RichardErkhov
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README.md
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1 |
+
Quantization made by Richard Erkhov.
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+
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+
[Github](https://github.com/RichardErkhov)
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+
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[Discord](https://discord.gg/pvy7H8DZMG)
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+
[Request more models](https://github.com/RichardErkhov/quant_request)
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CursorCore-QW2.5-1.5B - AWQ
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- Model creator: https://huggingface.co/TechxGenus/
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- Original model: https://huggingface.co/TechxGenus/CursorCore-QW2.5-1.5B/
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Original model description:
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---
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tags:
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- code
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base_model:
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- Qwen/Qwen2.5-Coder-1.5B
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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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<p align="center">
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<a href="http://arxiv.org/abs/2410.07002">[📄arXiv]</a> |
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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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- [2) Assistant-Conversation](#2-assistant-conversation)
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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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## Introduction
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+
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+
CursorCore is a series of open-source models designed for AI-assisted programming. It aims to support features such as automated editing and inline chat, replicating the core abilities of closed-source AI-assisted programming tools like Cursor. This is achieved by aligning data generated through Programming-Instruct. Please read [our paper](http://arxiv.org/abs/2410.07002) to learn more.
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<p align="center">
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<img width="100%" alt="conversation" src="https://raw.githubusercontent.com/TechxGenus/CursorCore/main/pictures/conversation.png">
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</p>
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![CursorWeb](https://raw.githubusercontent.com/TechxGenus/CursorCore/main/pictures/CursorWeb.gif)
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## Models
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+
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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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### 1) Normal chat
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Script:
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+
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````python
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+
import torch
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+
from transformers import AutoTokenizer, AutoModelForCausalLM
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+
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+
tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-9B")
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model = AutoModelForCausalLM.from_pretrained(
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"TechxGenus/CursorCore-Yi-9B",
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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messages = [
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{"role": "user", "content": "Hi!"},
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]
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prompt = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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inputs = tokenizer.encode(prompt, return_tensors="pt")
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outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=512)
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print(tokenizer.decode(outputs[0]))
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````
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Output:
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+
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````txt
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<|im_start|>system
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You are a helpful programming assistant.<|im_end|>
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<|im_start|>user
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Hi!<|im_end|>
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<|im_start|>assistant
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Hello! I'm an AI language model and I can help you with any programming questions you might have. What specific problem or task are you trying to solve?<|im_end|>
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````
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### 2) Assistant-Conversation
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In our work, we introduce a new framework of AI-assisted programming task. It is designed for aligning anything during programming process, used for the implementation of features like Tab and Inline Chat.
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+
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+
Script 1:
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117 |
+
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118 |
+
````python
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119 |
+
import torch
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120 |
+
from transformers import AutoTokenizer, AutoModelForCausalLM
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121 |
+
from eval.utils import prepare_input_for_wf
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122 |
+
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tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-9B")
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124 |
+
model = AutoModelForCausalLM.from_pretrained(
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125 |
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"TechxGenus/CursorCore-Yi-9B",
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126 |
+
torch_dtype=torch.bfloat16,
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127 |
+
device_map="auto"
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128 |
+
)
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129 |
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sample = {
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130 |
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"history": [
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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):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n 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):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quick_sort(left) + middle + quick_sort(right)"""
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141 |
+
},
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"user": ""
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143 |
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}
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144 |
+
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145 |
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prompt = tokenizer.apply_chat_template(
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146 |
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prepare_input_for_wf(sample),
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tokenize=False,
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148 |
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chat_template="assistant-conversation",
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add_generation_prompt=True
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)
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inputs = tokenizer.encode(prompt, return_tensors="pt")
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outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=512, do_sample=False)
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print(tokenizer.decode(outputs[0]))
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````
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Output 1:
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````txt
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<|im_start|>system
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+
You are a helpful programming assistant.<|im_end|>
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162 |
+
<|im_start|>history
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+
```python
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164 |
+
def quick_sort(arr):
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165 |
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if len(arr) <= 1:
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+
return arr
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167 |
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pivot = arr[len(arr) // 2]
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168 |
+
left = [x for x in arr if x < pivot]
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169 |
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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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```<|im_end|>
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173 |
+
<|im_start|>current
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+
```python
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175 |
+
def quick_sort(array):
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176 |
+
if len(arr) <= 1:
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return arr
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178 |
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pivot = arr[len(arr) // 2]
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179 |
+
left = [x for x in arr if x < pivot]
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180 |
+
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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+
```<|im_end|>
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<|im_start|>assistant
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<|next_start|>```python
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def quick_sort(array):
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187 |
+
if len(array) <= 1:
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+
return array
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189 |
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pivot = array[len(array) // 2]
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190 |
+
left = [x for x in array if x < pivot]
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191 |
+
middle = [x for x in array if x == pivot]
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192 |
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right = [x for x in array if x > pivot]
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return quick_sort(left) + middle + quick_sort(right)
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+
```<|next_end|>
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195 |
+
The user has provided a revised code change that corrects the parameter name from `arr` to `array` in the `quick_sort` function. This change ensures consistency in the function definition and avoids potential confusion or errors.
