash\npip install nemo-evaluator-launcher\n```\n\n**Set API key and run evaluation**:\n```bash\nexport NGC_API_KEY=nvapi-your-key-here\n\n# Create minimal config\ncat > config.yaml \u003C\u003C 'EOF'\ndefaults:\n  - execution: local\n  - deployment: none\n  - _self_\n\nexecution:\n  output_dir: .\u002Fresults\n\ntarget:\n  api_endpoint:\n    model_id: meta\u002Fllama-3.1-8b-instruct\n    url: https:\u002F\u002Fintegrate.api.nvidia.com\u002Fv1\u002Fchat\u002Fcompletions\n    api_key_name: NGC_API_KEY\n\nevaluation:\n  tasks:\n    - name: ifeval\nEOF\n\n# Run evaluation\nnemo-evaluator-launcher run --config-dir . --config-name config\n```\n\n**View available tasks**:\n```bash\nnemo-evaluator-launcher ls tasks\n```\n\n## Common Workflows\n\n### Workflow 1: Evaluate Model on Standard Benchmarks\n\nRun core academic benchmarks (MMLU, GSM8K, IFEval) on any OpenAI-compatible endpoint.\n\n**Checklist**:\n```\nStandard Evaluation:\n- [ ] Step 1: Configure API endpoint\n- [ ] Step 2: Select benchmarks\n- [ ] Step 3: Run evaluation\n- [ ] Step 4: Check results\n```\n\n**Step 1: Configure API endpoint**\n\n```yaml\n# config.yaml\ndefaults:\n  - execution: local\n  - deployment: none\n  - _self_\n\nexecution:\n  output_dir: .\u002Fresults\n\ntarget:\n  api_endpoint:\n    model_id: meta\u002Fllama-3.1-8b-instruct\n    url: https:\u002F\u002Fintegrate.api.nvidia.com\u002Fv1\u002Fchat\u002Fcompletions\n    api_key_name: NGC_API_KEY\n```\n\nFor self-hosted endpoints (vLLM, TRT-LLM):\n```yaml\ntarget:\n  api_endpoint:\n    model_id: my-model\n    url: http:\u002F\u002Flocalhost:8000\u002Fv1\u002Fchat\u002Fcompletions\n    api_key_name: \"\"  # No key needed for local\n```\n\n**Step 2: Select benchmarks**\n\nAdd tasks to your config:\n```yaml\nevaluation:\n  tasks:\n    - name: ifeval           # Instruction following\n    - name: gpqa_diamond     # Graduate-level QA\n      env_vars:\n        HF_TOKEN: HF_TOKEN   # Some tasks need HF token\n    - name: gsm8k_cot_instruct  # Math reasoning\n    - name: humaneval        # Code generation\n```\n\n**Step 3: Run evaluation**\n\n```bash\n# Run with config file\nnemo-evaluator-launcher run \\\n  --config-dir . \\\n  --config-name config\n\n# Override output directory\nnemo-evaluator-launcher run \\\n  --config-dir . \\\n  --config-name config \\\n  -o execution.output_dir=.\u002Fmy_results\n\n# Limit samples for quick testing\nnemo-evaluator-launcher run \\\n  --config-dir . \\\n  --config-name config \\\n  -o +evaluation.nemo_evaluator_config.config.params.limit_samples=10\n```\n\n**Step 4: Check results**\n\n```bash\n# Check job status\nnemo-evaluator-launcher status \u003Cinvocation_id>\n\n# List all runs\nnemo-evaluator-launcher ls runs\n\n# View results\ncat results\u002F\u003Cinvocation_id>\u002F\u003Ctask>\u002Fartifacts\u002Fresults.yml\n```\n\n### Workflow 2: Run Evaluation on Slurm HPC Cluster\n\nExecute large-scale evaluation on HPC infrastructure.\n\n**Checklist**:\n```\nSlurm Evaluation:\n- [ ] Step 1: Configure Slurm settings\n- [ ] Step 2: Set up model deployment\n- [ ] Step 3: Launch evaluation\n- [ ] Step 4: Monitor job status\n```\n\n**Step 1: Configure Slurm settings**\n\n```yaml\n# slurm_config.yaml\ndefaults:\n  - execution: slurm\n  - deployment: vllm\n  - _self_\n\nexecution:\n  hostname: cluster.example.com\n  account: my_slurm_account\n  partition: gpu\n  output_dir: \u002Fshared\u002Fresults\n  walltime: \"04:00:00\"\n  nodes: 1\n  gpus_per_node: 8\n```\n\n**Step 2: Set up model deployment**\n\n```yaml\ndeployment:\n  checkpoint_path: \u002Fshared\u002Fmodels\u002Fllama-3.1-8b\n  tensor_parallel_size: 2\n  data_parallel_size: 4\n  max_model_len: 4096\n\ntarget:\n  api_endpoint:\n    model_id: llama-3.1-8b\n    # URL auto-generated by deployment\n```\n\n**Step 3: Launch evaluation**\n\n```bash\nnemo-evaluator-launcher run \\\n  --config-dir . \\\n  --config-name slurm_config\n```\n\n**Step 4: Monitor job status**\n\n```bash\n# Check status (queries sacct)\nnemo-evaluator-launcher status \u003Cinvocation_id>\n\n# View detailed info\nnemo-evaluator-launcher info \u003Cinvocation_id>\n\n# Kill if needed\nnemo-evaluator-launcher kill \u003Cinvocation_id>\n```\n\n### Workflow 3: Compare Multiple Models\n\nBenchmark multiple models on the same tasks for comparison.