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LLM Optimization Research
PythonPyTorchLLM InferenceQuantizationModel Compression
Optimized LLMs for edge devices using dynamic pruning, quantization, and distributed inference by making models fit where they shouldn't.
Research project optimizing Large Language Models for edge deployment. Implemented and evaluated techniques including dynamic pruning, quantization, and distributed inference to reduce computational requirements while preserving model quality on resource-constrained devices.
Key Features
- Dynamic pruning for model size reduction
- Quantization for efficient edge inference
- Distributed inference across devices
- Performance benchmarking and trade-off analysis
Technology Stack
AI/ML
PyTorchLLMQuantizationModel Compression
Tools
GitJupyter Notebooks
Challenges
- Balancing model size, accuracy, and inference speed on edge hardware
- Evaluating optimization techniques across different model architectures
Key Learnings
- LLM compression and quantization techniques
- Edge deployment constraints and optimization strategies
- Research methodology for model performance evaluation
Screenshots
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