evaluating-llms-harnesslisted

# 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 - **ARC** (AI2 Reasoning Challenge) - Science questions **Code benchmarks**: - **HumanEval** - Python c