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196 |
+
|
197 |
+
To implement this, we will:
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+
1. Update the parameter name in the function definition from `arr` to `array`.
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199 |
+
2. Ensure that all references to `arr` within the function are updated to `array`.
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+
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This will make the function definition and internal references consistent, improving code readability and maintainability.<|im_end|>
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+
````
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203 |
+
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204 |
+
Script 2:
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205 |
+
|
206 |
+
````python
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207 |
+
import torch
|
208 |
+
from transformers import AutoTokenizer, AutoModelForCausalLM
|
209 |
+
from eval.utils import prepare_input_for_wf
|
210 |
+
|
211 |
+
tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-9B")
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212 |
+
model = AutoModelForCausalLM.from_pretrained(
|
213 |
+
"TechxGenus/CursorCore-Yi-9B",
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214 |
+
torch_dtype=torch.bfloat16,
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215 |
+
device_map="auto"
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216 |
+
)
|
217 |
+
sample = {
|
218 |
+
"history": [],
|
219 |
+
"current": {
|
220 |
+
"type": "code",
|
221 |
+
"lang": "python",
|
222 |
+
"code": """def quick_sort(array):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quick_sort(left) + middle + quick_sort(right)"""
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223 |
+
},
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224 |
+
"user": "Add Docstring."
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225 |
+
}
|
226 |
+
|
227 |
+
prompt = tokenizer.apply_chat_template(
|
228 |
+
prepare_input_for_wf(sample),
|
229 |
+
tokenize=False,
|
230 |
+
chat_template="assistant-conversation",
|
231 |
+
add_generation_prompt=True
|
232 |
+
)
|
233 |
+
|
234 |
+
inputs = tokenizer.encode(prompt, return_tensors="pt")
|
235 |
+
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=512, do_sample=False)
|
236 |
+
print(tokenizer.decode(outputs[0]))
|
237 |
+
````
|
238 |
+
|
239 |
+
Output 2:
|
240 |
+
|
241 |
+
````txt
|
242 |
+
<|im_start|>system
|
243 |
+
You are a helpful programming assistant.<|im_end|>
|
244 |
+
<|im_start|>current
|
245 |
+
```python
|
246 |
+
def quick_sort(array):
|
247 |
+
if len(arr) <= 1:
|
248 |
+
return arr
|
249 |
+
pivot = arr[len(arr) // 2]
|
250 |
+
left = [x for x in arr if x < pivot]
|
251 |
+
middle = [x for x in arr if x == pivot]
|
252 |
+
right = [x for x in arr if x > pivot]
|
253 |
+
return quick_sort(left) + middle + quick_sort(right)
|
254 |
+
```<|im_end|>
|
255 |
+
<|im_start|>user
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256 |
+
Add Docstring.<|im_end|>
|
257 |
+
<|im_start|>assistant
|
258 |
+
<|next_start|>```python
|
259 |
+
def quick_sort(array):
|
260 |
+
"""
|
261 |
+
This is an implementation of the quick sort algorithm.