\n\n**Checklist**:\n```\nModel Comparison:\n- [ ] Step 1: Create base config\n- [ ] Step 2: Run evaluations with overrides\n- [ ] Step 3: Export and compare results\n```\n\n**Step 1: Create base config**\n\n```yaml\n# base_eval.yaml\ndefaults:\n  - execution: local\n  - deployment: none\n  - _self_\n\nexecution:\n  output_dir: .\u002Fcomparison_results\n\nevaluation:\n  nemo_evaluator_config:\n    config:\n      params:\n        temperature: 0.01\n        parallelism: 4\n  tasks:\n    - name: mmlu_pro\n    - name: gsm8k_cot_instruct\n    - name: ifeval\n```\n\n**Step 2: Run evaluations with model overrides**\n\n```bash\n# Evaluate Llama 3.1 8B\nnemo-evaluator-launcher run \\\n  --config-dir . \\\n  --config-name base_eval \\\n  -o target.api_endpoint.model_id=meta\u002Fllama-3.1-8b-instruct \\\n  -o target.api_endpoint.url=https:\u002F\u002Fintegrate.api.nvidia.com\u002Fv1\u002Fchat\u002Fcompletions\n\n# Evaluate Mistral 7B\nnemo-evaluator-launcher run \\\n  --config-dir . \\\n  --config-name base_eval \\\n  -o target.api_endpoint.model_id=mistralai\u002Fmistral-7b-instruct-v0.3 \\\n  -o target.api_endpoint.url=https:\u002F\u002Fintegrate.api.nvidia.com\u002Fv1\u002Fchat\u002Fcompletions\n```\n\n**Step 3: Export and compare**\n\n```bash\n# Export to MLflow\nnemo-evaluator-launcher export \u003Cinvocation_id_1> --dest mlflow\nnemo-evaluator-launcher export \u003Cinvocation_id_2> --dest mlflow\n\n# Export to local JSON\nnemo-evaluator-launcher export \u003Cinvocation_id> --dest local --format json\n\n# Export to Weights & Biases\nnemo-evaluator-launcher export \u003Cinvocation_id> --dest wandb\n```\n\n### Workflow 4: Safety and Vision-Language Evaluation\n\nEvaluate models on safety benchmarks and VLM tasks.\n\n**Checklist**:\n```\nSafety\u002FVLM Evaluation:\n- [ ] Step 1: Configure safety tasks\n- [ ] Step 2: Set up VLM tasks (if applicable)\n- [ ] Step 3: Run evaluation\n```\n\n**Step 1: Configure safety tasks**\n\n```yaml\nevaluation:\n  tasks:\n    - name: aegis              # Safety harness\n    - name: wildguard          # Safety classification\n    - name: garak              # Security probing\n```\n\n**Step 2: Configure VLM tasks**\n\n```yaml\n# For vision-language models\ntarget:\n  api_endpoint:\n    type: vlm  # Vision-language endpoint\n    model_id: nvidia\u002Fllama-3.2-90b-vision-instruct\n    url: https:\u002F\u002Fintegrate.api.nvidia.com\u002Fv1\u002Fchat\u002Fcompletions\n\nevaluation:\n  tasks:\n    - name: ocrbench           # OCR evaluation\n    - name: chartqa            # Chart understanding\n    - name: mmmu               # Multimodal understanding\n```\n\n## When to Use vs Alternatives\n\n**Use NeMo Evaluator when:**\n- Need **100+ benchmarks** from 18+ harnesses in one platform\n- Running evaluations on **Slurm HPC clusters** or cloud\n- Requiring **reproducible** containerized evaluation\n- Evaluating against **OpenAI-compatible APIs** (vLLM, TRT-LLM, NIMs)\n- Need **enterprise-grade** evaluation with result export (MLflow, W&B)\n\n**Use alternatives instead:**\n- **lm-evaluation-harness**: Simpler setup for quick local evaluation\n- **bigcode-evaluation-harness**: Focused only on code benchmarks\n- **HELM**: Stanford's broader evaluation (fairness, efficiency)\n- **Custom scripts**: Highly specialized domain evaluation\n\n## Supported Harnesses and Tasks\n\n| Harness | Task Count | Categories |\n|---------|-----------|------------|\n| `lm-evaluation-harness` | 60+ | MMLU, GSM8K, HellaSwag, ARC |\n| `simple-evals` | 20+ | GPQA, MATH, AIME |\n| `bigcode-evaluation-harness` | 25+ | HumanEval, MBPP, MultiPL-E |\n| `safety-harness` | 3 | Aegis, WildGuard |\n| `garak` | 1 | Security probing |\n| `vlmevalkit` | 6+ | OCRBench, ChartQA, MMMU |\n| `bfcl` | 6 | Function calling v2\u002Fv3 |\n| `mtbench` | 2 | Multi-turn