Link HF paper page
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nielsr HF Staff - opened
README.md
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---
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tags:
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- code
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base_model:
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- TechxGenus/CursorCore-Yi-1.5B-SR
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library_name: transformers
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pipeline_tag: text-generation
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license: apache-2.0
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---
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# CursorCore: Assist Programming through Aligning Anything
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<a href="https://hf.co/papers/2410.07002">[🤗HF Paper]</a> |
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<a href="https://huggingface.co/collections/TechxGenus/cursorcore-series-6706618c38598468866b60e2">[🤖Models]</a> |
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<a href="https://github.com/TechxGenus/CursorCore">[🛠️Code]</a> |
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<a href="https://github.com/TechxGenus/CursorWeb">[Web]</a> |
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<a href="https://discord.gg/Z5Tev8fV">[Discord]</a>
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</p>
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<hr>
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- [CursorCore: Assist Programming through Aligning Anything](#cursorcore-assist-programming-through-aligning-anything)
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- [Introduction](#introduction)
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- [Models](#models)
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- [Usage](#usage)
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- [1) Normal chat](#1-normal-chat)
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- [3) Web Demo](#3-web-demo)
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- [Future Work](#future-work)
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- [Citation](#citation)
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- [Contribution](#contribution)
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<hr>
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## Models
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Our models have been open-sourced on Hugging Face. You can access our models here: [CursorCore-Series](https://huggingface.co/collections/TechxGenus/cursorcore-series-6706618c38598468866b60e2"). We also provide pre-quantized weights for GPTQ and AWQ here: [CursorCore-Quantization](https://huggingface.co/collections/TechxGenus/cursorcore-quantization-67066431f29f252494ee8cf3)
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## Usage
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Here are some examples of how to use our model:
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{
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"type": "code",
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"lang": "python",
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"code": """def quick_sort(arr)
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}
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],
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"current": {
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"type": "code",
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"lang": "python",
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"code": """def quick_sort(array)
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},
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"user": ""
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}
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"current": {
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"type": "code",
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"lang": "python",
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"code": """def quick_sort(array)
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},
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"user": "Add Docstring."
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}
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"current": {
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"type": "code",
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"lang": "python",
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"code": """def quick_sort(array)
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},
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"user": "Add Docstring."
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}
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"current": {
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"type": "code",
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"lang": "python",
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"code": """def quick_sort(array)
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},
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"user": "Add Docstring."
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}
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}
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```
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## Contribution
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Contributions are welcome! If you find any bugs or have suggestions for improvements, please open an issue or submit a pull request.
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---
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base_model:
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- TechxGenus/CursorCore-Yi-1.5B-SR
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library_name: transformers
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license: apache-2.0
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pipeline_tag: text-generation
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tags:
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- code
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---
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# CursorCore: Assist Programming through Aligning Anything
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<a href="https://hf.co/papers/2410.07002">[🤗HF Paper]</a> |
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<a href="https://huggingface.co/collections/TechxGenus/cursorcore-series-6706618c38598468866b60e2">[🤖Models]</a> |
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<a href="https://github.com/TechxGenus/CursorCore">[🛠️Code]</a> |
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<a href="https://github.com/TechxGenus/CursorWeb">[<img src="https://github.com/TechxGenus/CursorCore/blob/main/pictures/cursorcore.png" width="12.5px">Web]</a> |
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<a href="https://discord.gg/Z5Tev8fV">[<img src="https://github.com/TechxGenus/CursorCore/blob/main/pictures/discord.png" width="15x">Discord]</a>
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</p>
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<hr>
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- [CursorCore: Assist Programming through Aligning Anything](#cursorcore-assist-programming-through-aligning-anything)
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- [Introduction](#introduction)
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- [Structure](#structure)
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- [Models](#models)
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- [Usage](#usage)
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- [1) Normal chat](#1-normal-chat)
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- [3) Web Demo](#3-web-demo)
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- [Future Work](#future-work)
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- [Citation](#citation)
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- [Acknowledgements](#acknowledgements)
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- [Contribution](#contribution)
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<hr>
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## Structure
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- `./benchmark` contains the APEval benchmark
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- `./data` contains code to preprocess datasets
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- `./eval` contains code to evaluate models
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- `./gen` contains code to prompt LLMs for generation
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- `./generic` common functions, tools and special tokens
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- `./src` contains code about Programming-Instruct
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- `./train` contains code for training CursorCore
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Please ensure all dependencies are installed using the following command:
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```bash
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pip install -r requirements.txt
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```
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We also use [flash-attention](https://github.com/Dao-AILab/flash-attention) for efficient training and [flashinfer](https://github.com/flashinfer-ai/flashinfer) to accelerate inference. See the documents for them to learn how to install.
