Update README.md

README.md

@@ -1,3 +1,122 @@
----
-license: mit
----
+---
+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.