BigCodeBench

💥 Impact • 📰 News • 🔥 Quick Start • 🚀 Remote Evaluation • 💻 LLM-generated Code • 🧑 Advanced Usage • 📰 Result Submission • 📜 Citation

BigCodeBench has been trusted by many LLM teams including:

• Zhipu AI
• Alibaba Qwen
• DeepSeek
• Amazon AWS AI
• Snowflake AI Research
• ServiceNow Research
• Meta AI
• Cohere AI
• Sakana AI
• Allen Institute for Artificial Intelligence (AI2)

• [2025年01月22日] We are releasing bigcodebench==v0.2.2.dev2, with 163 models evaluated!
• [2024年10月06日] We are releasing bigcodebench==v0.2.0!
• [2024年10月05日] We create a public code execution API on the Hugging Face space.
• [2024年10月01日] We have evaluated 139 models on BigCodeBench-Hard so far. Take a look at the leaderboard!
• [2024年08月19日] To make the evaluation fully reproducible, we add a real-time code execution session to the leaderboard. It can be viewed here.
• [2024年08月02日] We release bigcodebench==v0.1.9.

BigCodeBench is an easy-to-use benchmark for solving practical and challenging tasks via code. It aims to evaluate the true programming capabilities of large language models (LLMs) in a more realistic setting. The benchmark is designed for HumanEval-like function-level code generation tasks, but with much more complex instructions and diverse function calls.

There are two splits in BigCodeBench:

• Complete: Thes split is designed for code completion based on the comprehensive docstrings.
• Instruct: The split works for the instruction-tuned and chat models only, where the models are asked to generate a code snippet based on the natural language instructions. The instructions only contain necessary information, and require more complex reasoning.

BigCodeBench focuses on task automation via code generation with diverse function calls and complex instructions, with:

• ✨ Precise evaluation & ranking: See our leaderboard for latest LLM rankings before & after rigorous evaluation.
• ✨ Pre-generated samples: BigCodeBench accelerates code intelligence research by open-sourcing LLM-generated samples for various models -- no need to re-run the expensive benchmarks!

To get started, please first set up the environment:

# By default, you will use the remote evaluation API to execute the output samples.
pip install bigcodebench --upgrade
# You are suggested to use `flash-attn` for generating code samples.
pip install packaging ninja
pip install flash-attn --no-build-isolation
# Note: if you have installation problem, consider using pre-built
# wheels from https://github.com/Dao-AILab/flash-attention/releases

# Install to use bigcodebench.generate
pip install "git+https://github.com/bigcode-project/bigcodebench.git" --upgrade

We use the greedy decoding as an example to show how to evaluate the generated code samples via remote API.

bigcodebench.evaluate \
 --model meta-llama/Meta-Llama-3.1-8B-Instruct \
 --execution [e2b|gradio|local] \
 --split [complete|instruct] \
 --subset [full|hard] \
 --backend [vllm|openai|anthropic|google|mistral|hf|hf-inference]

• All the resulted files will be stored in a folder named bcb_results.
• The generated code samples will be stored in a file named [model_name]--bigcodebench-[instruct|complete]--[backend]-[temp]-[n_samples]-sanitized_calibrated.jsonl.
• The evaluation results will be stored in a file named [model_name]--bigcodebench-[instruct|complete]--[backend]-[temp]-[n_samples]-sanitized_calibrated_eval_results.json.
• The pass@k results will be stored in a file named [model_name]--bigcodebench-[instruct|complete]--[backend]-[temp]-[n_samples]-sanitized_calibrated_pass_at_k.json.

To use E2B, you need to set up an account and get an API key from E2B.

export E2B_API_KEY=<your_e2b_api_key>

Access OpenAI APIs from OpenAI Console

export OPENAI_API_KEY=<your_openai_api_key>

Access Anthropic APIs from Anthropic Console

export ANTHROPIC_API_KEY=<your_anthropic_api_key>

Access Mistral APIs from Mistral Console

export MISTRAL_API_KEY=<your_mistral_api_key>

Access Gemini APIs from Google AI Studio

export GOOGLE_API_KEY=<your_google_api_key>

Access the Hugging Face Serverless Inference API

export HF_INFERENCE_API_KEY=<your_hf_api_key>

Please make sure your HF access token has the Make calls to inference providers permission.

We share pre-generated code samples from LLMs we have evaluated on the full set:

• See the attachment of our v0.2.4. We include sanitized_samples_calibrated.zip for your convenience.

Please refer to the ADVANCED USAGE for more details.

Please email both the generated code samples and the execution results to terry.zhuo@monash.edu if you would like to contribute your model to the leaderboard. Note that the file names should be in the format of [model_name]--[revision]--[bigcodebench|bigcodebench-hard]-[instruct|complete]--[backend]-[temp]-[n_samples]-sanitized_calibrated.jsonl and [model_name]--[revision]--[bigcodebench|bigcodebench-hard]-[instruct|complete]--[backend]-[temp]-[n_samples]-sanitized_calibrated_eval_results.json. You can file an issue to remind us if we do not respond to your email within 3 days.

@article{zhuo2024bigcodebench,
 title={BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions},
 author={Zhuo, Terry Yue and Vu, Minh Chien and Chim, Jenny and Hu, Han and Yu, Wenhao and Widyasari, Ratnadira and Yusuf, Imam Nur Bani and Zhan, Haolan and He, Junda and Paul, Indraneil and others},
 journal={arXiv preprint arXiv:2406.15877},
 year={2024}
}

• EvalPlus