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evaluating-code-models
GitHubBigCode评估工具，用于在HumanEval等15+基准上评测代码生成模型。支持多语言、pass@k指标及自定义配置，适用于模型能力对比与质量测量。
触发场景
需要评测代码生成模型的性能
比较不同模型的编码能力
测试模型的多语言支持情况
衡量代码生成的整体质量
安装
npx skills add synthetic-sciences/openscience --skill evaluating-code-models -g -y
SKILL.md
Frontmatter
{
"name": "evaluating-code-models",
"tags": [
"Evaluation",
"Code Generation",
"HumanEval",
"MBPP",
"MultiPL-E",
"Pass@k",
"BigCode",
"Benchmarking",
"Code Models"
],
"author": "Synthetic Sciences",
"license": "MIT",
"version": "1.0.0",
"category": "ml-training",
"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.",
"dependencies": [
"bigcode-evaluation-harness",
"transformers>=4.25.1",
"accelerate>=0.13.2",
"datasets>=2.6.1"
]
}
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:
git clone https://github.com/bigcode-project/bigcode-evaluation-harness.git
cd bigcode-evaluation-harness
pip install -e .
accelerate config
Evaluate on HumanEval:
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:
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
# Standard HuggingFace model
accelerate launch main.py \
--model bigcode/starcoder2-7b \
--tasks humaneval \
--max_length_generation 512 \
--temperature 0.2 \
--do_sample True \
--n_samples 200 \
--batch_size 50 \
--allow_code_execution
# Quantized model (4-bit)
accelerate launch main.py \
--model codellama/CodeLlama-34b-hf \
--tasks humaneval \
--load_in_4bit \
--max_length_generation 512 \
--allow_code_execution
# Custom/private model
accelerate launch main.py \
--model /path/to/my-code-model \
--tasks humaneval \
--trust_remote_code \
--use_auth_token \
--allow_code_execution
Step 3: Run evaluation
# Full evaluation with pass@k estimation (k=1,10,100)
accelerate launch main.py \
--model bigcode/starcoder2-7b \
--tasks humaneval \
--temperature 0.8 \
--n_samples 200 \
--batch_size 50 \
--allow_code_execution \
--save_generations \
--metric_output_path results/starcoder2-humaneval.json
Step 4: Analyze results
Results in results/starcoder2-humaneval.json:
{
"humaneval": {
"pass@1": 0.354,
"pass@10": 0.521,
"pass@100": 0.689
},
"config": {
"model": "bigcode/starcoder2-7b",
"temperature": 0.8,
"n_samples": 200
}
}
Workflow 2: Multi-Language Evaluation (MultiPL-E)
Evaluate code generation across 18 programming languages.
Checklist:
Multi-Language Evaluation:
- [ ] Step 1: Generate solutions (host machine)
- [ ] Step 2: Run evaluation in Docker (safe execution)
- [ ] Step 3: Compare across languages
Step 1: Generate solutions on host
# Generate without execution (safe)
accelerate launch main.py \
--model bigcode/starcoder2-7b \
--tasks multiple-py,multiple-js,multiple-java,multiple-cpp \
--max_length_generation 650 \
--temperature 0.8 \
--n_samples 50 \
--batch_size 50 \
--generation_only \
--save_generations \
--save_generations_path generations_multi.json
Step 2: Evaluate in Docker container
# Pull the MultiPL-E Docker image
docker pull ghcr.io/bigcode-project/evaluation-harness-multiple
# Run evaluation inside container
docker run -v $(pwd)/generations_multi.json:/app/generations.json:ro \
-it evaluation-harness-multiple python3 main.py \
--model bigcode/starcoder2-7b \
--tasks multiple-py,multiple-js,multiple-java,multiple-cpp \
--load_generations_path /app/generations.json \
--allow_code_execution \
--n_samples 50
Supported languages: Python, JavaScript, Java, C++, Go, Rust, TypeScript, C#, PHP, Ruby, Swift, Kotlin, Scala, Perl, Julia, Lua, R, Racket
Workflow 3: Instruction-Tuned Model Evaluation
Evaluate chat/instruction models with proper formatting.
Checklist:
Instruction Model Evaluation:
- [ ] Step 1: Use instruction-tuned tasks
- [ ] Step 2: Configure instruction tokens
- [ ] Step 3: Run evaluation
Step 1: Choose instruction tasks
- instruct-humaneval: HumanEval with instruction prompts
- humanevalsynthesize-{lang}: HumanEvalPack synthesis tasks
Step 2: Configure instruction tokens
# For models with chat templates (e.g., CodeLlama-Instruct)
accelerate launch main.py \
--model codellama/CodeLlama-7b-Instruct-hf \
--tasks instruct-humaneval \
--instruction_tokens "<s>[INST],</s>,[/INST]" \
--max_length_generation 512 \
--allow_code_execution
Step 3: HumanEvalPack for instruction models
# Test code synthesis across 6 languages
accelerate launch main.py \
--model codellama/CodeLlama-7b-Instruct-hf \
--tasks humanevalsynthesize-python,humanevalsynthesize-js \
--prompt instruct \
--max_length_generation 512 \
--allow_code_execution
Workflow 4: Compare Multiple Models
Benchmark suite for model comparison.