conversation |\n| `livecodebench` | 10+ | Live coding evaluation |\n| `helm` | 15 | Medical domain |\n| `nemo-skills` | 8 | Math, science, agentic |\n\n## Common Issues\n\n**Issue: Container pull fails**\n\nEnsure NGC credentials are configured:\n```bash\ndocker login nvcr.io -u '$oauthtoken' -p $NGC_API_KEY\n```\n\n**Issue: Task requires environment variable**\n\nSome tasks need HF_TOKEN or JUDGE_API_KEY:\n```yaml\nevaluation:\n  tasks:\n    - name: gpqa_diamond\n      env_vars:\n        HF_TOKEN: HF_TOKEN  # Maps env var name to env var\n```\n\n**Issue: Evaluation timeout**\n\nIncrease parallelism or reduce samples:\n```bash\n-o +evaluation.nemo_evaluator_config.config.params.parallelism=8\n-o +evaluation.nemo_evaluator_config.config.params.limit_samples=100\n```\n\n**Issue: Slurm job not starting**\n\nCheck Slurm account and partition:\n```yaml\nexecution:\n  account: correct_account\n  partition: gpu\n  qos: normal  # May need specific QOS\n```\n\n**Issue: Different results than expected**\n\nVerify configuration matches reported settings:\n```yaml\nevaluation:\n  nemo_evaluator_config:\n    config:\n      params:\n        temperature: 0.0  # Deterministic\n        num_fewshot: 5    # Check paper's fewshot count\n```\n\n## CLI Reference\n\n| Command | Description |\n|---------|-------------|\n| `run` | Execute evaluation with config |\n| `status \u003Cid>` | Check job status |\n| `info \u003Cid>` | View detailed job info |\n| `ls tasks` | List available benchmarks |\n| `ls runs` | List all invocations |\n| `export \u003Cid>` | Export results (mlflow\u002Fwandb\u002Flocal) |\n| `kill \u003Cid>` | Terminate running job |\n\n## Configuration Override Examples\n\n```bash\n# Override model endpoint\n-o target.api_endpoint.model_id=my-model\n-o target.api_endpoint.url=http:\u002F\u002Flocalhost:8000\u002Fv1\u002Fchat\u002Fcompletions\n\n# Add evaluation parameters\n-o +evaluation.nemo_evaluator_config.config.params.temperature=0.5\n-o +evaluation.nemo_evaluator_config.config.params.parallelism=8\n-o +evaluation.nemo_evaluator_config.config.params.limit_samples=50\n\n# Change execution settings\n-o execution.output_dir=\u002Fcustom\u002Fpath\n-o execution.mode=parallel\n\n# Dynamically set tasks\n-o 'evaluation.tasks=[{name: ifeval}, {name: gsm8k}]'\n```\n\n## Python API Usage\n\nFor programmatic evaluation without the CLI:\n\n```python\nfrom nemo_evaluator.core.evaluate import evaluate\nfrom nemo_evaluator.api.api_dataclasses import (\n    EvaluationConfig,\n    EvaluationTarget,\n    ApiEndpoint,\n    EndpointType,\n    ConfigParams\n)\n\n# Configure evaluation\neval_config = EvaluationConfig(\n    type=\"mmlu_pro\",\n    output_dir=\".\u002Fresults\",\n    params=ConfigParams(\n        limit_samples=10,\n        temperature=0.0,\n        max_new_tokens=1024,\n        parallelism=4\n    )\n)\n\n# Configure target endpoint\ntarget_config = EvaluationTarget(\n    api_endpoint=ApiEndpoint(\n        model_id=\"meta\u002Fllama-3.1-8b-instruct\",\n        url=\"https:\u002F\u002Fintegrate.api.nvidia.com\u002Fv1\u002Fchat\u002Fcompletions\",\n        type=EndpointType.CHAT,\n        api_key=\"nvapi-your-key-here\"\n    )\n)\n\n# Run evaluation\nresult = evaluate(eval_cfg=eval_config, target_cfg=target_config)\n```\n\n## Advanced Topics\n\n**Multi-backend execution**: See [references\u002Fexecution-backends.md](references\u002Fexecution-backends.md)\n**Configuration deep-dive**: See [references\u002Fconfiguration.md](references\u002Fconfiguration.md)\n**Adapter and interceptor system**: See [references\u002Fadapter-system.md](references\u002Fadapter-system.md)\n**Custom benchmark integration**: See [references\u002Fcustom-benchmarks.md](references\u002Fcustom-benchmarks.md)\n\n## Requirements\n\n- **Python**: 3.10-3.13\n- **Docker**: Required for local execution\n- **NGC API Key**: For pulling containers and using NVIDIA Build\n- **HF_TOKEN**: Required for some benchmarks (GPQA, MMLU)\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002FNVIDIA-NeMo\u002FEvaluator\n- **NGC