|
262 |
+
"""
|
263 |
+
if len(arr) <= 1:
|
264 |
+
return arr
|
265 |
+
pivot = arr[len(arr) // 2]
|
266 |
+
left = [x for x in arr if x < pivot]
|
267 |
+
middle = [x for x in arr if x == pivot]
|
268 |
+
right = [x for x in arr if x > pivot]
|
269 |
+
return quick_sort(left) + middle + quick_sort(right)
|
270 |
+
```<|next_end|><|im_end|>
|
271 |
+
````
|
272 |
+
|
273 |
+
For models in Locate-and-Change (LC) and Search-and-Replace (SR) formats, the output examples are as follows:
|
274 |
+
|
275 |
+
Script for LC:
|
276 |
+
|
277 |
+
````python
|
278 |
+
import torch
|
279 |
+
from transformers import AutoTokenizer, AutoModelForCausalLM
|
280 |
+
from eval.utils import prepare_input_for_lc
|
281 |
+
|
282 |
+
tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-1.5B-LC")
|
283 |
+
model = AutoModelForCausalLM.from_pretrained(
|
284 |
+
"TechxGenus/CursorCore-Yi-1.5B-LC",
|
285 |
+
torch_dtype=torch.bfloat16,
|
286 |
+
device_map="auto"
|
287 |
+
)
|
288 |
+
sample = {
|
289 |
+
"history": [],
|
290 |
+
"current": {
|
291 |
+
"type": "code",
|
292 |
+
"lang": "python",
|
293 |
+
"code": """def quick_sort(array):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quick_sort(left) + middle + quick_sort(right)"""
|
294 |
+
},
|
295 |
+
"user": "Add Docstring."
|
296 |
+
}
|
297 |
+
|
298 |
+
prompt = tokenizer.apply_chat_template(
|
299 |
+
prepare_input_for_lc(sample),
|
300 |
+
tokenize=False,
|
301 |
+
chat_template="assistant-conversation",
|
302 |
+
add_generation_prompt=True
|
303 |
+
)
|
304 |
+
|
305 |
+
inputs = tokenizer.encode(prompt, return_tensors="pt")
|
306 |
+
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=512, do_sample=False)
|
307 |
+
print(tokenizer.decode(outputs[0]))
|
308 |
+
````
|
309 |
+
|
310 |
+
Output for LC:
|
311 |
+
|
312 |
+
````txt
|
313 |
+
<|im_start|>system
|
314 |
+
You are a helpful programming assistant.<|im_end|>
|
315 |
+
<|im_start|>current
|
316 |
+
```python
|
317 |
+
1 def quick_sort(array):
|
318 |
+
2 if len(arr) <= 1:
|
319 |
+
3 return arr
|
320 |
+
4 pivot = arr[len(arr) // 2]
|
321 |
+
5 left = [x for x in arr if x < pivot]
|
322 |
+
6 middle = [x for x in arr if x == pivot]
|
323 |
+
7 right = [x for x in arr if x > pivot]
|
324 |
+
8 return quick_sort(left) + middle + quick_sort(right)
|
325 |
+
```<|im_end|>
|
326 |
+
<|im_start|>user
|
327 |
+
Add Docstring.<|im_end|>
|
328 |
+
<|im_start|>assistant
|
329 |
+
<|next_start|>1,1
|
330 |
+
```
|
331 |
+
'''This function will sort an array using quick sort algorithm'''
|
332 |
+
```<|next_end|>
|
333 |
+
To enhance the readability and maintainability of the code, we should add a docstring to the `quick_sort` function. A docstring provides a clear description of what the function does, which is particularly useful for other developers who might use or modify this code in the future.
|
334 |
+
|
335 |
+
The docstring will be added immediately after the function definition, explaining that the function uses the quick sort algorithm to sort an array. This will make the code more self-explanatory and easier to understand.
