# Evaluating Code Models

Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks...

- Repository: https://github.com/Orchestra-Research/AI-Research-SKILLs (14176 stars)
- Category: Testing
- License: MIT
- Last updated: 2026-10-11
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill evaluating-code-models -g -a claude-code -y`
Codex: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill evaluating-code-models -g -a codex -y`
Cursor: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill evaluating-code-models -g -a cursor -y`
Gemini CLI: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill evaluating-code-models -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add Orchestra-Research/AI-Research-SKILLs`

## Description

Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# BigCode Evaluation Harness - Code Model Benchmarking

## Quick Start

BigCode Evaluation Harness evaluates code generation models across 15+ benchmarks including HumanEval, MBPP, and MultiPL-E (18 languages).

**Installation**:
```bash
git clone https://github.com/bigcode-project/bigcode-evaluation-harness.git
cd bigcode-evaluation-harness
pip install -e .
accelerate config
```

**Evaluate on HumanEval**:
```bash
accelerate launch main.py \
  --model bigcode/starcoder2-7b \
  --tasks humaneval \
  --max_length_generation 512 \
  --temperature 0.2 \
  --n_samples 20 \
  --batch_size 10 \
  --allow_code_execution \
  --save_generations
```

**View available tasks**:
```bash
python -c "from bigcode_eval.tasks import ALL_TASKS; print(ALL_TASKS)"
```

## Common Workflows

### Workflow 1: Standard Code Benchmark Evaluation

Evaluate model on core code benchmarks (HumanEval, MBPP, HumanEval+).

**Checklist**:
```
Code Benchmark Evaluation:
- [ ] Step 1: Choose benchmark suite
- [ ] Step 2: Configure model and generation
- [ ] Step 3: Run evaluation with code execution
- [ ] Step 4: Analyze pass@k results
```

**Step 1: Choose benchmark suite**

**Python code generation** (most common):
- **HumanEval**: 164 handwritten problems, function completion
- **HumanEval+**: Same 164 problems with 80× more tests (stricter)
- **MBPP**: 500 crowd-sourced problems, entry-level difficulty
- **MBPP+**: 399 curated problems with 35× more tests

**Multi-language** (18 languages):
- **MultiPL-E**: HumanEval/MBPP translated to C++, Java, JavaScript, Go, Rust, etc.

**Advanced**:
- **APPS**: 10,000 problems (introductory/interview/competition)
- **DS-1000**: 1,000 data science problems across 7 libraries

**Step 2: Configure model and generation**

The rest: https://github.com/Orchestra-Research/AI-Research-SKILLs/blob/HEAD/11-evaluation/bigcode-evaluation-harness/SKILL.md