README.md

metadata

license: mit
datasets:
  - HuggingFaceFW/fineweb-2
  - amphora/QwQ-LongCoT-130K
  - bigcode/the-stack
  - codeparrot/github-code
  - code_search_net/code_search_net
  - google/pythia-code-dataset
  - DeepMind/alphacode_data
  - jsdatasets/crosswoz
  - google/web-questions-sp
  - facebook/react
  - react-community/react-native-datasets
  - nodejs/node-test-commit
  - your-org/awesome-nodejs-curated
  - edx/edx-platform
  - django/django
  - W3C/web-platform-tests
  - your-org/diverse-html-dataset
  - DeepMind/alphamind_data
  - OpenAI/human-eval
language:
  - en
metrics:
  - accuracy
  - code_bleu
  - execution_accuracy
  - unit_test_accuracy
  - code_coverage
  - human_evaluation_results
base_model:
  - codellama/CodeLlama-70b-Instruct-hf
  - prithivMLmods/Codepy-Deepthink-3B
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.