[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADPKWXEGED5MVZCDPNC2A7":3,"skill-same:01M6ADPKWXEGED5MVZCDPNC2A7":126},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":40,"linkOnly":41,"markdownContent":42,"markdownBody":43,"frontMatter":44,"refName":58,"commitSha":59,"skillDirectory":60,"forksCount":61,"repositoryHtmlUrl":62,"repositoryHomepageUrl":63,"ownerHtmlUrl":64,"ownerBlogUrl":65,"ownerDescription":66,"updatedAt":67,"install":68,"resources":81},"01M6ADPKWXEGED5MVZCDPNC2A7","evaluation-lm-evaluation-harness",null,"Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33481,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fevaluation-lm-evaluation-harness\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39],"harness","LM Evaluation Harness","Evaluation","evaluation lm evaluation harness","vLLM","when","orchestra","GSM8K","progress","across","use","eval","model","Transformers","llms","MMLU","HumanEval","reporting","training","benchmarking","research","2026-11-10 10:12:30",false,"---\nname: evaluation-lm-evaluation-harness\ndescription: Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  author: Orchestra Research\n  tags: [Evaluation, LM Evaluation Harness, Benchmarking, MMLU, HumanEval, GSM8K, EleutherAI, Model Quality, Academic Benchmarks, Industry Standard]\n  dependencies: [lm-eval, transformers, vllm]\n---\n\n# lm-evaluation-harness - LLM Benchmarking\n\n## Quick start\n\nlm-evaluation-harness evaluates LLMs across 60+ academic benchmarks using standardized prompts and metrics.\n\n**Installation**:\n```bash\npip install lm-eval\n```\n\n**Evaluate any HuggingFace model**:\n```bash\nlm_eval --model hf \\\n  --model_args pretrained=meta-llama\u002FLlama-2-7b-hf \\\n  --tasks mmlu,gsm8k,hellaswag \\\n  --device cuda:0 \\\n  --batch_size 8\n```\n\n**View available tasks**:\n```bash\nlm_eval --tasks list\n```\n\n## Common workflows\n\n### Workflow 1: Standard benchmark evaluation\n\nEvaluate model on core benchmarks (MMLU, GSM8K, HumanEval).\n\nCopy this checklist:\n\n```\nBenchmark Evaluation:\n- [ ] Step 1: Choose benchmark suite\n- [ ] Step 2: Configure model\n- [ ] Step 3: Run evaluation\n- [ ] Step 4: Analyze results\n```\n\n**Step 1: Choose benchmark suite**\n\n**Core reasoning benchmarks**:\n- **MMLU** (Massive Multitask Language Understanding) - 57 subjects, multiple choice\n- **GSM8K** - Grade school math word problems\n- **HellaSwag** - Common sense reasoning\n- **TruthfulQA** - Truthfulness and factuality\n- **ARC** (AI2 Reasoning Challenge) - Science questions\n\n**Code benchmarks**:\n- **HumanEval** - Python code generation (164 problems)\n- **MBPP** (Mostly Basic Python Problems) - Python coding\n\n**Standard suite** (recommended for model releases):\n```bash\n--tasks mmlu,gsm8k,hellaswag,truthfulqa,arc_challenge\n```\n\n**Step 2: Configure model**\n\n**HuggingFace model**:\n```bash\nlm_eval --model hf \\\n  --model_args pretrained=meta-llama\u002FLlama-2-7b-hf,dtype=bfloat16 \\\n  --tasks mmlu \\\n  --device cuda:0 \\\n  --batch_size auto  # Auto-detect optimal batch size\n```\n\n**Quantized model (4-bit\u002F8-bit)**:\n```bash\nlm_eval --model hf \\\n  --model_args pretrained=meta-llama\u002FLlama-2-7b-hf,load_in_4bit=True \\\n  --tasks mmlu \\\n  --device cuda:0\n```\n\n**Custom checkpoint**:\n```bash\nlm_eval --model hf \\\n  --model_args pretrained=\u002Fpath\u002Fto\u002Fmy-model,tokenizer=\u002Fpath\u002Fto\u002Ftokenizer \\\n  --tasks mmlu \\\n  --device cuda:0\n```\n\n**Step 3: Run evaluation**\n\n```bash\n# Full MMLU evaluation (57 subjects)\nlm_eval --model hf \\\n  --model_args pretrained=meta-llama\u002FLlama-2-7b-hf \\\n  --tasks mmlu \\\n  --num_fewshot 5 \\  # 5-shot evaluation (standard)\n  --batch_size 8 \\\n  --output_path results\u002F \\\n  --log_samples  # Save individual predictions\n\n# Multiple benchmarks at once\nlm_eval --model hf \\\n  --model_args pretrained=meta-llama\u002FLlama-2-7b-hf \\\n  --tasks mmlu,gsm8k,hellaswag,truthfulqa,arc_challenge \\\n  --num_fewshot 5 \\\n  --batch_size 8 \\\n  --output_path results\u002Fllama2-7b-eval.json\n```\n\n**Step 4: Analyze results**\n\nResults saved to `results\u002Fllama2-7b-eval.json`:\n\n```json\n{\n  \"results\": {\n    \"mmlu\": {\n      \"acc\": 0.459,\n      \"acc_stderr\": 0.004\n    },\n    \"gsm8k\": {\n      \"exact_match\": 0.142,\n      \"exact_match_stderr\": 0.006\n    },\n    \"hellaswag\": {\n      \"acc_norm\": 0.765,\n      \"acc_norm_stderr\": 0.004\n    }\n  },\n  \"config\": {\n    \"model\": \"hf\",\n    \"model_args\": \"pretrained=meta-llama\u002FLlama-2-7b-hf\",\n    \"num_fewshot\": 5\n  }\n}\n```\n\n### Workflow 2: Track training progress\n\nEvaluate checkpoints during training.\n\n```\nTraining Progress Tracking:\n- [ ] Step 1: Set up periodic evaluation\n- [ ] Step 2: Choose quick benchmarks\n- [ ] Step 3: Automate evaluation\n- [ ] Step 4: Plot learning curves\n```\n\n**Step 1: Set up periodic evaluation**\n\nEvaluate every N training steps:\n\n```bash\n#!\u002Fbin\u002Fbash\n# eval_checkpoint.sh\n\nCHECKPOINT_DIR=$1\nSTEP=$2\n\nlm_eval --model hf \\\n  --model_args pretrained=$CHECKPOINT_DIR\u002Fcheckpoint-$STEP \\\n  --tasks gsm8k,hellaswag \\\n  --num_fewshot 0 \\  # 0-shot for speed\n  --batch_size 16 \\\n  --output_path results\u002Fstep-$STEP.json\n```\n\n**Step 2: Choose quick benchmarks**\n\nFast benchmarks for frequent evaluation:\n- **HellaSwag**: ~10 minutes on 1 GPU\n- **GSM8K**: ~5 minutes\n- **PIQA**: ~2 minutes\n\nAvoid for frequent eval (too slow):\n- **MMLU**: ~2 hours (57 subjects)\n- **HumanEval**: Requires code execution\n\n**Step 3: Automate evaluation**\n\nIntegrate with training script:\n\n```python\n# In training loop\nif step % eval_interval == 0:\n    model.save_pretrained(f\"checkpoints\u002Fstep-{step}\")\n\n    # Run evaluation\n    os.system(f\".\u002Feval_checkpoint.sh checkpoints step-{step}\")\n```\n\nOr use PyTorch Lightning callbacks:\n\n```python\nfrom pytorch_lightning import Callback\n\nclass EvalHarnessCallback(Callback):\n    def on_validation_epoch_end(self, trainer, pl_module):\n        step = trainer.global_step\n        checkpoint_path = f\"checkpoints\u002Fstep-{step}\"\n\n        # Save checkpoint\n        trainer.save_checkpoint(checkpoint_path)\n\n        # Run lm-eval\n        os.system(f\"lm_eval --model hf --model_args pretrained={checkpoint_path} ...\")\n```\n\n**Step 4: Plot learning curves**\n\n```python\nimport json\nimport matplotlib.pyplot as plt\n\n# Load all results\nsteps = []\nmmlu_scores = []\n\nfor file in sorted(glob.glob(\"results\u002Fstep-*.json\")):\n    with open(file) as f:\n        data = json.load(f)\n        step = int(file.split(\"-\")[1].split(\".\")[0])\n        steps.append(step)\n        mmlu_scores.append(data[\"results\"][\"mmlu\"][\"acc\"])\n\n# Plot\nplt.plot(steps, mmlu_scores)\nplt.xlabel(\"Training Step\")\nplt.ylabel(\"MMLU Accuracy\")\nplt.title(\"Training Progress\")\nplt.savefig(\"training_curve.png\")\n```\n\n### Workflow 3: Compare multiple models\n\nBenchmark suite for model comparison.\n\n```\nModel Comparison:\n- [ ] Step 1: Define model list\n- [ ] Step 2: Run evaluations\n- [ ] Step 3: Generate comparison table\n```\n\n**Step 1: Define model list**\n\n```bash\n# models.txt\nmeta-llama\u002FLlama-2-7b-hf\nmeta-llama\u002FLlama-2-13b-hf\nmistralai\u002FMistral-7B-v0.1\nmicrosoft\u002Fphi-2\n```\n\n**Step 2: Run evaluations**\n\n```bash\n#!