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Commit History
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Use float16 on GPU, float32 fallback on CPU for fine-tuned model
Adityad3e5c89
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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
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Major upgrade: auto-metrics, Plotly charts, Qwen2.5, PDF support, expanded KB
Aditya- Add ROUGE-L and semantic similarity auto-metrics per answer card - Add Plotly bar charts for aggregate quality/speed comparison in Analytics section - Upgrade fine-tuned model from TinyLlama to Qwen/Qwen2.5-1.5B-Instruct - Update Colab notebook with Qwen2.5 model and ChatML prompt format - Add PDF ingestion to RAG pipeline (drop PDFs into data/pdfs/) - Add 5 new knowledge base docs: big_o_notation, heaps, sql_basics, typescript_basics, git_basics - Add data/reference_answers.json with 21 Q&A pairs for automated scoring - Add HuggingFace Spaces frontmatter and deploy guide to README - Add plotly, rouge-score, pypdf, langchain-huggingface, numpy to requirementsa52cc98
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Add LLM vs RAG vs Fine-Tuning comparison project
Aditya4a3f117