Professional Python & AI/ML Debugger (Test Version)
Model Description
This repository, rajratna-2023/test, is a specialized experimental model designed to function as a Senior AI/ML Engineer and Python Specialist.
The model's core objective is to provide high-level debugging assistance, architectural advice, and performance optimization for code specifically used in Machine Learning and Deep Learning workflows.
Intended Use
This model is intended for developers, researchers, and data scientists who require help with:
- Tensor Dimension & Shape Debugging: Resolving
RuntimeError and dimension mismatch issues in PyTorch, TensorFlow, or JAX.
- Memory Management: Troubleshooting "Out of Memory" (OOM) errors and suggesting gradient accumulation or mixed-precision strategies.
- Algorithm Logic: Auditing custom loss functions, optimizer implementations, and training loops.
- Library-Specific Issues: Debugging complex interactions within the Hugging Face ecosystem (
transformers, peft, accelerate, etc.).
- Performance Tuning: Optimizing data pipelines (Dataloaders), vectorization (NumPy), and concurrency.
Limitations
- Experimental Status: This is currently a test repository and is in the early stages of development.
- Hallucination Risk: As with all Large Language Models, the model may occasionally suggest incorrect code or non-existent library functions. Always verify fixes against official documentation.
- Context Limits: The model is subject to standard context window limitations.
Training Roadmap (Planned)
To achieve professional-grade debugging, the following data strategy is planned:
- Code-Fix Pairs: Training on datasets consisting of "Broken Code + Traceback" $\rightarrow$ "Fixed Code + Explanation".
- Synthetic Error Generation: Using larger models to generate complex, subtle bugs in ML code to train the model on pattern recognition.
- Documentation Grounding: Fine-tuning on the official documentation of major ML frameworks.
Contact
