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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2308.12950 |
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| _version_ | 1866914661856706560 |
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| author | Rozière, Baptiste Gehring, Jonas Gloeckle, Fabian Sootla, Sten Gat, Itai Tan, Xiaoqing Ellen Adi, Yossi Liu, Jingyu Sauvestre, Romain Remez, Tal Rapin, Jérémy Kozhevnikov, Artyom Evtimov, Ivan Bitton, Joanna Bhatt, Manish Ferrer, Cristian Canton Grattafiori, Aaron Xiong, Wenhan Défossez, Alexandre Copet, Jade Azhar, Faisal Touvron, Hugo Martin, Louis Usunier, Nicolas Scialom, Thomas Synnaeve, Gabriel |
| author_facet | Rozière, Baptiste Gehring, Jonas Gloeckle, Fabian Sootla, Sten Gat, Itai Tan, Xiaoqing Ellen Adi, Yossi Liu, Jingyu Sauvestre, Romain Remez, Tal Rapin, Jérémy Kozhevnikov, Artyom Evtimov, Ivan Bitton, Joanna Bhatt, Manish Ferrer, Cristian Canton Grattafiori, Aaron Xiong, Wenhan Défossez, Alexandre Copet, Jade Azhar, Faisal Touvron, Hugo Martin, Louis Usunier, Nicolas Scialom, Thomas Synnaeve, Gabriel |
| contents | We release Code Llama, a family of large language models for code based on Llama 2 providing state-of-the-art performance among open models, infilling capabilities, support for large input contexts, and zero-shot instruction following ability for programming tasks. We provide multiple flavors to cover a wide range of applications: foundation models (Code Llama), Python specializations (Code Llama - Python), and instruction-following models (Code Llama - Instruct) with 7B, 13B, 34B and 70B parameters each. All models are trained on sequences of 16k tokens and show improvements on inputs with up to 100k tokens. 7B, 13B and 70B Code Llama and Code Llama - Instruct variants support infilling based on surrounding content. Code Llama reaches state-of-the-art performance among open models on several code benchmarks, with scores of up to 67% and 65% on HumanEval and MBPP, respectively. Notably, Code Llama - Python 7B outperforms Llama 2 70B on HumanEval and MBPP, and all our models outperform every other publicly available model on MultiPL-E. We release Code Llama under a permissive license that allows for both research and commercial use. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_12950 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Code Llama: Open Foundation Models for Code Rozière, Baptiste Gehring, Jonas Gloeckle, Fabian Sootla, Sten Gat, Itai Tan, Xiaoqing Ellen Adi, Yossi Liu, Jingyu Sauvestre, Romain Remez, Tal Rapin, Jérémy Kozhevnikov, Artyom Evtimov, Ivan Bitton, Joanna Bhatt, Manish Ferrer, Cristian Canton Grattafiori, Aaron Xiong, Wenhan Défossez, Alexandre Copet, Jade Azhar, Faisal Touvron, Hugo Martin, Louis Usunier, Nicolas Scialom, Thomas Synnaeve, Gabriel Computation and Language We release Code Llama, a family of large language models for code based on Llama 2 providing state-of-the-art performance among open models, infilling capabilities, support for large input contexts, and zero-shot instruction following ability for programming tasks. We provide multiple flavors to cover a wide range of applications: foundation models (Code Llama), Python specializations (Code Llama - Python), and instruction-following models (Code Llama - Instruct) with 7B, 13B, 34B and 70B parameters each. All models are trained on sequences of 16k tokens and show improvements on inputs with up to 100k tokens. 7B, 13B and 70B Code Llama and Code Llama - Instruct variants support infilling based on surrounding content. Code Llama reaches state-of-the-art performance among open models on several code benchmarks, with scores of up to 67% and 65% on HumanEval and MBPP, respectively. Notably, Code Llama - Python 7B outperforms Llama 2 70B on HumanEval and MBPP, and all our models outperform every other publicly available model on MultiPL-E. We release Code Llama under a permissive license that allows for both research and commercial use. |
| title | Code Llama: Open Foundation Models for Code |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2308.12950 |