Update README.md

README.md

@@ -13,6 +13,379 @@ license: apache-2.0
 This model was converted to GGUF format from [`TechxGenus/CursorCore-Yi-1.5B`](https://huggingface.co/TechxGenus/CursorCore-Yi-1.5B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/TechxGenus/CursorCore-Yi-1.5B) for more details on the model.
 
+---
+Model details:
+-
+CursorCore: Assist Programming through Aligning Anything
+
+[📄arXiv] | [🤗HF Paper] | [🤖Models] | [🛠️Code] | [Web] | [Discord]
+
+    CursorCore: Assist Programming through Aligning Anything
+        Introduction
+        Models
+        Usage
+            1) Normal chat
+            2) Assistant-Conversation
+            3) Web Demo
+        Future Work
+        Citation
+        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 to learn more.
+
+conversation
+
+CursorWeb
+Models
+
+Our models have been open-sourced on Hugging Face. You can access our models here: CursorCore-Series. We also provide pre-quantized weights for GPTQ and AWQ here: CursorCore-Quantization
+Usage
+
+Here are some examples of how to use our model:
+1) Normal chat
+
+Script:
+
+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:
+
+<|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:
+
+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:
+
+<|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:
+
+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:
+
+<|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:
+
+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:
+
+<|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:
+
+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:
+
+<|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 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
+
+@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.
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)