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Add Fine-Tuned benchmark results (85.3% accuracy) from checkpoint-25
Aditya- Update system3_inference.py to load LoRA adapter directly from checkpoint-25/ instead of requiring a merged finetuned_model/ dir - Add fill_r3.py: standalone script to populate r3 metrics in the existing benchmark cache without re-running API calls - Update data/benchmark_cache.json with real Fine-Tuned metrics for all 50 questions (accuracy 85.3%, ROUGE-L 0.283, HR 0.0%) - Update README benchmark table with real Fine-Tuned numbers09c4e61 -
Add pre-computed benchmark cache for all 50 questions
Aditya- data/benchmark_cache.json: real benchmark results from Baseline LLM and RAG across all 50 reference Q&A pairs (accuracy, ROUGE-L, groundedness, answer relevance, faithfulness, latency, cost). App loads this on startup so the analytics tab shows results instantly without re-running the benchmark. Summary: Baseline 81.5% accuracy @ 1.46s avg, RAG 79.9% @ 4.19s avg. - run_benchmark.py: standalone script to regenerate the cache. Imports rouge_score before heavy ML libs to avoid segfault. Saves incrementally after each question so it can resume from interruptions.78c912f