Update app.py

app.py

@@ -72,9 +72,7 @@ def display_work_experience():
         - Implemented advanced LLM fine-tuning strategies for Qwen model including Parameter-Efficient Fine-Tuning (PEFT) with LoRA and 2-stage whole model training on multi-GPU clusters, achieving 12% performance improvement across 15+ categories
         - Developed "auto hinter" system to improve LLM reasoning, guiding models towards correct answers based on question complexity, resulting in 8% performance increment on PhD-level questions
         - Built "auto rater" system to assess responses from leading models like Gemini 2.5 Pro and o3 custom builds, scoring across four key dimensions: completeness, coherence, clarity, correctness, style and formatting
-        - Applied advanced model compression techniques including quantization and distillation methods to optimize inference performance while maintaining model accuracy for production-ready LLM deployment
-        - Designed robust evaluation pipelines incorporating ROC curve analysis, performance benchmarking, bias mitigation, and RMSE validation to ensure model reliability and efficiency
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     **San Jose State University, San Jose, CA, USA**
     August 2024 - December 2024
     - **Teaching Assistant**