🐙OctoPack
Collection
13 items
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SantaCoderPack is an pre-trained model with the same architecture of SantaCoder on CommitPack using this format:
<commit_before>code_before<commit_msg>message<commit_after>code_after
Data | CommitPack | 4TB of GitHub commits across 350 programming languages |
---|---|---|
Model | SantaCoderPack | SantaCoderPack (1.1B parameters) pre-trained on CommitPack |
Evaluation | HumanEvalPack/HumanEvalFix | Extension of OpenAI's HumanEval to HumanEvalFix |
The model follows instructions provided in the input. We recommend prefacing your input with "def has_close_elements(numbers: List[float], threshold: float) -> bool:\n for idx, elem in enumerate(numbers):\n for idx2, elem2 in enumerate(numbers):\n if idx != idx2:\n distance = elem - elem2\n if distance < threshold:\n return True\n\n return FalseFix bugs in has_close_elements."
Feel free to share your generations in the Community tab!
# pip install -q transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "bigcode/santacoderpack"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
inputs = tokenizer.encode("Q<commit_before>def has_close_elements(numbers: List[float], threshold: float) -> bool:\n for idx, elem in enumerate(numbers):\n for idx2, elem2 in enumerate(numbers):\n if idx != idx2:\n distance = elem - elem2\n if distance < threshold:\n return True\n\n return False<commit_message>Fix bugs in has_close_elements.<commit_after>", return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
@article{muennighoff2023octopack,
title={OctoPack: Instruction Tuning Code Large Language Models},
author={Niklas Muennighoff and Qian Liu and Armel Zebaze and Qinkai Zheng and Binyuan Hui and Terry Yue Zhuo and Swayam Singh and Xiangru Tang and Leandro von Werra and Shayne Longpre},
journal={arXiv preprint arXiv:2308.07124},
year={2023}
}