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bigcode/starcoder

A 15.5B parameter model trained on 80+ programming languages from The Stack (v1.2) dataset, using a GPT-2 architecture with multi-query attention and Fill-in-the-Middle objective. The model is capable of generating code snippets provided some context, but the generated code is not guaranteed to work as intended and may contain bugs or exploits. The model is licensed under the BigCode OpenRAIL-M v1 license agreement.

A 15.5B parameter model trained on 80+ programming languages from The Stack (v1.2) dataset, using a GPT-2 architecture with multi-query attention and Fill-in-the-Middle objective. The model is capable of generating code snippets provided some context, but the generated code is not guaranteed to work as intended and may contain bugs or exploits. The model is licensed under the BigCode OpenRAIL-M v1 license agreement.

Public
$0.0005 / sec
8k

Input

text to generate from

maximum length of the newly generated generated text.If not set or None defaults to model's max context length minus input length. (Default: 512, 1 ≤ max_new_tokens ≤ 100000)

Temperature

temperature to use for sampling. 0 means the output is deterministic. Values greater than 1 encourage more diversity (Default: 0.7, 0 ≤ temperature ≤ 100)

Sample from the set of tokens with highest probability such that sum of probabilies is higher than p. Lower values focus on the most probable tokens.Higher values sample more low-probability tokens (Default: 0.9, 0 < top_p ≤ 1)

Sample from the best k (number of) tokens. 0 means off (Default: 0, 0 ≤ top_k < 100000)

Repetition Penalty

repetition penalty. Value of 1 means no penalty, values greater than 1 discourage repetition, smaller than 1 encourage repetition. (Default: 1, 0.01 ≤ repetition_penalty ≤ 5)

Up to 16 strings that will terminate generation immediately. Please separate items by comma

Num Responses

Number of output sequences to return. Incompatible with streaming (Default: 1, 1 ≤ num_responses ≤ 2)

How to format the response 2

Presence Penalty

Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics. (Default: 0, -2 ≤ presence_penalty ≤ 2)

Frequency Penalty

Positive values penalize new tokens based on how many times they appear in the text so far, increasing the model's likelihood to talk about new topics. (Default: 0, -2 ≤ frequency_penalty ≤ 2)

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Output

I have this dream about the day I got a job at a tech company. I just woke up on a plane. I sat down on the floor and started getting work done. After getting up around 6 p.m., I looked around and

StarCoder

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Play with the model on the StarCoder Playground.

Table of Contents

  1. Model Summary
  2. Use
  3. Limitations
  4. Training
  5. License
  6. Citation

Model Summary

The StarCoder models are 15.5B parameter models trained on 80+ programming languages from The Stack (v1.2), with opt-out requests excluded. The model uses Multi Query Attention, a context window of 8192 tokens, and was trained using the Fill-in-the-Middle objective on 1 trillion tokens.

Use

Intended use

The model was trained on GitHub code. As such it is not an instruction model and commands like "Write a function that computes the square root." do not work well. However, by using the Tech Assistant prompt you can turn it into a capable technical assistant.

Feel free to share your generations in the Community tab!

Attribution & Other Requirements

The pretraining dataset of the model was filtered for permissive licenses only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected. We provide a search index that let's you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code.

Limitations

The model has been trained on source code from 80+ programming languages. The predominant natural language in source code is English although other languages are also present. As such the model is capable of generating code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient, contain bugs or exploits. See the paper for an in-depth discussion of the model limitations.

Training

Model

  • Architecture: GPT-2 model with multi-query attention and Fill-in-the-Middle objective
  • Pretraining steps: 250k
  • Pretraining tokens: 1 trillion
  • Precision: bfloat16

Hardware

  • GPUs: 512 Tesla A100
  • Training time: 24 days

Software

License

The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement here.

Citation

@article{li2023starcoder,
      title={StarCoder: may the source be with you!},
      author={Raymond Li and Loubna Ben Allal and Yangtian Zi and Niklas Muennighoff and Denis Kocetkov and Chenghao Mou and Marc Marone and Christopher Akiki and Jia Li and Jenny Chim and Qian Liu and Evgenii Zheltonozhskii and Terry Yue Zhuo and Thomas Wang and Olivier Dehaene and Mishig Davaadorj and Joel Lamy-Poirier and João Monteiro and Oleh Shliazhko and Nicolas Gontier and Nicholas Meade and Armel Zebaze and Ming-Ho Yee and Logesh Kumar Umapathi and Jian Zhu and Benjamin Lipkin and Muhtasham Oblokulov and Zhiruo Wang and Rudra Murthy and Jason Stillerman and Siva Sankalp Patel and Dmitry Abulkhanov and Marco Zocca and Manan Dey and Zhihan Zhang and Nour Fahmy and Urvashi Bhattacharyya and Wenhao Yu and Swayam Singh and Sasha Luccioni and Paulo Villegas and Maxim Kunakov and Fedor Zhdanov and Manuel Romero and Tony Lee and Nadav Timor and Jennifer Ding and Claire Schlesinger and Hailey Schoelkopf and Jan Ebert and Tri Dao and Mayank Mishra and Alex Gu and Jennifer Robinson and Carolyn Jane Anderson and Brendan Dolan-Gavitt and Danish Contractor and Siva Reddy and Daniel Fried and Dzmitry Bahdanau and Yacine Jernite and Carlos Muñoz Ferrandis and Sean Hughes and Thomas Wolf and Arjun Guha and Leandro von Werra and Harm de Vries},
      year={2023},
      eprint={2305.06161},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}