Stable Code Technical Report
Fuente:
arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929299603324928 |
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| author | Pinnaparaju, Nikhil Adithyan, Reshinth Phung, Duy Tow, Jonathan Baicoianu, James Datta, Ashish Zhuravinskyi, Maksym Mahan, Dakota Bellagente, Marco Riquelme, Carlos Cooper, Nathan |
| author_facet | Pinnaparaju, Nikhil Adithyan, Reshinth Phung, Duy Tow, Jonathan Baicoianu, James Datta, Ashish Zhuravinskyi, Maksym Mahan, Dakota Bellagente, Marco Riquelme, Carlos Cooper, Nathan |
| contents | We introduce Stable Code, the first in our new-generation of code language models series, which serves as a general-purpose base code language model targeting code completion, reasoning, math, and other software engineering-based tasks. Additionally, we introduce an instruction variant named Stable Code Instruct that allows conversing with the model in a natural chat interface for performing question-answering and instruction-based tasks. In this technical report, we detail the data and training procedure leading to both models. Their weights are available via Hugging Face for anyone to download and use at https://huggingface.co/stabilityai/stable-code-3b and https://huggingface.co/stabilityai/stable-code-instruct-3b. This report contains thorough evaluations of the models, including multilingual programming benchmarks, and the MT benchmark focusing on multi-turn dialogues. At the time of its release, Stable Code is the state-of-the-art open model under 3B parameters and even performs comparably to larger models of sizes 7 billion and 15 billion parameters on the popular Multi-PL benchmark. Stable Code Instruct also exhibits state-of-the-art performance on the MT-Bench coding tasks and on Multi-PL completion compared to other instruction tuned models. Given its appealing small size, we also provide throughput measurements on a number of edge devices. In addition, we open source several quantized checkpoints and provide their performance metrics compared to the original model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_01226 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Stable Code Technical Report Pinnaparaju, Nikhil Adithyan, Reshinth Phung, Duy Tow, Jonathan Baicoianu, James Datta, Ashish Zhuravinskyi, Maksym Mahan, Dakota Bellagente, Marco Riquelme, Carlos Cooper, Nathan Computation and Language We introduce Stable Code, the first in our new-generation of code language models series, which serves as a general-purpose base code language model targeting code completion, reasoning, math, and other software engineering-based tasks. Additionally, we introduce an instruction variant named Stable Code Instruct that allows conversing with the model in a natural chat interface for performing question-answering and instruction-based tasks. In this technical report, we detail the data and training procedure leading to both models. Their weights are available via Hugging Face for anyone to download and use at https://huggingface.co/stabilityai/stable-code-3b and https://huggingface.co/stabilityai/stable-code-instruct-3b. This report contains thorough evaluations of the models, including multilingual programming benchmarks, and the MT benchmark focusing on multi-turn dialogues. At the time of its release, Stable Code is the state-of-the-art open model under 3B parameters and even performs comparably to larger models of sizes 7 billion and 15 billion parameters on the popular Multi-PL benchmark. Stable Code Instruct also exhibits state-of-the-art performance on the MT-Bench coding tasks and on Multi-PL completion compared to other instruction tuned models. Given its appealing small size, we also provide throughput measurements on a number of edge devices. In addition, we open source several quantized checkpoints and provide their performance metrics compared to the original model. |
| title | Stable Code Technical Report |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2404.01226 |