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## Models
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Our models have been open-sourced on Hugging Face. You can access our models here: [CursorCore-Series](https://huggingface.co/collections/TechxGenus/cursorcore-series-6706618c38598468866b60e2"). We also provide pre-quantized weights for GPTQ and AWQ here: [CursorCore-Quantization](https://huggingface.co/collections/TechxGenus/cursorcore-quantization-67066431f29f252494ee8cf3)
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We use the manually written benchmark APEval to assess the model's ability to assist programming. We also utilize [EvalPlus](https://github.com/evalplus/evalplus), [CanItEdit](https://github.com/nuprl/CanItEdit) and [OctoPack](https://github.com/bigcode-project/octopack) to evaluate the model's performance in Python program generation, instructional code editing, and automated program repair. Since we use a custom conversation template, its generation method differs significantly from both instruct models and base models. Please refer to [our paper](http://arxiv.org/abs/2410.07002) for more details.
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Evaluation results on APEval:
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<img src="https://github.com/TechxGenus/CursorCore/blob/main/pictures/APEval.png" alt="APEval" width="75%"/>
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Evaluation results on EvalPlus, CanItEdit and OctoPack:
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<img src="https://github.com/TechxGenus/CursorCore/blob/main/pictures/EvalPlus_CanItEdit_OctoPack.png" alt="EvalPlus_CanItEdit_OctoPack" width="75%">
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## Usage
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Here are some examples of how to use our model:
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{
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"type": "code",
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"lang": "python",
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"code": """def quick_sort(arr):
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if len(arr) <= 1:
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return arr
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pivot = arr[len(arr) // 2]
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left = [x for x in arr if x < pivot]
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middle = [x for x in arr if x == pivot]
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right = [x for x in arr if x > pivot]
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return quick_sort(left) + middle + quick_sort(right)"""
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}
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],
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"current": {
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"type": "code",
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"lang": "python",
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"code": """def quick_sort(array):
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if len(arr) <= 1:
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return arr
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pivot = arr[len(arr) // 2]
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left = [x for x in arr if x < pivot]
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middle = [x for x in arr if x == pivot]
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right = [x for x in arr if x > pivot]
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return quick_sort(left) + middle + quick_sort(right)"""
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},
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"user": ""
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}
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"current": {
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"type": "code",
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"lang": "python",
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"code": """def quick_sort(array):
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if len(arr) <= 1:
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return arr
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pivot = arr[len(arr) // 2]
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left = [x for x in arr if x < pivot]
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middle = [x for x in arr if x == pivot]
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right = [x for x in arr if x > pivot]
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return quick_sort(left) + middle + quick_sort(right)"""
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},
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"user": "Add Docstring."
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}
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"current": {
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"type": "code",
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"lang": "python",
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"code": """def quick_sort(array):
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if len(arr) <= 1:
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return arr
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pivot = arr[len(arr) // 2]
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left = [x for x in arr if x < pivot]
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middle = [x for x in arr if x == pivot]
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right = [x for x in arr if x > pivot]
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return quick_sort(left) + middle + quick_sort(right)"""
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},
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"user": "Add Docstring."
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}
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"current": {
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"type": "code",
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"lang": "python",
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"code": """def quick_sort(array):
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if len(arr) <= 1:
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return arr
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pivot = arr[len(arr) // 2]
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left = [x for x in arr if x < pivot]
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middle = [x for x in arr if x == pivot]
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right = [x for x in arr if x > pivot]
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return quick_sort(left) + middle + quick_sort(right)"""
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},
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"user": "Add Docstring."
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}
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}
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```
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## Acknowledgements
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The open-source community has been of great help to us, and we reference numerous projects and applications. They include but are not limited to:
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[Deepseek-Coder](https://github.com/deepseek-ai/DeepSeek-Coder), [Yi-Coder](https://github.com/01-ai/Yi-Coder), [Qwen-Coder](https://github.com/QwenLM/Qwen2.5-Coder), [Self-Instruct](https://github.com/yizhongw/self-instruct), [Evol-Instruct](https://github.com/theblackcat102/evol-dataset), [OSS-Instruct](https://github.com/ise-uiuc/magicoder), [EvalPlus](https://github.com/evalplus/evalplus), [CanItEdit](https://github.com/nuprl/CanItEdit), [OctoPack](https://github.com/bigcode-project/octopack), [Aider](https://github.com/Aider-AI/aider), [Continue](https://github.com/continuedev/continue), [Cursor](https://github.com/getcursor/cursor), ...
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## Contribution
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Contributions are welcome! If you find any bugs or have suggestions for improvements, please open an issue or submit a pull request.
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