Containers**: nvcr.io\u002Fnvidia\u002Feval-factory\u002F\n- **NVIDIA Build**: https:\u002F\u002Fbuild.nvidia.com (free hosted models)\n- **Documentation**: https:\u002F\u002Fgithub.com\u002FNVIDIA-NeMo\u002FEvaluator\u002Ftree\u002Fmain\u002Fdocs\n",{"name":5,"tags":59,"author":62,"license":15,"version":63,"description":7,"dependencies":64},[28,47,46,60,45,48,44,43,61,31,41],"Benchmarking","Docker","Orchestra Research","1.0.0",[65,53],"nemo-evaluator-launcher>=0.1.25","main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fevaluation-nemo-evaluator",3911,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-11 21:38:31",{"source":8,"skillName":5,"commands":77,"prompt":87,"zipAvailable":88},[78,81,84],{"tool":79,"command":80},"npx","npx skills add davila7\u002Fclaude-code-templates --skill evaluation-nemo-evaluator",{"tool":82,"command":83},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill 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DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[121],{"categoryNo":122,"categoryName":123,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[125,126,127,128,129,130,131,132,133,32,134,135,136,137,138,139,140,141,142,143],"scoring","pool","wealthville pool scoring","liquidity","which","for","exit","now","use","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":145,"name":146,"nameZh":6,"description":147,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":148,"categories":149,"keywords":151,"publishedAt":54},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[150],{"categoryNo":122,"categoryName":123,"taxonomyType":21,"taxonomyTypeDesc":22},[152,153,154,155,156,157,158,159,160,161,162,32,163,164,165,166,167,168,169,170],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":172,"name":173,"nameZh":6,"description":174,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":175,"categories":176,"keywords":184,"publishedAt":54},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[177,178,179],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":122,"categoryName":123,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":180,"categoryName":181,"taxonomyType":182,"taxonomyTypeDesc":183},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[185,186,187,188,189,190,191,130,192,193,194,195,196,197,198,133,199,200,32,201],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":203,"name":204,"nameZh":6,"description":205,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":206,"licenseFileUrl":207,"sourceHtmlUrl":208,"categories":209,"keywords":213,"publishedAt":54},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[210],{"categoryNo":211,"categoryName":212,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,130,236],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":238,"name":239,"nameZh":6,"description":240,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":206,"licenseFileUrl":241,"sourceHtmlUrl":242,"categories":243,"keywords":248,"publishedAt":54},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[244,247],{"categoryNo":245,"categoryName":246,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":211,"categoryName":212,"taxonomyType":21,"taxonomyTypeDesc":22},[249,250,251,252,253,215,254,224,225,255,256,257,258,130,37,135,259],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","behavior",{"skillNo":261,"name":262,"nameZh":6,"description":263,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":206,"licenseFileUrl":264,"sourceHtmlUrl":265,"categories":266,"keywords":274,"publishedAt":54},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[267,270,271],{"categoryNo":268,"categoryName":269,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":122,"categoryName":123,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":272,"categoryName":273,"taxonomyType":182,"taxonomyTypeDesc":183},"01JBSEEDCAT000000000000036","内容创作者",[275,276,277,278,279,280,281,282,217,283,227,163,284,285,286,287,288,197,289,32,290,291,292,293,294,141,295],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]