\u002Fbin\u002Fbash\n# eval_all_models.sh\n\nTASKS=\"mmlu,gsm8k,hellaswag,truthfulqa\"\n\nwhile read model; do\n    echo \"Evaluating $model\"\n\n    # Extract model name for output file\n    model_name=$(echo $model | sed 's\u002F\\\u002F\u002F-\u002Fg')\n\n    lm_eval --model hf \\\n      --model_args pretrained=$model,dtype=bfloat16 \\\n      --tasks $TASKS \\\n      --num_fewshot 5 \\\n      --batch_size auto \\\n      --output_path results\u002F$model_name.json\n\ndone \u003C models.txt\n```\n\n**Step 3: Generate comparison table**\n\n```python\nimport json\nimport pandas as pd\n\nmodels = [\n    \"meta-llama-Llama-2-7b-hf\",\n    \"meta-llama-Llama-2-13b-hf\",\n    \"mistralai-Mistral-7B-v0.1\",\n    \"microsoft-phi-2\"\n]\n\ntasks = [\"mmlu\", \"gsm8k\", \"hellaswag\", \"truthfulqa\"]\n\nresults = []\nfor model in models:\n    with open(f\"results\u002F{model}.json\") as f:\n        data = json.load(f)\n        row = {\"Model\": model.replace(\"-\", \"\u002F\")}\n        for task in tasks:\n            # Get primary metric for each task\n            metrics = data[\"results\"][task]\n            if \"acc\" in metrics:\n                row[task.upper()] = f\"{metrics['acc']:.3f}\"\n            elif \"exact_match\" in metrics:\n                row[task.upper()] = f\"{metrics['exact_match']:.3f}\"\n        results.append(row)\n\ndf = pd.DataFrame(results)\nprint(df.to_markdown(index=False))\n```\n\nOutput:\n```\n| Model                  | MMLU  | GSM8K | HELLASWAG | TRUTHFULQA |\n|------------------------|-------|-------|-----------|------------|\n| meta-llama\u002FLlama-2-7b  | 0.459 | 0.142 | 0.765     | 0.391      |\n| meta-llama\u002FLlama-2-13b | 0.549 | 0.287 | 0.801     | 0.430      |\n| mistralai\u002FMistral-7B   | 0.626 | 0.395 | 0.812     | 0.428      |\n| microsoft\u002Fphi-2        | 0.560 | 0.613 | 0.682     | 0.447      |\n```\n\n### Workflow 4: Evaluate with vLLM (faster inference)\n\nUse vLLM backend for 5-10x faster evaluation.\n\n```\nvLLM Evaluation:\n- [ ] Step 1: Install vLLM\n- [ ] Step 2: Configure vLLM backend\n- [ ] Step 3: Run evaluation\n```\n\n**Step 1: Install vLLM**\n\n```bash\npip install vllm\n```\n\n**Step 2: Configure vLLM backend**\n\n```bash\nlm_eval --model vllm \\\n  --model_args pretrained=meta-llama\u002FLlama-2-7b-hf,tensor_parallel_size=1,dtype=auto,gpu_memory_utilization=0.8 \\\n  --tasks mmlu \\\n  --batch_size auto\n```\n\n**Step 3: Run evaluation**\n\nvLLM is 5-10× faster than standard HuggingFace:\n\n```bash\n# Standard HF: ~2 hours for MMLU on 7B model\nlm_eval --model hf \\\n  --model_args pretrained=meta-llama\u002FLlama-2-7b-hf \\\n  --tasks mmlu \\\n  --batch_size 8\n\n# vLLM: ~15-20 minutes for MMLU on 7B model\nlm_eval --model vllm \\\n  --model_args pretrained=meta-llama\u002FLlama-2-7b-hf,tensor_parallel_size=2 \\\n  --tasks mmlu \\\n  --batch_size auto\n```\n\n## When to use vs alternatives\n\n**Use lm-evaluation-harness when:**\n- Benchmarking models for academic papers\n- Comparing model quality across standard tasks\n- Tracking training progress\n- Reporting standardized metrics (everyone uses same prompts)\n- Need reproducible evaluation\n\n**Use alternatives instead:**\n- **HELM** (Stanford): Broader evaluation (fairness, efficiency, calibration)\n- **AlpacaEval**: Instruction-following evaluation with LLM judges\n- **MT-Bench**: Conversational multi-turn evaluation\n- **Custom scripts**: Domain-specific evaluation\n\n## Common issues\n\n**Issue: Evaluation too slow**\n\nUse vLLM backend:\n```bash\nlm_eval --model vllm \\\n  --model_args pretrained=model-name,tensor_parallel_size=2\n```\n\nOr reduce fewshot examples:\n```bash\n--num_fewshot 0  # Instead of 5\n```\n\nOr evaluate subset of MMLU:\n```bash\n--tasks mmlu_stem  # Only STEM subjects\n```\n\n**Issue: Out of memory**\n\nReduce batch size:\n```bash\n--batch_size 1  # Or --batch_size auto\n```\n\nUse quantization:\n```bash\n--model_args pretrained=model-name,load_in_8bit=True\n```\n\nEnable CPU offloading:\n```bash\n--model_args pretrained=model-name,device_map=auto,offload_folder=offload\n```\n\n**Issue: Different results than reported**\n\nCheck fewshot count:\n```bash\n--num_fewshot 5  # Most papers use 5-shot\n```\n\nCheck exact task name:\n```bash\n--tasks mmlu  # Not mmlu_direct or mmlu_fewshot\n```\n\nVerify model and tokenizer match:\n```bash\n--model_args pretrained=model-name,tokenizer=same-model-name\n```\n\n**Issue: HumanEval not executing code**\n\nInstall execution dependencies:\n```bash\npip install human-eval\n```\n\nEnable code execution:\n```bash\nlm_eval --model hf \\\n  --model_args pretrained=model-name \\\n  --tasks humaneval \\\n  --allow_code_execution  # Required for HumanEval\n```\n\n## Advanced topics\n\n**Benchmark descriptions**: See [references\u002Fbenchmark-guide.md](references\u002Fbenchmark-guide.md) for detailed description of all 60+ tasks, what they measure, and interpretation.\n\n**Custom tasks**: See [references\u002Fcustom-tasks.md](references\u002Fcustom-tasks.md) for creating domain-specific evaluation tasks.\n\n**API evaluation**: See [references\u002Fapi-evaluation.md](references\u002Fapi-evaluation.md) for evaluating OpenAI, Anthropic, and other API models.\n\n**Multi-GPU strategies**: See [references\u002Fdistributed-eval.md](references\u002Fdistributed-eval.md) for data parallel and tensor parallel evaluation.\n\n## Hardware requirements\n\n- **GPU**: NVIDIA (CUDA 11.8+), works on CPU (very slow)\n- **VRAM**:\n  - 7B model: 16GB (bf16) or 8GB (8-bit)\n  - 13B model: 28GB (bf16) or 14GB (8-bit)\n  - 70B model: Requires multi-GPU or quantization\n- **Time** (7B model, single A100):\n  - HellaSwag: 10 minutes\n  - GSM8K: 5 minutes\n  - MMLU (full): 2 hours\n  - HumanEval: 20 minutes\n\n## Resources\n\n- GitHub: https:\u002F\u002Fgithub.com\u002FEleutherAI\u002Flm-evaluation-harness\n- Docs: https:\u002F\u002Fgithub.com\u002FEleutherAI\u002Flm-evaluation-harness\u002Ftree\u002Fmain\u002Fdocs\n- Task library: 60+ tasks including MMLU, GSM8K, HumanEval, TruthfulQA, HellaSwag, ARC, WinoGrande, etc.\n- Leaderboard: https:\u002F\u002Fhuggingface.co\u002Fspaces\u002FHuggingFaceH4\u002Fopen_llm_leaderboard (uses this harness)\n\n\n\n","# lm-evaluation-harness - LLM Benchmarking\n\n## Quick start\n\nlm-evaluation-harness evaluates LLMs across 60+ academic benchmarks using standardized prompts and metrics.\n\n**Installation**:\n```bash\npip install lm-eval\n```\n\n**Evaluate any HuggingFace model**:\n```bash\nlm_eval --model hf \\\n  --model_args pretrained=meta-llama\u002FLlama-2-7b-hf \\\n  --tasks mmlu,gsm8k,hellaswag \\\n  --device cuda:0 \\\n  --batch_size 8\n```\n\n**View available tasks**:\n```bash\nlm_eval --tasks list\n```\n\n## Common workflows\n\n### Workflow 1: Standard benchmark evaluation\n\nEvaluate model on core benchmarks (MMLU, GSM8K, HumanEval).\n\nCopy this checklist:\n\n```\nBenchmark Evaluation:\n- [ ] Step 1: Choose benchmark suite\n- [ ] Step 2: Configure model\n- [ ] Step 3: Run evaluation\n- [ ] Step 4: Analyze results\n```\n\n**Step 1: Choose benchmark suite**\n\n**Core reasoning benchmarks**:\n- **MMLU** (Massive Multitask Language Understanding) - 57 subjects, multiple choice\n- **GSM8K** - Grade school math word problems\n- **HellaSwag** - Common sense reasoning\n- **TruthfulQA** - Truthfulness and factuality\n- **ARC** (AI2 Reasoning Challenge) - Science questions\n\n**Code benchmarks**:\n- **HumanEval** - Python code generation (164 problems)\n- **MBPP** (Mostly Basic Python Problems) - Python coding\n\n**Standard suite** (recommended for model releases):\n```bash\n--tasks