README.md

---
license: mit # Example: Choose a specific license
datasets:
  # General Code and Language Understanding:
  - HuggingFaceFW/fineweb-2
  - amphora/QwQ-LongCoT-130K

  # Diverse Programming Languages and Paradigms:
  - bigcode/the-stack # Use the full version for maximum coverage
  - codeparrot/github-code  # Filter for: Python, Java, C++, JavaScript, Go
  - code_search_net/code_search_net # Diverse code with natural language descriptions
  - google/pythia-code-dataset  # Python-focused, but includes examples from many domains
  - DeepMind/alphacode_data  # Code from competitive programming (Codeforces)

  # Web Development & Reasoning:
  - jsdatasets/crosswoz # Conversational dataset for web dev tasks
  - google/web-questions-sp # Complex web-related questions for reasoning

  # React-Specific:
  - facebook/react # React codebase, documentation, issues
  - react-community/react-native-datasets # For React Native support (if needed)

  # Node.js:
  - nodejs/node-test-commit # Node.js code changes and commit messages
  - your-org/awesome-nodejs-curated # Create a dataset from sindresorhus/awesome-nodejs

  # Python (Backend & Tooling):
  - edx/edx-platform # edX platform codebase (Python)
  - django/django # Django web framework codebase

  # HTML and Frontend:
  - W3C/web-platform-tests # Tests for HTML, CSS, JavaScript
  - your-org/diverse-html-dataset # Create a dataset of scraped and cleaned HTML

  # Deep Thinking and Reasoning (Enhance General Abilities):
  - DeepMind/alphamind_data # Data from AlphaMind for complex reasoning
  - OpenAI/human-eval # Python programming problems for evaluation

language:
  - en
  # - Add other languages if needed

metrics:
  - accuracy
  - code_bleu
  - execution_accuracy
  - unit_test_accuracy
  - code_coverage
  - human_evaluation_results # Placeholder

base_model:
  # Choose ONE highly capable, code-focused model (fine-tune this one):
  - codellama/CodeLlama-70b-Instruct-hf  # Example
  - prithivMLmods/Codepy-Deepthink-3B  # Side assist
  #- deepseek-ai/DeepSeek-V3 # Example: A strong DeepSeek Coder model (remove, and choose one)

pipeline_tag: text-generation

tags:
  - code
  - ide
  - code-generation
  - code-completion
  - code-refactoring
  - bug-detection
  - code-review
  - security
  - best-practices
  - web-development
  - react
  - nodejs
  - python
  - html

inference:
  optimizations:
    - quantization
---

# Detailed Model Description (Fill this in after training)

## Model Description

This model is designed to power an AI-driven IDE with a focus on web development, particularly React, Node.js, Python, and HTML. It has been trained on a diverse range of datasets, including:

*   General web text and code for broad language understanding.
*   Code in multiple programming languages (with a focus on web-related languages).
*   Datasets specifically related to React, Node.js, and general web development tasks.
*   Data to enhance deep thinking and reasoning capabilities.
*   Synthetic and/or collected data simulating IDE interactions (code editing, debugging, UI element navigation).
*   Datasets focused on security vulnerabilities and coding best practices.

The model is intended to assist developers with:

*   Code generation
*   Code completion
*   Code refactoring
*   Bug detection and fixing
*   Code review
*   Adherence to security and best practices

## Intended Uses & Limitations

*   **Intended Use:** To be integrated into an IDE to enhance developer productivity and code quality, especially in the context of web development.
*   **Limitations:**
    *   The model may still generate incorrect or suboptimal code. Human oversight is always required.
    *   Performance may vary across programming languages and specific coding tasks.
    *   The model's knowledge is limited to the data it was trained on.

## Evaluation Results

*   Provide detailed quantitative evaluation results using the metrics specified above.
*   Summarize the findings from human evaluations and user studies.

## Training Procedure

*   Describe the fine-tuning process, including hyperparameters, training duration, and any special techniques used.

## Ethical Considerations

*   Discuss any potential biases in the training data or model behavior.
*   Address the responsible use of AI for code generation.