|
336 |
+
|
337 |
+
Here's the plan:
|
338 |
+
1. Add a docstring at the beginning of the `quick_sort` function.
|
339 |
+
2. Ensure the docstring is clear and concise, describing the purpose of the function.
|
340 |
+
|
341 |
+
This modification will improve the code's documentation without altering its functionality.<|im_end|>
|
342 |
+
````
|
343 |
+
|
344 |
+
Script for SR:
|
345 |
+
|
346 |
+
````python
|
347 |
+
import torch
|
348 |
+
from transformers import AutoTokenizer, AutoModelForCausalLM
|
349 |
+
from eval.utils import prepare_input_for_sr
|
350 |
+
|
351 |
+
tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-1.5B-SR")
|
352 |
+
model = AutoModelForCausalLM.from_pretrained(
|
353 |
+
"TechxGenus/CursorCore-Yi-1.5B-SR",
|
354 |
+
torch_dtype=torch.bfloat16,
|
355 |
+
device_map="auto"
|
356 |
+
)
|
357 |
+
sample = {
|
358 |
+
"history": [],
|
359 |
+
"current": {
|
360 |
+
"type": "code",
|
361 |
+
"lang": "python",
|
362 |
+
"code": """def quick_sort(array):\n if len(arr) <= 1:\n return arr\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quick_sort(left) + middle + quick_sort(right)"""
|
363 |
+
},
|
364 |
+
"user": "Add Docstring."
|
365 |
+
}
|
366 |
+
|
367 |
+
prompt = tokenizer.apply_chat_template(
|
368 |
+
prepare_input_for_sr(sample),
|
369 |
+
tokenize=False,
|
370 |
+
chat_template="assistant-conversation",
|
371 |
+
add_generation_prompt=True
|
372 |
+
)
|
373 |
+
|
374 |
+
inputs = tokenizer.encode(prompt, return_tensors="pt")
|
375 |
+
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=512, do_sample=False)
|
376 |
+
print(tokenizer.decode(outputs[0]))
|
377 |
+
````
|
378 |
+
|
379 |
+
Output for SR:
|
380 |
+
|
381 |
+
````txt
|
382 |
+
<|im_start|>system
|
383 |
+
You are a helpful programming assistant.<|im_end|>
|
384 |
+
<|im_start|>current
|
385 |
+
```python
|
386 |
+
def quick_sort(array):
|
387 |
+
if len(arr) <= 1:
|
388 |
+
return arr
|
389 |
+
pivot = arr[len(arr) // 2]
|
390 |
+
left = [x for x in arr if x < pivot]
|
391 |
+
middle = [x for x in arr if x == pivot]
|
392 |
+
right = [x for x in arr if x > pivot]
|
393 |
+
return quick_sort(left) + middle + quick_sort(right)
|
394 |
+
```<|im_end|>
|
395 |
+
<|im_start|>user
|
396 |
+
Add Docstring.<|im_end|>
|
397 |
+
<|im_start|>assistant
|
398 |
+
<|next_start|>```python
|
399 |
+
def quick_sort(array):
|
400 |
+
<|search_and_replace|>
|
401 |
+
def quick_sort(array):
|
402 |
+
"""
|
403 |
+
This function implements quick sort algorithm
|
404 |
+
"""
|
405 |
+
```<|next_end|><|im_end|>
|
406 |
+
````
|
407 |
+
|
408 |
+
### 3) Web Demo
|
409 |
+
|
410 |
+
We create a web demo for CursorCore. Please visit [CursorWeb](https://github.com/TechxGenus/CursorWeb) for more details.
|
411 |
+
|
412 |
+
## Future Work
|
413 |
+
|
414 |
+
CursorCore is still in a very early stage, and lots of work is needed to achieve a better user experience. For example:
|
415 |
+
|
416 |
+
- Repository-level editing support
|
417 |
+
- Better and faster editing formats
|
418 |
+
- Better user interface and presentation
|
419 |
+
- ...
|
420 |
+
|
421 |
+
## Citation
|
422 |
+
|
423 |
+
```bibtex
|
424 |
+
@article{jiang2024cursorcore,
|
425 |
+
title = {CursorCore: Assist Programming through Aligning Anything},
|
426 |
+
author = {Hao Jiang and Qi Liu and Rui Li and Shengyu Ye and Shijin Wang},
|
427 |
+
year = {2024},
|
428 |
+
journal = {arXiv preprint arXiv: 2410.07002}
|
429 |
+
}
|
430 |
+
```
|
431 |
+
|
432 |
+
## Contribution
|
433 |
+
|
434 |
+
Contributions are welcome! If you find any bugs or have suggestions for improvements, please open an issue or submit a pull request.
|
435 |
+
|
436 |
+
|