mmlu,gsm8k,hellaswag,truthfulqa,arc_challenge\n```\n\n**Step 2: Configure model**\n\n**HuggingFace model**:\n```bash\nlm_eval --model hf \\\n  --model_args pretrained=meta-llama\u002FLlama-2-7b-hf,dtype=bfloat16 \\\n  --tasks mmlu \\\n  --device cuda:0 \\\n  --batch_size auto  # Auto-detect optimal batch size\n```\n\n**Quantized model (4-bit\u002F8-bit)**:\n```bash\nlm_eval --model hf \\\n  --model_args pretrained=meta-llama\u002FLlama-2-7b-hf,load_in_4bit=True \\\n  --tasks mmlu \\\n  --device cuda:0\n```\n\n**Custom checkpoint**:\n```bash\nlm_eval --model hf \\\n  --model_args pretrained=\u002Fpath\u002Fto\u002Fmy-model,tokenizer=\u002Fpath\u002Fto\u002Ftokenizer \\\n  --tasks mmlu \\\n  --device cuda:0\n```\n\n**Step 3: Run evaluation**\n\n```bash\n# Full MMLU evaluation (57 subjects)\nlm_eval --model hf \\\n  --model_args pretrained=meta-llama\u002FLlama-2-7b-hf \\\n  --tasks mmlu \\\n  --num_fewshot 5 \\  # 5-shot evaluation (standard)\n  --batch_size 8 \\\n  --output_path results\u002F \\\n  --log_samples  # Save individual predictions\n\n# Multiple benchmarks at once\nlm_eval --model hf \\\n  --model_args pretrained=meta-llama\u002FLlama-2-7b-hf \\\n  --tasks mmlu,gsm8k,hellaswag,truthfulqa,arc_challenge \\\n  --num_fewshot 5 \\\n  --batch_size 8 \\\n  --output_path results\u002Fllama2-7b-eval.json\n```\n\n**Step 4: Analyze results**\n\nResults saved to `results\u002Fllama2-7b-eval.json`:\n\n```json\n{\n  \"results\": {\n    \"mmlu\": {\n      \"acc\": 0.459,\n      \"acc_stderr\": 0.004\n    },\n    \"gsm8k\": {\n      \"exact_match\": 0.142,\n      \"exact_match_stderr\": 0.006\n    },\n    \"hellaswag\": {\n      \"acc_norm\": 0.765,\n      \"acc_norm_stderr\": 0.004\n    }\n  },\n  \"config\": {\n    \"model\": \"hf\",\n    \"model_args\": \"pretrained=meta-llama\u002FLlama-2-7b-hf\",\n    \"num_fewshot\": 5\n  }\n}\n```\n\n### Workflow 2: Track training progress\n\nEvaluate checkpoints during training.\n\n```\nTraining Progress Tracking:\n- [ ] Step 1: Set up periodic evaluation\n- [ ] Step 2: Choose quick benchmarks\n- [ ] Step 3: Automate evaluation\n- [ ] Step 4: Plot learning curves\n```\n\n**Step 1: Set up periodic evaluation**\n\nEvaluate every N training steps:\n\n```bash\n#!\u002Fbin\u002Fbash\n# eval_checkpoint.sh\n\nCHECKPOINT_DIR=$1\nSTEP=$2\n\nlm_eval --model hf \\\n  --model_args pretrained=$CHECKPOINT_DIR\u002Fcheckpoint-$STEP \\\n  --tasks gsm8k,hellaswag \\\n  --num_fewshot 0 \\  # 0-shot for speed\n  --batch_size 16 \\\n  --output_path results\u002Fstep-$STEP.json\n```\n\n**Step 2: Choose quick benchmarks**\n\nFast benchmarks for frequent evaluation:\n- **HellaSwag**: ~10 minutes on 1 GPU\n- **GSM8K**: ~5 minutes\n- **PIQA**: ~2 minutes\n\nAvoid for frequent eval (too slow):\n- **MMLU**: ~2 hours (57 subjects)\n- **HumanEval**: Requires code execution\n\n**Step 3: Automate evaluation**\n\nIntegrate with training script:\n\n```python\n# In training loop\nif step % eval_interval == 0:\n    model.save_pretrained(f\"checkpoints\u002Fstep-{step}\")\n\n    # Run evaluation\n    os.system(f\".\u002Feval_checkpoint.sh checkpoints step-{step}\")\n```\n\nOr use PyTorch Lightning callbacks:\n\n```python\nfrom pytorch_lightning import Callback\n\nclass EvalHarnessCallback(Callback):\n    def on_validation_epoch_end(self, trainer, pl_module):\n        step = trainer.global_step\n        checkpoint_path = f\"checkpoints\u002Fstep-{step}\"\n\n        # Save checkpoint\n        trainer.save_checkpoint(checkpoint_path)\n\n        # Run lm-eval\n        os.system(f\"lm_eval --model hf --model_args pretrained={checkpoint_path} ...\")\n```\n\n**Step 4: Plot learning curves**\n\n```python\nimport json\nimport matplotlib.pyplot as plt\n\n# Load all results\nsteps = []\nmmlu_scores = []\n\nfor file in sorted(glob.glob(\"results\u002Fstep-*.json\")):\n    with open(file) as f:\n        data = json.load(f)\n        step = int(file.split(\"-\")[1].split(\".