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README.md
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README.md
@@ -1,47 +1,417 @@
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---
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license: Apache License 2.0
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#model-type:
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##如 gpt、phi、llama、chatglm、baichuan 等
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#- gpt
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#domain:
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##如 nlp、cv、audio、multi-modal
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#- nlp
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#language:
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##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
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#- cn
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#metrics:
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##如 CIDEr、Blue、ROUGE 等
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#- CIDEr
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#tags:
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##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
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#- pretrained
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#tools:
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##如 vllm、fastchat、llamacpp、AdaSeq 等
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#- vllm
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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-LC
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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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### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重，可浏览“模型文件”页面获取。
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#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型
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SDK下载
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```bash
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#安装ModelScope
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pip install modelscope
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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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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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## 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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### 1) Normal chat
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Script:
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````python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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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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````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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Script 1:
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````python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from eval.utils import prepare_input_for_wf
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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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sample = {
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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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},
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"user": ""
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}
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prompt = tokenizer.apply_chat_template(
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prepare_input_for_wf(sample),
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tokenize=False,
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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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<|im_start|>history
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```python
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#SDK模型下载
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from modelscope import snapshot_download
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model_dir = snapshot_download('TechxGenus-MS/CursorCore-Yi-1.5B-LC-AWQ')
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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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```<|im_end|>
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<|im_start|>current
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```python
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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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```<|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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if len(array) <= 1:
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return array
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pivot = array[len(array) // 2]
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left = [x for x in array if x < pivot]
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middle = [x for x in array if x == pivot]
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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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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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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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2. Ensure that all references to `arr` within the function are updated to `array`.
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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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Script 2:
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````python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from eval.utils import prepare_input_for_wf
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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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sample = {
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"history": [],
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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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},
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"user": "Add Docstring."
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}
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prompt = tokenizer.apply_chat_template(
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prepare_input_for_wf(sample),
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tokenize=False,
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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 2:
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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|>current
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```python
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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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```<|im_end|>
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<|im_start|>user
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Add Docstring.<|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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"""
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This is an implementation of the quick sort algorithm.
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"""
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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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```<|next_end|><|im_end|>
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````
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For models in Locate-and-Change (LC) and Search-and-Replace (SR) formats, the output examples are as follows:
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Script for LC:
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````python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from eval.utils import prepare_input_for_lc
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tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-1.5B-LC")
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model = AutoModelForCausalLM.from_pretrained(
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"TechxGenus/CursorCore-Yi-1.5B-LC",
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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sample = {
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"history": [],
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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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},
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"user": "Add Docstring."
|
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}
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prompt = tokenizer.apply_chat_template(
|
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prepare_input_for_lc(sample),
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tokenize=False,
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chat_template="assistant-conversation",
|
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add_generation_prompt=True
|
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)
|
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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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|
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Output for LC:
|
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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|>current
|
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```python
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1 def quick_sort(array):
|
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2 if len(arr) <= 1:
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3 return arr
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4 pivot = arr[len(arr) // 2]
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5 left = [x for x in arr if x < pivot]
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6 middle = [x for x in arr if x == pivot]
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7 right = [x for x in arr if x > pivot]
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8 return quick_sort(left) + middle + quick_sort(right)
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```<|im_end|>
|
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<|im_start|>user
|
||||
Add Docstring.<|im_end|>
|
||||
<|im_start|>assistant
|
||||
<|next_start|>1,1
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```
|
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Git下载
|
||||
```
|
||||
#Git模型下载
|
||||
git clone https://www.modelscope.cn/TechxGenus-MS/CursorCore-Yi-1.5B-LC-AWQ.git
|
||||
'''This function will sort an array using quick sort algorithm'''
|
||||
```<|next_end|>
|
||||
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.
|
||||
|
||||
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.
|
||||
|
||||
Here's the plan:
|
||||
1. Add a docstring at the beginning of the `quick_sort` function.
|
||||
2. Ensure the docstring is clear and concise, describing the purpose of the function.
|
||||
|
||||
This modification will improve the code's documentation without altering its functionality.<|im_end|>
|
||||
````
|
||||
|
||||
Script for SR:
|
||||
|
||||
````python
|
||||
import torch
|
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from transformers import AutoTokenizer, AutoModelForCausalLM
|
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from eval.utils import prepare_input_for_sr
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-1.5B-SR")
|
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model = AutoModelForCausalLM.from_pretrained(
|
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"TechxGenus/CursorCore-Yi-1.5B-SR",
|
||||
torch_dtype=torch.bfloat16,
|
||||
device_map="auto"
|
||||
)
|
||||
sample = {
|
||||
"history": [],
|
||||
"current": {
|
||||
"type": "code",
|
||||
"lang": "python",
|
||||
"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)"""
|
||||
},
|
||||
"user": "Add Docstring."
|
||||
}
|
||||
|
||||
prompt = tokenizer.apply_chat_template(
|
||||
prepare_input_for_sr(sample),
|
||||
tokenize=False,
|
||||
chat_template="assistant-conversation",
|
||||
add_generation_prompt=True
|
||||
)
|
||||
|
||||
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)
|
||||
print(tokenizer.decode(outputs[0]))
|
||||
````
|
||||
|
||||
Output for SR:
|
||||
|
||||
````txt
|
||||
<|im_start|>system
|
||||
You are a helpful programming assistant.<|im_end|>
|
||||
<|im_start|>current
|
||||
```python
|
||||
def quick_sort(array):
|
||||
if len(arr) <= 1:
|
||||
return arr
|
||||
pivot = arr[len(arr) // 2]
|
||||
left = [x for x in arr if x < pivot]
|
||||
middle = [x for x in arr if x == pivot]
|
||||
right = [x for x in arr if x > pivot]
|
||||
return quick_sort(left) + middle + quick_sort(right)
|
||||
```<|im_end|>
|
||||
<|im_start|>user
|
||||
Add Docstring.<|im_end|>
|
||||
<|im_start|>assistant
|
||||
<|next_start|>```python
|
||||
def quick_sort(array):
|
||||
<|search_and_replace|>
|
||||
def quick_sort(array):
|
||||
"""
|
||||
This function implements quick sort algorithm
|
||||
"""
|
||||
```<|next_end|><|im_end|>
|
||||
````
|
||||
|
||||
### 3) Web Demo
|
||||
|
||||
We create a web demo for CursorCore. Please visit [CursorWeb](https://github.com/TechxGenus/CursorWeb) for more details.
|
||||
|
||||
## Future Work
|
||||
|
||||
CursorCore is still in a very early stage, and lots of work is needed to achieve a better user experience. For example:
|
||||
|
||||
- Repository-level editing support
|
||||
- Better and faster editing formats
|
||||
- Better user interface and presentation
|
||||
- ...
|
||||
|
||||
## Citation
|
||||
|
||||
```bibtex
|
||||
@article{jiang2024cursorcore,
|
||||
title = {CursorCore: Assist Programming through Aligning Anything},
|
||||
author = {Hao Jiang and Qi Liu and Rui Li and Shengyu Ye and Shijin Wang},
|
||||
year = {2024},
|
||||
journal = {arXiv preprint arXiv: 2410.07002}
|
||||
}
|
||||
```
|
||||
|
||||
<p style="color: lightgrey;">如果您是本模型的贡献者，我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>，及时完善模型卡片内容。</p>
|
||||
## Contribution
|
||||
|
||||
Contributions are welcome! If you find any bugs or have suggestions for improvements, please open an issue or submit a pull request.
|
||||
|
||||
Reference in New Issue
Block a user