commit 044cec1b572dfb28dc70c523b88263ac7bbf5d5d Author: ModelHub XC Date: Fri Sep 25 13:35:18 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: RichardErkhov/TechxGenus_-_CursorCore-Yi-1.5B-SR-gguf Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..3544300 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,54 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs 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Programming through Aligning Anything](https://huggingface.co/papers/2410.07002). + +Original model card: https://huggingface.co/TechxGenus/CursorCore-Yi-1.5B-SR +Code repository: https://github.com/TechxGenus/CursorCore + +Quantization made by Richard Erkhov. + +- [Github](https://github.com/RichardErkhov) +- [Discord](https://discord.gg/pvy7H8DZMG) +- [Request more models](https://github.com/RichardErkhov/quant_request) + + +CursorCore-Yi-1.5B-SR - GGUF +- Model creator: https://huggingface.co/TechxGenus/ +- Original model: https://huggingface.co/TechxGenus/CursorCore-Yi-1.5B-SR/ + + +| Name | Quant method | Size | +| ---- | ---- | ---- | +| [CursorCore-Yi-1.5B-SR.Q2_K.gguf](https://huggingface.co/RichardErkhov/TechxGenus_-_CursorCore-Yi-1.5B-SR-gguf/blob/main/CursorCore-Yi-1.5B-SR.Q2_K.gguf) | Q2_K | 0.59GB | +| [CursorCore-Yi-1.5B-SR.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/TechxGenus_-_CursorCore-Yi-1.5B-SR-gguf/blob/main/CursorCore-Yi-1.5B-SR.Q3_K_S.gguf) | Q3_K_S | 0.67GB | +| [CursorCore-Yi-1.5B-SR.Q3_K.gguf](https://huggingface.co/RichardErkhov/TechxGenus_-_CursorCore-Yi-1.5B-SR-gguf/blob/main/CursorCore-Yi-1.5B-SR.Q3_K.gguf) | Q3_K | 0.73GB | +| [CursorCore-Yi-1.5B-SR.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/TechxGenus_-_CursorCore-Yi-1.5B-SR-gguf/blob/main/CursorCore-Yi-1.5B-SR.Q3_K_M.gguf) | Q3_K_M | 0.73GB | +| [CursorCore-Yi-1.5B-SR.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/TechxGenus_-_CursorCore-Yi-1.5B-SR-gguf/blob/main/CursorCore-Yi-1.5B-SR.Q3_K_L.gguf) | Q3_K_L | 0.77GB | +| [CursorCore-Yi-1.5B-SR.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/TechxGenus_-_CursorCore-Yi-1.5B-SR-gguf/blob/main/CursorCore-Yi-1.5B-SR.IQ4_XS.gguf) | IQ4_XS | 0.78GB | +| [CursorCore-Yi-1.5B-SR.Q4_0.gguf](https://huggingface.co/RichardErkhov/TechxGenus_-_CursorCore-Yi-1.5B-SR-gguf/blob/main/CursorCore-Yi-1.5B-SR.Q4_0.gguf) | Q4_0 | 0.81GB | +| [CursorCore-Yi-1.5B-SR.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/TechxGenus_-_CursorCore-Yi-1.5B-SR-gguf/blob/main/CursorCore-Yi-1.5B-SR.IQ4_NL.gguf) | IQ4_NL | 0.81GB | +| [CursorCore-Yi-1.5B-SR.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/TechxGenus_-_CursorCore-Yi-1.5B-SR-gguf/blob/main/CursorCore-Yi-1.5B-SR.Q4_K_S.gguf) | Q4_K_S | 0.84GB | +| [CursorCore-Yi-1.5B-SR.Q4_K.gguf](https://huggingface.co/RichardErkhov/TechxGenus_-_CursorCore-Yi-1.5B-SR-gguf/blob/main/CursorCore-Yi-1.5B-SR.Q4_K.gguf) | Q4_K | 0.9GB | +| [CursorCore-Yi-1.5B-SR.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/TechxGenus_-_CursorCore-Yi-1.5B-SR-gguf/blob/main/CursorCore-Yi-1.5B-SR.Q4_K_M.gguf) | Q4_K_M | 0.9GB | +| [CursorCore-Yi-1.5B-SR.Q4_1.gguf](https://huggingface.co/RichardErkhov/TechxGenus_-_CursorCore-Yi-1.5B-SR-gguf/blob/main/CursorCore-Yi-1.5B-SR.Q4_1.gguf) | Q4_1 | 0.89GB | +| [CursorCore-Yi-1.5B-SR.Q5_0.gguf](https://huggingface.co/RichardErkhov/TechxGenus_-_CursorCore-Yi-1.5B-SR-gguf/blob/main/CursorCore-Yi-1.5B-SR.Q5_0.gguf) | Q5_0 | 0.96GB | +| [CursorCore-Yi-1.5B-SR.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/TechxGenus_-_CursorCore-Yi-1.5B-SR-gguf/blob/main/CursorCore-Yi-1.5B-SR.Q5_K_S.gguf) | Q5_K_S | 0.98GB | +| [CursorCore-Yi-1.5B-SR.Q5_K.gguf](https://huggingface.co/RichardErkhov/TechxGenus_-_CursorCore-Yi-1.5B-SR-gguf/blob/main/CursorCore-Yi-1.5B-SR.Q5_K.gguf) | Q5_K | 1.02GB | +| [CursorCore-Yi-1.5B-SR.