bunyaminergen
/

Qwen2.5-Coder-1.5B-Instruct-SFT

@@ -4,206 +4,226 @@ base_model_relation: finetune
 library_name: peft
 language:
 - en
-thumbnail: url to a thumbnail used in social sharing
 tags:
 - code
 license: gpl-3.0
 datasets:
 - bunyaminergen/Stable-Code-Python-SFT
 pipeline_tag: text-generation
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More
-information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:
-  ** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute)
-presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]
-### Framework versions
-- PEFT 0.15.1

 library_name: peft
 language:
 - en
 tags:
 - code
+- codeqwen
+- chat
+- qwen
+- qwen-coder
 license: gpl-3.0
 datasets:
 - bunyaminergen/Stable-Code-Python-SFT
 pipeline_tag: text-generation
+license_link: https://huggingface.co/bunyaminergen/Qwen2.5-Coder-1.5B-Instruct-SFT/blob/main/LICENSE
 ---
+# Qwen2.5-Coder-1.5B-Instruct-SFT
+The Qwen2.5-Coder-1.5B-Instruct-SFT model has been fine-tuned using Supervised Fine Tuning (SFT) method specifically to
+enhance its performance in generating docstrings, a critical task in coding. Currently, it generates docstrings
+following the Google style guide. For differences compared to the base (pre-trained) model, please refer to
+the [Comparison](#comparison) section.
+**Note**: _If you would like to contribute to this repository,
+please read the [CONTRIBUTING](CONTRIBUTING.md) first._
+---
+### TableofContents
+- [Usage](#usage)
+- [Comparison](#comparison)
+- [Dataset](#dataset)
+- [Documentations](#documentations)
+- [License](#licence)
+- [Links](#links)
+- [Team](#team)
+- [Contact](#contact)
+- [Citation](#citation)
+### Usage
+```python
+from wavlmmsdd.audio.diarization.diarize import Diarizer
+from wavlmmsdd.audio.feature.embedding import WavLMSV
+from wavlmmsdd.audio.preprocess.resample import Resample
+from wavlmmsdd.audio.preprocess.convert import Convert
+from wavlmmsdd.audio.utils.utils import Build
+def main():
+    # Audio Path
+    audio_path = "ae.wav"
+    # Resample to 16000 Khz
+    resampler = Resample(audio_file=audio_path)
+    wave_16k, sr_16k = resampler.to_16k()
+    # Convert to Mono
+    converter = Convert(waveform=wave_16k, sample_rate=sr_16k)
+    converter.to_mono()
+    saved_path = converter.save()
+    # Build Manifest File
+    builder = Build(saved_path)
+    manifest_path = builder.manifest()
+    # Embedding
+    embedder = WavLMSV()
+    # Diarization
+    diarizer = Diarizer(embedding=embedder, manifest_path=manifest_path)
+    diarizer.run()
+if __name__ == "__main__":
+    main()
+```
+---
+### Comparison
+##### Base Model
+`System:` You are a Python programmer.
+`User:` Give me a quick an example of bubble sort in Python.
+`Assistant:` Bubble Sort is a simple sorting algorithm that repeatedly steps through the list, compares adjacent
+elements and swaps them if they are in the wrong order. The pass through the list is repeated until the list is sorted.
+Here's a basic implementation of Bubble Sort in Python:
+```python
+def bubble_sort(arr):
+    n = len(arr)
+    # Traverse through all array elements
+    for i in range(n):
+        # Last i elements are already in place
+        for j in range(0, n - i - 1):
+            # Swap if the element found is greater than the next element
+            if arr[j] > arr[j + 1]:
+                arr[j], arr[j + 1] = arr[j + 1], arr[j]
+# Example usage:
+arr = [64, 34, 25, 12, 22, 11, 90]
+bubble_sort(arr)
+print("Sorted array:", arr)
+```
+In this example:
+- We define a function `bubble_sort` that takes an array as input.
+- Inside the function, we use two nested loops to iterate over each pair of adjacent elements in the array.
+- If the current element (`arr[j]`) is greater than the next element (`arr[j+1]`), we swap them.
+- This process continues until no more swaps are needed, indicating that the array is sorted.
+- Finally, we demonstrate how to use the `bubble_sort` function by sorting an array and printing the sorted result.
+Bubble Sort has a time complexity of O(n^2) in the worst case scenario, making it less efficient than other sorting
+algorithms like QuickSort or MergeSort. However, it is easy to understand and implement, which makes it suitable for
+small data sets or educational purposes.
+##### Supervised Fine Tuned Model
+`System:` You are a Python programmer.
+`User:` Give me a quick an example of bubble sort in Python.
+`Assistant:`  Here's a simple implementation of the Bubble Sort algorithm in Python, along with its docstring following
+Google Python Style Guide:
+```python
+def bubble_sort(arr):
+    """
+    Bubble sorts an array of integers in ascending order.
+    Args:
+        arr (list): A list of integers to be sorted.
+    Returns:
+        list: The sorted list of integers.
+    Raises:
+        TypeError: If the input is not a list or contains non-integer elements.
+    Examples:
+        >>> bubble_sort([5, 3, 1, 4, 6])
+        [1, 3, 4, 5, 6]
+    """
+    # Check if the input is a list and contains only integers
+    if not isinstance(arr, list) or not all(isinstance(x, int) for x in arr):
+        raise TypeError("Input must be a list of integers.")
+    n = len(arr)
+    # Traverse through all array elements
+    for i in range(n):
+        # Last i elements are already in place
+        for j in range(0, n - i - 1):
+            # Traverse the array from 0 to n-i-1
+            # Swap if the element found is greater than the next element
+            if arr[j] > arr[j + 1]:
+                arr[j], arr[j + 1] = arr[j + 1], arr[j]
+    return arr
+```
+This implementation follows the Bubble Sort algorithm, which repeatedly steps through the list, compares adjacent
+elements, and swaps them if they are in the wrong order. The pass through the list is repeated until the list is sorted.
+This implementation includes error handling and examples for usage.
+---
+### Dataset
+- [bunyaminergen/Stable-Code-Python-SFT](https://huggingface.co/datasets/bunyaminergen/Stable-Code-Python-SFT)
+---
+### Documentations
+- [CONTRIBUTING](CONTRIBUTING.md)
+---
+### Licence
+- [LICENSE](LICENSE)
+---
+### Links
+- [Github](https://github.com/bunyaminergen/)
+- [Website](https://bunyaminergen.com)
+- [Linkedin](https://www.linkedin.com/in/bunyaminergen)
+---
+### Team
+- [Bunyamin Ergen](https://www.linkedin.com/in/bunyaminergen)
+---
+### Contact
+- [Mail](mailto:[email protected])
+---
+### Citation
+```bibtex
+@software{       Qwen2.5-Coder-1.5B-Instruct-SFT,
+  author       = {Bunyamin Ergen},
+  title        = {{Qwen2.5-Coder-1.5B-Instruct-SFT}},
+  year         = {2025},
+  month        = {04},
+}
+```
+---