\")[0])\n        steps.append(step)\n        mmlu_scores.append(data[\"results\"][\"mmlu\"][\"acc\"])\n\n# Plot\nplt.plot(steps, mmlu_scores)\nplt.xlabel(\"Training Step\")\nplt.ylabel(\"MMLU Accuracy\")\nplt.title(\"Training Progress\")\nplt.savefig(\"training_curve.png\")\n```\n\n### Workflow 3: Compare multiple models\n\nBenchmark suite for model comparison.\n\n```\nModel Comparison:\n- [ ] Step 1: Define model list\n- [ ] Step 2: Run evaluations\n- [ ] Step 3: Generate comparison table\n```\n\n**Step 1: Define model list**\n\n```bash\n# models.txt\nmeta-llama\u002FLlama-2-7b-hf\nmeta-llama\u002FLlama-2-13b-hf\nmistralai\u002FMistral-7B-v0.1\nmicrosoft\u002Fphi-2\n```\n\n**Step 2: Run evaluations**\n\n```bash\n#!\u002Fbin\u002Fbash\n# eval_all_models.sh\n\nTASKS=\"mmlu,gsm8k,hellaswag,truthfulqa\"\n\nwhile read model; do\n    echo \"Evaluating $model\"\n\n    # Extract model name for output file\n    model_name=$(echo $model | sed 's\u002F\\\u002F\u002F-\u002Fg')\n\n    lm_eval --model hf \\\n      --model_args pretrained=$model,dtype=bfloat16 \\\n      --tasks $TASKS \\\n      --num_fewshot 5 \\\n      --batch_size auto \\\n      --output_path results\u002F$model_name.json\n\ndone \u003C models.txt\n```\n\n**Step 3: Generate comparison table**\n\n```python\nimport json\nimport pandas as pd\n\nmodels = [\n    \"meta-llama-Llama-2-7b-hf\",\n    \"meta-llama-Llama-2-13b-hf\",\n    \"mistralai-Mistral-7B-v0.1\",\n    \"microsoft-phi-2\"\n]\n\ntasks = [\"mmlu\", \"gsm8k\", \"hellaswag\", \"truthfulqa\"]\n\nresults = []\nfor model in models:\n    with open(f\"results\u002F{model}.json\") as f:\n        data = json.load(f)\n        row = {\"Model\": model.replace(\"-\", \"\u002F\")}\n        for task in tasks:\n            # Get primary metric for each task\n            metrics = data[\"results\"][task]\n            if \"acc\" in metrics:\n                row[task.upper()] = f\"{metrics['acc']:.3f}\"\n            elif \"exact_match\" in metrics:\n                row[task.upper()] = f\"{metrics['exact_match']:.3f}\"\n        results.append(row)\n\ndf = pd.DataFrame(results)\nprint(df.to_markdown(index=False))\n```\n\nOutput:\n```\n| Model                  | MMLU  | GSM8K | HELLASWAG | TRUTHFULQA |\n|------------------------|-------|-------|-----------|------------|\n| meta-llama\u002FLlama-2-7b  | 0.459 | 0.142 | 0.765     | 0.391      |\n| meta-llama\u002FLlama-2-13b | 0.549 | 0.287 | 0.801     | 0.430      |\n| mistralai\u002FMistral-7B   | 0.626 | 0.395 | 0.812     | 0.428      |\n| microsoft\u002Fphi-2        | 0.560 | 0.613 | 0.682     | 0.447      |\n```\n\n### Workflow 4: Evaluate with vLLM (faster inference)\n\nUse vLLM backend for 5-10x faster evaluation.\n\n```\nvLLM Evaluation:\n- [ ] Step 1: Install vLLM\n- [ ] Step 2: Configure vLLM backend\n- [ ] Step 3: Run evaluation\n```\n\n**Step 1: Install vLLM**\n\n```bash\npip install vllm\n```\n\n**Step 2: Configure vLLM backend**\n\n```bash\nlm_eval --model vllm \\\n  --model_args pretrained=meta-llama\u002FLlama-2-7b-hf,tensor_parallel_size=1,dtype=auto,gpu_memory_utilization=0.8 \\\n  --tasks mmlu \\\n  --batch_size auto\n```\n\n**Step 3: Run evaluation**\n\nvLLM is 5-10× faster than standard HuggingFace:\n\n```bash\n# Standard HF: ~2 hours for MMLU on 7B model\nlm_eval --model hf \\\n  --model_args pretrained=meta-llama\u002FLlama-2-7b-hf \\\n  --tasks mmlu \\\n  --batch_size 8\n\n# vLLM: ~15-20 minutes for MMLU on 7B model\nlm_eval --model vllm \\\n  --model_args pretrained=meta-llama\u002FLlama-2-7b-hf,tensor_parallel_size=2 \\\n  --tasks mmlu \\\n  --batch_size auto\n```\n\n## When to use