The following papers were recommended by the Semantic Scholar API
\n- \n
- DeepSeek-Coder: When the Large Language Model Meets Programming - The Rise of Code Intelligence (2024) \n
- TinyLlama: An Open-Source Small Language Model (2024) \n
- Studying Vulnerable Code Entities in R (2024) \n
- RepoAgent: An LLM-Powered Open-Source Framework for Repository-level Code Documentation Generation (2024) \n
- AST-T5: Structure-Aware Pretraining for Code Generation and Understanding (2024) \n
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May i know how to download the dataset of the stack-v2 bigcode? If anyone know about kindly help?
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Models for Code (Code LLMs),\nintroduces StarCoder2. In partnership with Software Heritage (SWH), we build\nThe Stack v2 on top of the digital commons of their source code archive.\nAlongside the SWH repositories spanning 619 programming languages, we carefully\nselect other high-quality data sources, such as GitHub pull requests, Kaggle\nnotebooks, and code documentation. This results in a training set that is 4x\nlarger than the first StarCoder dataset. We train StarCoder2 models with 3B,\n7B, and 15B parameters on 3.3 to 4.3 trillion tokens and thoroughly evaluate\nthem on a comprehensive set of Code LLM benchmarks. We find that our small\nmodel, StarCoder2-3B, outperforms other Code LLMs of similar size on most\nbenchmarks, and also outperforms StarCoderBase-15B. Our large model,\nStarCoder2- 15B, significantly outperforms other models of comparable size. In\naddition, it matches or outperforms CodeLlama-34B, a model more than twice its\nsize. Although DeepSeekCoder- 33B is the best-performing model at code\ncompletion for high-resource languages, we find that StarCoder2-15B outperforms\nit on math and code reasoning benchmarks, as well as several low-resource\nlanguages. We make the model weights available under an OpenRAIL license and\nensure full transparency regarding the training data by releasing the SoftWare\nHeritage persistent IDentifiers (SWHIDs) of the source code data.","upvotes":152,"discussionId":"65e161d606c9c86f25225a8a","ai_summary":"StarCoder2, a large language model for code developed through a collaboration with Software Heritage, outperforms other models of similar size on various benchmarks and matches or outperforms larger models in specific areas.","ai_keywords":["Large Language Models","Code LLMs","StarCoder2","Software Heritage","The Stack","GitHub pull requests","Kaggle notebooks","code documentation","DeepSeekCoder","math and code reasoning benchmarks","OpenRAIL license","SWHIDs"]},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"631d9fc7e4b67f5168f65eef","avatarUrl":"/avatars/5ead011052c96962901a5419c68b9cc2.svg","isPro":false,"fullname":"Sherman Chann","user":"152334H","type":"user"},{"_id":"629e2bcc46b4826be2c57fe3","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/629e2bcc46b4826be2c57fe3/41BiA52XlZi31ABsljFiq.jpeg","isPro":false,"fullname":"Tianyang Liu","user":"tianyang","type":"user"},{"_id":"61e7c06064d3c6c929057bee","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/61e7c06064d3c6c929057bee/QxULx1EA1bgmjXxupQX4B.jpeg","isPro":false,"fullname":"θηη","user":"gary109","type":"user"},{"_id":"620783f24e28382272337ba4","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/620783f24e28382272337ba4/zkUveQPNiDfYjgGhuFErj.jpeg","isPro":false,"fullname":"GuoLiangTang","user":"Tommy930","type":"user"},{"_id":"64747f7e33192631bacd8831","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/64747f7e33192631bacd8831/dstkZJ4sHJSeqLesV5cOC.jpeg","isPro":false,"fullname":"Taufiq Dwi Purnomo","user":"taufiqdp","type":"user"},{"_id":"644b584a9279988e0cbeb664","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/644b584a9279988e0cbeb664/fhWCI_Q26tTruhdFkjejw.jpeg","isPro":false,"fullname":"Jiawei Liu","user":"ganler","type":"user"},{"_id":"62b7fb545233925f253531c8","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/62b7fb545233925f253531c8/W50u2G1HK3EtUKHRU189V.jpeg","isPro":false,"fullname":"Terry Yue Zhuo","user":"terryyz","type":"user"},{"_id":"62cd5057674cdb524450093d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/62cd5057674cdb524450093d/f67rlrdsKPRTLdXCXoa_X.jpeg","isPro":false,"fullname":"Mayank Mishra","user":"mayank-mishra","type":"user"},{"_id":"5fcaabed246881afd5b00167","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1651847561574-5fcaabed246881afd5b00167.jpeg","isPro":false,"fullname":"Muhtasham Oblokulov","user":"muhtasham","type":"user"},{"_id":"5e48005437cb5b49818287a5","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/5e48005437cb5b49818287a5/4uCXGGui-9QifAT4qelxU.png","isPro":false,"fullname":"Leandro von Werra","user":"lvwerra","type":"user"},{"_id":"612ee6a7b960e78c6d2319d4","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/612ee6a7b960e78c6d2319d4/2Hu9BaAyXbyh1vt0v1Qui.jpeg","isPro":false,"fullname":"Qian Liu","user":"SivilTaram","type":"user"},{"_id":"5fcfb7c407408029ba3577e2","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1608146735109-5fcfb7c407408029ba3577e2.png","isPro":false,"fullname":"Simon Brandeis","user":"sbrandeis","type":"user"}],"acceptLanguages":["*"],"dailyPaperRank":1}">Abstract
StarCoder2, a large language model for code developed through a collaboration with Software Heritage, outperforms other models of similar size on various benchmarks and matches or outperforms larger models in specific areas.
The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder2. In partnership with Software Heritage (SWH), we build The Stack v2 on top of the digital commons of their source code archive. Alongside the SWH repositories spanning 619 programming languages, we carefully select other high-quality data sources, such as GitHub pull requests, Kaggle notebooks, and code documentation. This results in a training set that is 4x larger than the first StarCoder dataset. We train StarCoder2 models with 3B, 7B, and 15B parameters on 3.3 to 4.3 trillion tokens and thoroughly evaluate them on a comprehensive set of Code LLM benchmarks. We find that our small model, StarCoder2-3B, outperforms other Code LLMs of similar size on most benchmarks, and also outperforms StarCoderBase-15B. Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size. In addition, it matches or outperforms CodeLlama-34B, a model more than twice its size. Although DeepSeekCoder- 33B is the best-performing model at code completion for high-resource languages, we find that StarCoder2-15B outperforms it on math and code reasoning benchmarks, as well as several low-resource languages. We make the model weights available under an OpenRAIL license and ensure full transparency regarding the training data by releasing the SoftWare Heritage persistent IDentifiers (SWHIDs) of the source code data.
Community
I really love the approach and results (am I biased?)
Super excited to finally release StarCoder2 and The Stack v2 to the world. Looking forward to seeing what the community will build on top of them!
This release is a game changer for open science and open-source AI: world-class performance built responsibly and transparently.
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May i know how to download the dataset of the stack-v2 bigcode? If anyone know about kindly help?