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Skill

evaluating-llms-harness

From NousResearch

lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.).

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NousResearch/hermes-agent
Status
Actively maintained
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Source file
skills/mlops/evaluation/lm-evaluation-harness/SKILL.md

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# lm-evaluation-harness - LLM Benchmarking

## What's inside

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.

## Quick start

lm-evaluation-harness evaluates LLMs across 60+ academic benchmarks using standardized prompts and metrics.

**Installation**:
```bash
pip install lm-eval
```

**Evaluate any HuggingFace model**:
```bash
lm_eval --model hf \
  --model_args pretrained=meta-llama/Llama-2-7b-hf \
  --tasks mmlu,gsm8k,hellaswag \
  --device cuda:0 \
  --batch_size 8
```

**View available tasks**:
```bash
lm_eval --tasks list
```

## Common workflows

### Workflow 1: Standard benchmark evaluation

Evaluate model on core benchmarks (MMLU, GSM8K, HumanEval).

Copy this checklist:

```
Benchmark Evaluation:
- [ ] Step 1: Choose benchmark suite
- [ ] Step 2: Configure model
- [ ] Step 3: Run evaluation
- [ ] Step 4: Analyze results
```

**Step 1: Choose benchmark suite**

**Core reasoning benchmarks**:
- **MMLU** (Massive Multitask Language Understanding) - 57 subjects, multiple choice
- **GSM8K** - Grade school math word problems
- **HellaSwag** - Common sense reasoning
- **TruthfulQA** - Truthfulness and factuality
- **
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