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/TechxGenus_-_CursorCore-Yi-1.5B-SR-gguf/blob/main/CursorCore-Yi-1.5B-SR.Q5_K_M.gguf) | Q5_K_M | 1.02GB | +| [CursorCore-Yi-1.5B-SR.Q5_1.gguf](https://huggingface.co/RichardErkhov/TechxGenus_-_CursorCore-Yi-1.5B-SR-gguf/blob/main/CursorCore-Yi-1.5B-SR.Q5_1.gguf) | Q5_1 | 1.04GB | +| [CursorCore-Yi-1.5B-SR.Q6_K.gguf](https://huggingface.co/RichardErkhov/TechxGenus_-_CursorCore-Yi-1.5B-SR-gguf/blob/main/CursorCore-Yi-1.5B-SR.Q6_K.gguf) | Q6_K | 1.19GB | +| [CursorCore-Yi-1.5B-SR.Q8_0.gguf](https://huggingface.co/RichardErkhov/TechxGenus_-_CursorCore-Yi-1.5B-SR-gguf/blob/main/CursorCore-Yi-1.5B-SR.Q8_0.gguf) | Q8_0 | 1.46GB | + + + + +Original model description: +--- +tags: +- code +base_model: +- 01-ai/Yi-Coder-1.5B +library_name: transformers +pipeline_tag: text-generation +license: apache-2.0 +--- + +# CursorCore: Assist Programming through Aligning Anything + +

+[📄arXiv] | +[🤗HF Paper] | +[🤖Models] | +[🛠️Code] | +[Web] | +[Discord] +

+ +
+ +- [CursorCore: Assist Programming through Aligning Anything](#cursorcore-assist-programming-through-aligning-anything) + - [Introduction](#introduction) + - [Models](#models) + - [Usage](#usage) + - [1) Normal chat](#1-normal-chat) + - [2) Assistant-Conversation](#2-assistant-conversation) + - [3) Web Demo](#3-web-demo) + - [Future Work](#future-work) + - [Citation](#citation) + - [Contribution](#contribution) + +
+ +## Introduction + +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. + +

+conversation +

+ +![CursorWeb](https://raw.githubusercontent.com/TechxGenus/CursorCore/main/pictures/CursorWeb.gif) + +## Models + +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) + +## Usage + +Here are some examples of how to use our model: + +### 1) Normal chat + +Script: + +````python +import torch +from transformers import AutoTokenizer, AutoModelForCausalLM + +tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-9B") +model = AutoModelForCausalLM.from_pretrained( + "TechxGenus/CursorCore-Yi-9B", + torch_dtype=torch.bfloat16, + device_map="auto" +) + +messages = [ + {"role": "user", "content": "Hi!"}, +] +prompt = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True +) + +inputs = tokenizer.encode(prompt, return_tensors="pt") +outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=512) +print(tokenizer.decode(outputs[0])) +```` + +Output: + +````txt +<|im_start|>system +You are a helpful programming assistant.<|im_end|> +<|im_start|>user +Hi!<|im_end|> +<|im_start|>assistant +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|> +```` + +### 2) Assistant-Conversation + +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. + +Script 1: + +````python +import torch +from transformers import AutoTokenizer, AutoModelForCausalLM +from eval.utils import prepare_input_for_wf + +tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-9B") +model = AutoModelForCausalLM.from_pretrained( + "TechxGenus/CursorCore-Yi-9B", + torch_dtype=torch.bfloat16, + device_map="auto" +) +sample = { + "history": [ + { + "type": "code", + "lang": "python", + "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)""" + } + ], + "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": "" +} + +prompt = tokenizer.apply_chat_template( + prepare_input_for_wf(sample), + tokenize=False, + chat_template="assistant-conversation", + add_generation_prompt=True +) + +inputs = tokenizer.encode(prompt, return_tensors="pt") +outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=512, do_sample=False) +print(tokenizer.decode(outputs[0])) +```` + +Output 1: + +````txt +<|im_start|>system +You are a helpful programming assistant.