vs alternatives\n\n**Use lm-evaluation-harness when:**\n- Benchmarking models for academic papers\n- Comparing model quality across standard tasks\n- Tracking training progress\n- Reporting standardized metrics (everyone uses same prompts)\n- Need reproducible evaluation\n\n**Use alternatives instead:**\n- **HELM** (Stanford): Broader evaluation (fairness, efficiency, calibration)\n- **AlpacaEval**: Instruction-following evaluation with LLM judges\n- **MT-Bench**: Conversational multi-turn evaluation\n- **Custom scripts**: Domain-specific evaluation\n\n## Common issues\n\n**Issue: Evaluation too slow**\n\nUse vLLM backend:\n```bash\nlm_eval --model vllm \\\n  --model_args pretrained=model-name,tensor_parallel_size=2\n```\n\nOr reduce fewshot examples:\n```bash\n--num_fewshot 0  # Instead of 5\n```\n\nOr evaluate subset of MMLU:\n```bash\n--tasks mmlu_stem  # Only STEM subjects\n```\n\n**Issue: Out of memory**\n\nReduce batch size:\n```bash\n--batch_size 1  # Or --batch_size auto\n```\n\nUse quantization:\n```bash\n--model_args pretrained=model-name,load_in_8bit=True\n```\n\nEnable CPU offloading:\n```bash\n--model_args pretrained=model-name,device_map=auto,offload_folder=offload\n```\n\n**Issue: Different results than reported**\n\nCheck fewshot count:\n```bash\n--num_fewshot 5  # Most papers use 5-shot\n```\n\nCheck exact task name:\n```bash\n--tasks mmlu  # Not mmlu_direct or mmlu_fewshot\n```\n\nVerify model and tokenizer match:\n```bash\n--model_args pretrained=model-name,tokenizer=same-model-name\n```\n\n**Issue: HumanEval not executing code**\n\nInstall execution dependencies:\n```bash\npip install human-eval\n```\n\nEnable code execution:\n```bash\nlm_eval --model hf \\\n  --model_args pretrained=model-name \\\n  --tasks humaneval \\\n  --allow_code_execution  # Required for HumanEval\n```\n\n## Advanced topics\n\n**Benchmark descriptions**: See [references\u002Fbenchmark-guide.md](references\u002Fbenchmark-guide.md) for detailed description of all 60+ tasks, what they measure, and interpretation.\n\n**Custom tasks**: See [references\u002Fcustom-tasks.md](references\u002Fcustom-tasks.md) for creating domain-specific evaluation tasks.\n\n**API evaluation**: See [references\u002Fapi-evaluation.md](references\u002Fapi-evaluation.md) for evaluating OpenAI, Anthropic, and other API models.\n\n**Multi-GPU strategies**: See [references\u002Fdistributed-eval.md](references\u002Fdistributed-eval.md) for data parallel and tensor parallel evaluation.\n\n## Hardware requirements\n\n- **GPU**: NVIDIA (CUDA 11.8+), works on CPU (very slow)\n- **VRAM**:\n  - 7B model: 16GB (bf16) or 8GB (8-bit)\n  - 13B model: 28GB (bf16) or 14GB (8-bit)\n  - 70B model: Requires multi-GPU or quantization\n- **Time** (7B model, single A100):\n  - HellaSwag: 10 minutes\n  - GSM8K: 5 minutes\n  - MMLU (full): 2 hours\n  - HumanEval: 20 minutes\n\n## Resources\n\n- GitHub: https:\u002F\u002Fgithub.com\u002FEleutherAI\u002Flm-evaluation-harness\n- Docs: https:\u002F\u002Fgithub.com\u002FEleutherAI\u002Flm-evaluation-harness\u002Ftree\u002Fmain\u002Fdocs\n- Task library: 60+ tasks including MMLU, GSM8K, HumanEval, TruthfulQA, HellaSwag, ARC, WinoGrande, etc.\n- Leaderboard: https:\u002F\u002Fhuggingface.co\u002Fspaces\u002FHuggingFaceH4\u002Fopen_llm_leaderboard (uses this harness)\n\n\n\n",{"name":5,"license":15,"metadata":45,"description":7},{"tags":46,"author":52,"version":53,"dependencies":54},[21,20,47,34,35,26,48,49,50,51],"Benchmarking","EleutherAI","Model Quality","Academic Benchmarks","Industry Standard","Orchestra Research","1.0.0",[55,56,57],"lm-eval","transformers","vllm","main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fevaluation-lm-evaluation-harness",3931,"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","2026-11-14 