BigCode

Open and responsible research and development of large language models for code. #BigCodeProject run by @huggingface + @ServiceNowRSRCH

[BigCode @BigCodeProject]

Introducing: StarCoder2 and The Stack v2 ⭐️

StarCoder2 is trained with a 16k token context and repo-level information for 4T+ tokens. All built on The Stack v2 - the largest code dataset with 900B+ tokens.

All code, data and models are fully open!

https://t.co/fM7GinxJBd

[Terry Yue Zhuo @terryyuezhuo]

It’s so much fun working with the other 39 community members on this project!

Start to try out various frontier models in BigCodeArena today.

[BigCode @BigCodeProject]

- For more details, please check out the blog:  https://t.co/TMyYpHA3lM

- Try recent LLMs (e.g., Qwen3 series and DeepSeek-V3.2) on BigCodeArena now: https://t.co/KPk8q0wB8G

- Paper Link: https://t.co/a9plXehamb

- GitHub: https://t.co/tx7Ynz7j4N

[BigCode @BigCodeProject]

Introducing BigCodeArena, a human-in-the-loop platform for evaluating code through execution.

Unlike current open evaluation platforms that collect human preferences on text, it enables interaction with runnable code to assess functionality and quality across any language.

[BigCode @BigCodeProject]

BigCodeArena cannot be built without the support of the BigCode community. We are grateful for the huge credits provided by the @e2b team. We thank @hyperbolic_labs, @nvidia, and @Alibaba_Qwen for providing the model inference endpoints.

[Terry Yue Zhuo @terryyuezhuo]

BigCodeBench @BigCodeProject evaluation framework has been fully upgraded! Just pip install -U bigcodebench

With v0.2.0, it's now much easier to use compared to the previous v0.1.* versions. The new version adopts the @Gradio Client API interface from @huggingface Spaces by default, w/o the need for local environment setup, and can be replaced with a custom API if desired. Moreover, the latest version no longer requires running separate commands for each stage (like generate, sanitize, and evaluate), significantly simplifying the workflow.

The new version also features Batch Inference — running the LLMs on the BigCodeBench-Full set now takes under 5mins for generation and execution!

BTW, the benchmark data has been updated to v0.1.2, improving task instructions and test examples.

Some of the updates in this release were inspired by EvalPlus @JiaweiLiu_ . A big thank you for the continued maintenance of EvalPlus and the strong support for BigCodeBench 🤗

[Josh @JoshPurtell]

Evaluating LM agents has come a long way since gpt-4 released in March of 2023.

We now have SWE-Bench, (Visual) Web Arena, and other evaluations that tell us a lot about how the best models + architectures do on hard and important tasks.

There's still lots to do, though 🧵

[Terry Yue Zhuo @terryyuezhuo]

People may think BigCodeBench @BigCodeProject is nothing more than a straightforward coding benchmark, but it is not.

BigCodeBench is a rigorous testbed for LLM agents using code to solve complex and practical challenges. Each task demands significant reasoning capabilities for selecting appropriate library APIs and logically connecting them to craft a program.

Rather than merely providing high-level instructions, each task comes with detailed requirements to evaluate the model's ability to adhere to all aspects. While language models typically perform well on short and simple tasks, they often struggle with longer and more complex problems (e.g., BigCodeBench-Hard).

A model needs to perform well on BigCodeBench before it can be used for agentic software development.

[Qian Liu @sivil_taram]

By popular demand, I have released the StarCoder2 code documentation dataset, please check it out ⬇️

https://t.co/jQ9xsmIH4e

[Arjun Guha @ArjunGuha]

This work will appear at OOPSLA 2024.

New since last year: the StarCoder2 LLM from @BigCodeProject uses MultiPL-T as part of its pretraining corpus.

[Terry Yue Zhuo @terryyuezhuo]

Today, we are happy to announce the beta mode of real-time Code Execution for BigCodeBench @BigCodeProject, which has been integrated into our Hugging Face leaderboard.