<|im_end|> +<|im_start|>history +```python +def quick_sort(arr): + 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|>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|>assistant +<|next_start|>```python +def quick_sort(array): + if len(array) <= 1: + return array + pivot = array[len(array) // 2] + left = [x for x in array if x < pivot] + middle = [x for x in array if x == pivot] + right = [x for x in array if x > pivot] + return quick_sort(left) + middle + quick_sort(right) +```<|next_end|> +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. + +To implement this, we will: +1. Update the parameter name in the function definition from `arr` to `array`. +2. Ensure that all references to `arr` within the function are updated to `array`. + +This will make the function definition and internal references consistent, improving code readability and maintainability.<|im_end|> +```` + +Script 2: + +````python +import torch +from transformers import AutoTokenizer, AutoModelForCausalLM +from eval.utils import prepare_input_for_wf + +tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-9B") +model = AutoModelForCausalLM.from_pretrained( + "TechxGenus/CursorCore-Yi-9B", + 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_wf(sample), + tokenize=False, + chat_template="assistant-conversation", + add_generation_prompt=True +) + +inputs = tokenizer.encode(prompt, return_tensors="pt") +outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=512, do_sample=False) +print(tokenizer.decode(outputs[0])) +```` + +Output 2: + +````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): + """ + This is an implementation of the quick sort algorithm. + """ + 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) +```<|next_end|><|im_end|> +```` + +For models in Locate-and-Change (LC) and Search-and-Replace (SR) formats, the output examples are as follows: + +Script for LC: + +````python +import torch +from transformers import AutoTokenizer, AutoModelForCausalLM +from eval.utils import prepare_input_for_lc + +tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-1.5B-LC") +model = AutoModelForCausalLM.from_pretrained( + "TechxGenus/CursorCore-Yi-1.5B-LC", + 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_lc(sample), + tokenize=False, + chat_template="assistant-conversation", + add_generation_prompt=True +) + +inputs = tokenizer.encode(prompt, return_tensors="pt") +outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=512, do_sample=False) +print(tokenizer.decode(outputs[0])) +```` + +Output for LC: + +````txt +<|im_start|>system +You are a helpful programming assistant.<|im_end|> +<|im_start|>current +```python +1 def quick_sort(array): +2 if len(arr) <= 1: +3 return arr +4 pivot = arr[len(arr) // 2] +5 left = [x for x in arr if x < pivot] +6 middle = [x for x in arr if x == pivot] +7 right = [x for x in arr if x > pivot] +8 return quick_sort(left) + middle + quick_sort(right) +```<|im_end|> +<|im_start|>user +Add Docstring.<|im_end|> +<|im_start|>assistant +<|next_start|>1,1 +``` + '''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 +from transformers import AutoTokenizer, AutoModelForCausalLM +from eval.utils import prepare_input_for_sr + +tokenizer = AutoTokenizer.from_pretrained("TechxGenus/CursorCore-Yi-1.5B-SR") +model = AutoModelForCausalLM.from_pretrained( + "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") +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} +} +``` + +## Contribution + +Contributions are welcome! If you find any bugs or have suggestions for improvements, please open an issue or submit a pull request. + +