Step 1: Create evaluation script
#!/bin/bash
# eval_models.sh
MODELS=(
"bigcode/starcoder2-7b"
"codellama/CodeLlama-7b-hf"
"deepseek-ai/deepseek-coder-6.7b-base"
)
TASKS="humaneval,mbpp"
for model in "${MODELS[@]}"; do
model_name=$(echo $model | tr '/' '-')
echo "Evaluating $model"
accelerate launch main.py \
--model $model \
--tasks $TASKS \
--temperature 0.2 \
--n_samples 20 \
--batch_size 20 \
--allow_code_execution \
--metric_output_path results/${model_name}.json
done
Step 2: Generate comparison table
import json
import pandas as pd
models = ["bigcode-starcoder2-7b", "codellama-CodeLlama-7b-hf", "deepseek-ai-deepseek-coder-6.7b-base"]
results = []
for model in models:
with open(f"results/{model}.json") as f:
data = json.load(f)
results.append({
"Model": model,
"HumanEval pass@1": f"{data['humaneval']['pass@1']:.3f}",
"MBPP pass@1": f"{data['mbpp']['pass@1']:.3f}"
})
df = pd.DataFrame(results)
print(df.to_markdown(index=False))
When to Use vs Alternatives
Use BigCode Evaluation Harness when:
- Evaluating code generation models specifically
- Need multi-language evaluation (18 languages via MultiPL-E)
- Testing functional correctness with unit tests (pass@k)
- Benchmarking for BigCode/HuggingFace leaderboards
- Evaluating fill-in-the-middle (FIM) capabilities
Use alternatives instead:
- lm-evaluation-harness: General LLM benchmarks (MMLU, GSM8K, HellaSwag)
- EvalPlus: Stricter HumanEval+/MBPP+ with more test cases
- SWE-bench: Real-world GitHub issue resolution
- LiveCodeBench: Contamination-free, continuously updated problems
- CodeXGLUE: Code understanding tasks (clone detection, defect prediction)
Supported Benchmarks
| Benchmark | Problems | Languages | Metric | Use Case |
|---|---|---|---|---|
| HumanEval | 164 | Python | pass@k | Standard code completion |
| HumanEval+ | 164 | Python | pass@k | Stricter evaluation (80× tests) |
| MBPP | 500 | Python | pass@k | Entry-level problems |
| MBPP+ | 399 | Python | pass@k | Stricter evaluation (35× tests) |
| MultiPL-E | 164×18 | 18 languages | pass@k | Multi-language evaluation |
| APPS | 10,000 | Python | pass@k | Competition-level |
| DS-1000 | 1,000 | Python | pass@k | Data science (pandas, numpy, etc.) |
| HumanEvalPack | 164×3×6 | 6 languages | pass@k | Synthesis/fix/explain |
| Mercury | 1,889 | Python | Efficiency | Computational efficiency |
Common Issues
Issue: Different results than reported in papers
Check these factors:
# 1. Verify n_samples (need 200 for accurate pass@k)
--n_samples 200
# 2. Check temperature (0.2 for greedy-ish, 0.8 for sampling)
--temperature 0.8
# 3. Verify task name matches exactly
--tasks humaneval # Not "human_eval" or "HumanEval"
# 4. Check max_length_generation
--max_length_generation 512 # Increase for longer problems
Issue: CUDA out of memory
# Use quantization
--load_in_8bit
# OR
--load_in_4bit
# Reduce batch size
--batch_size 1
# Set memory limit
--max_memory_per_gpu "20GiB"
Issue: Code execution hangs or times out
Use Docker for safe execution:
# Generate on host (no execution)
--generation_only --save_generations
# Evaluate in Docker
docker run ... --allow_code_execution --load_generations_path ...
Issue: Low scores on instruction models
Ensure proper instruction formatting:
# Use instruction-specific tasks
--tasks instruct-humaneval
# Set instruction tokens for your model
--instruction_tokens "<s>[INST],</s>,[/INST]"
Issue: MultiPL-E language failures
Use the dedicated Docker image:
docker pull ghcr.io/bigcode-project/evaluation-harness-multiple
Command Reference
| Argument | Default | Description |
|---|---|---|
--model |
- | HuggingFace model ID or local path |
--tasks |
- | Comma-separated task names |
--n_samples |
1 | Samples per problem (200 for pass@k) |
--temperature |
0.2 | Sampling temperature |
--max_length_generation |
512 | Max tokens (prompt + generation) |
--batch_size |
1 | Batch size per GPU |
--allow_code_execution |
False | Enable code execution (required) |
--generation_only |
False | Generate without evaluation |
--load_generations_path |
- | Load pre-generated solutions |
--save_generations |
False | Save generated code |
--metric_output_path |
results.json | Output file for metrics |
--load_in_8bit |
False | 8-bit quantization |
--load_in_4bit |
False | 4-bit quantization |
--trust_remote_code |
False | Allow custom model code |
--precision |
fp32 | Model precision (fp32/fp16/bf16) |
Hardware Requirements
| Model Size | VRAM (fp16) | VRAM (4-bit) | Time (HumanEval, n=200) |
|---|---|---|---|
| 7B | 14GB | 6GB | ~30 min (A100) |
| 13B | 26GB | 10GB | ~1 hour (A100) |
| 34B | 68GB | 20GB | ~2 hours (A100) |
Resources
- GitHub: https://github.com/bigcode-project/bigcode-evaluation-harness
- Documentation: https://github.com/bigcode-project/bigcode-evaluation-harness/tree/main/docs
- BigCode Leaderboard: https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard
- HumanEval Dataset: https://huggingface.co/datasets/openai/openai_humaneval
- MultiPL-E: https://github.com/nuprl/MultiPL-E
版本历史
- e9844a4 当前 2026-07-11 17:28
依赖关系
-
required
bigcode-evaluation-harness -
required
transformers>=4.25.1 -
required
accelerate>=0.13.2 -
required
datasets>=2.6.1