06:14:46",{"source":8,"skillName":5,"commands":69,"prompt":79,"zipAvailable":80},[70,73,76],{"tool":71,"command":72},"npx","npx skills add davila7\u002Fclaude-code-templates --skill evaluation-lm-evaluation-harness",{"tool":74,"command":75},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill evaluation-lm-evaluation-harness",{"tool":77,"command":78},"pnpm","pnpm dlx 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",[133],{"categoryNo":134,"categoryName":135,"taxonomyType":136,"taxonomyTypeDesc":137},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[139,140,141,142,143,144,145,146,29,24,147,148,149,150,151,152,153,154,155,156],"scoring","pool","wealthville pool scoring","liquidity","which","for","exit","now","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":158,"name":159,"nameZh":6,"description":160,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":161,"categories":162,"keywords":164,"publishedAt":40},"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",[163],{"categoryNo":134,"categoryName":135,"taxonomyType":136,"taxonomyTypeDesc":137},[165,166,167,168,169,170,171,172,173,174,175,24,176,177,178,179,180,181,182,183],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":185,"name":186,"nameZh":6,"description":187,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":188,"categories":189,"keywords":199,"publishedAt":40},"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",[190,193,194],{"categoryNo":191,"categoryName":192,"taxonomyType":136,"taxonomyTypeDesc":137},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":134,"categoryName":135,"taxonomyType":136,"taxonomyTypeDesc":137},{"categoryNo":195,"categoryName":196,"taxonomyType":197,"taxonomyTypeDesc":198},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[200,201,202,203,204,205,206,144,207,208,209,210,211,212,213,29,214,215,24,216],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":218,"name":219,"nameZh":6,"description":220,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":221,"licenseFileUrl":222,"sourceHtmlUrl":223,"categories":224,"keywords":228,"publishedAt":40},"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",[225],{"categoryNo":226,"categoryName":227,"taxonomyType":136,"taxonomyTypeDesc":137},"01JBSEEDCAT000000000000009","前端开发",[229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,144,251],"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":253,"name":254,"nameZh":6,"description":255,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":221,"licenseFileUrl":256,"sourceHtmlUrl":257,"categories":258,"keywords":263,"publishedAt":40},"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",[259,262],{"categoryNo":260,"categoryName":261,"taxonomyType":136,"taxonomyTypeDesc":137},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":226,"categoryName":227,"taxonomyType":136,"taxonomyTypeDesc":137},[264,265,266,267,268,230,269,239,240,270,271,272,273,144,274,148,275],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":277,"name":278,"nameZh":6,"description":279,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":221,"licenseFileUrl":280,"sourceHtmlUrl":281,"categories":282,"keywords":290,"publishedAt":40},"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",[283,286,287],{"categoryNo":284,"categoryName":285,"taxonomyType":136,"taxonomyTypeDesc":137},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":134,"categoryName":135,"taxonomyType":136,"taxonomyTypeDesc":137},{"categoryNo":288,"categoryName":289,"taxonomyType":197,"taxonomyTypeDesc":198},"01JBSEEDCAT000000000000036","内容创作者",[291,292,293,294,295,296,297,298,232,299,242,176,300,301,302,303,304,212,305,24,306,307,308,309,310,154,311],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]