We understand that setting up a dependency-based execution environment can be cumbersome, even with the built-in Docker image and Dockerfile. To make the evaluation process more reproducible, we've built an interactive environment for you, with guidance from the @Gradio team! (Special thanks to @evilpingwin 🤗)

Please note: (1) The execution process might be slightly slower than what you experience on a local machine, as we are using the basic CPU option. There are some compatibility issues with the upgraded CPU environment, and we are currently exploring solutions. (2) Four tasks in the full set require some tricky setup, which has resulted in a pass rate of 99.6%. We will work to fix these in the next iterations :)

[BigCode @BigCodeProject]

Releasing BigCodeBench-Hard: a subset of more challenging and user-facing tasks.

BigCodeBench-Hard provides more accurate model performance evaluations and we also investigate some recent model updates.

Read more: https://t.co/zc4NuIvIDX
Leaderboard: https://t.co/zbTr3JPcCe

[Rajiv Shah @rajistics]

BigCodeBench dataset🌸
Use it as inspiration when building your Generative AI evaluations.

BigCodeBench h/t:
@BigCodeProject @terryyuezhuo @lvwerra @clefourrier @huggingface
(to name just a few of the people involved)

[Terry Yue Zhuo @terryyuezhuo]

Ppl are curious about the performance of DeepSeek-Coder-V2-Lite on BigCodeBench. We've added its results, along with a few other models, to the leaderboard! https://t.co/u4CCnwlu1r

DeepSeek-Coder-V2-Lite-Instruct is a beast indeed, similar to Magicoder-S-DS-6.7B, but with only 2.4B activated parameters! 🤯

We also update all the code generation results here: https://t.co/BUeSIu2MMF

Feel free to submit a PR if you want to see other models on BigCodeBench 🤗https://t.co/XAFwpZJtix

[Philipp Schmid @_philschmid]

It is time to deprecate HumanEval! 🧑🏻‍💻 @BigCodeProject just released BigCodeBench, a new benchmark to evaluate LLMs on challenging and complex coding tasks focused on realistic, function-level tasks that require the use of diverse libraries and complex reasoning! 👀

🧩 Contains 1,140 tasks with 5.6 test cases each, covering 139 libraries in Python.

📊 Uses Pass@1 with greedy decoding and Elo rating for comprehensive evaluation.

🏆 Best model is GPT-4o 61.1%, followed by DeepSeek-Coder-V2.

🥈 Best open Model is DeepSeek-Coder-V2 with 59.7%, better than Claude 3 Opus or Gemini.

👥 Tasks are created in a three-stage process, including synthetic data generation and cross-validation by humans.

🧱 Evaluation framework and Docker images available for easy reproduction

🔜 Plans to extend to multilingualism.

Blog: https://t.co/5eYKH46Xtq
Leaderboard: https://t.co/1ijWUJhNdf
Code: https://t.co/u2OCS6c1OO

[Terry Yue Zhuo @terryyuezhuo]

In the past few months, we’ve seen SOTA LLMs saturating basic coding benchmarks with short and simplified coding tasks. It's time to enter the next stage of coding challenge under comprehensive and realistic scenarios!

-- Here comes BigCodeBench, benchmarking LLMs on solving practical and challenging programming tasks!

So, can LLMs solve these tasks?
- Not yet!

🏆 Pass@1: Humans ace 97%, GPT-4o only hits 50-60%, but DeepSeek-Coder-V2 is tighy at its heels!

Check out our leaderboard, data, code, and paper: https://t.co/EYXhp0T8E5

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[BigCode @BigCodeProject]

We release leaderboard, dataset, code, and paper:
- 🤓 Blog: https://t.co/dRo1vRBLPV
- 🌐 Website: https://t.co/oUm1EcpAjn
- 🏆 Leaderboard: https://t.co/zbTr3JOEMG
-  📚 Dataset: https://t.co/MNpQBJ2Mbk
- 🛠️ Code: https://t.co/wJF09Yi3H2
- 📄 Paper: https://t.co/PeWXIZU84v

[BigCode @BigCodeProject]

Introducing 🌸BigCodeBench: Benchmarking Large Language Models on Solving Practical and Challenging Programming Tasks!

BigCodeBench goes beyond simple evals like HumanEval and MBPP and tests LLMs on more realistic and challenging coding tasks.

[BigCode @BigCodeProject]

BigCodeBench contains 1,140 function-level tasks to challenge LLMs to follow instructions and compose multiple function calls as tools from 139 Python libraries. To evaluate LLMs rigorously, each programming task encompasses 5.6 test cases with an average branch coverage of 99%.