metadata

language:
  - en
license: mit
library_name: transformers
tags:
  - code
base_model:
  - google/gemma-1.1-2b-it
datasets:
  - kreimben/leetcode_with_youtube_captions
  - kreimben/leetcode_user_submissions
widget:
  - text: >-
      explain about two sum problem. from brute force approach to the most
      advanced algorithms.
    example_title: two sum example
  - text: explain about leetcode 72 edit distance. i don't get even the approach.
    example_title: edit distance example
  - text: explain about leetcode 139 Word Break. please give me the approach.
    example_title: word break example
inference:
  parameters:
    max_new_tokens: 250
    temperature: 0.1
pipeline_tag: text-generation

CodeMind

Coding Test Explanatory LLM Model.

Model Details

Model Name: CodeMind
Base Model: gemma-1.1-7b-it
Fine-tuning Datasets:
- kreimben/leetcode_user_submissions_only_python
- kreimben/leetcode_with_youtube_captions
Model Type: Language Model
Language: English
License: gemma
Model Size: 8.54B

Intended Use

CodeMind is a fine-tuned language model specifically designed to assist users with coding test questions and provide programming education. It leverages the knowledge from LeetCode user submissions in Python and YouTube video captions related to LeetCode problems to offer guidance, explanations, and code examples.

Training Data

The model was fine-tuned using the following datasets:

kreimben/leetcode_user_submissions_only_python: This dataset contains a collection of LeetCode user submissions written in Python. It provides a wide range of coding solutions to various algorithmic problems commonly encountered in coding interviews.
kreimben/leetcode_with_youtube_captions: This dataset combines LeetCode problems with corresponding YouTube video captions. The captions provide explanations, thought processes, and step-by-step guidance for solving the coding problems.

Training Procedure

The model was fine-tuned using the Hugging Face Transformer library. The base model, gemma-1.1-2b-it, was further trained on the combined dataset of LeetCode user submissions and YouTube video captions. The fine-tuning process aimed to enhance the model's understanding of coding concepts, problem-solving strategies, and its ability to generate relevant code snippets and explanations.

Evaluation

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Bias and Limitations

The model's knowledge is primarily based on the LeetCode user submissions and YouTube video captions used for fine-tuning. It may have limitations in handling coding problems or concepts that are not well-represented in the training data.
The model's responses are generated based on patterns and information learned from the training data. It may sometimes produce incorrect or suboptimal solutions. Users should always review and verify the generated code before using it in practice.
The model may exhibit biases present in the training data, such as favoring certain programming styles, algorithms, or approaches. It is important to consider alternative solutions and best practices when using the model's outputs.

Ethical Considerations

The model should be used as a supportive tool for learning and problem-solving, not as a substitute for human expertise and critical thinking.
Users should be aware that the model's responses are generated based on patterns in the training data and may not always be accurate, complete, or up to date.
The model should not be relied upon for making critical decisions or solving real-world problems without thorough validation and testing.

Usage

To use the CodeMind model, you can access it through the Hugging Face model hub or by integrating it into your own applications using the provided API. Provide a coding problem or a question related to programming concepts, and the model will generate relevant explanations, code snippets, or guidance based on its training.

Please refer to the documentation and examples for detailed instructions on how to integrate and use the CodeMind model effectively.