{"repo": "1553965822/rpcot-lmc", "files": {"config/config.yaml": {"0bb226a6dd4cf286e3ec6d2f7ad8a424ef11a011": {"t": 1786068508.6786218, "n": 1200, "h": "d749409174fc740d", "c": "# config.yaml\n\n# General settings\nproject_name: \"Low Resource Contract Risk Detection\"\noutput_dir: \"result/output\"  \n\n# # Deepseek LLM configuration (Huggingface)\n# deepseek:\n#   api_base: \"https://api.xxxxx.com/\" # API base URL for DeepSeek\n#   api_key: \"sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxx\" # API secret key\n#   model_name: \"deepseek-chat\"  # DeepSeek Model name\n#   max_length: 2048\n#   temperature: 0.6\n# Deepseek LLM configuration (Xinference)\ndeepseek:\n  api_base: \"http://192.168.2.227:6006/v1\"  # Xinference API base URL\n  api_key: \"none\" \n  model_name: \"deepseek-r1-0528-qwen3\"  \n  max_length: 2048\n  temperature: 0.6\n\n\n# MiniCPM SLM (small model) configuration for fine-tuning\nminicpm:\n  model_path: \"models/minicpm3-4b\"\n  fine_tune_data_path: \"data/finetune_data.json\"\n  epochs: 3  \n  batch_size: 2  \n  learning_rate: 5e-5\n  max_seq_length: 384 \n\ntraining:\n  gpu_ids: [1, 6]\n  gradient_accumulation_steps: 2\n  output_dir: \"result/output/fine_tuned_model\"\n\n# Risk Knowledge Base\nrisk_knowledge_path: \"data/risk_knowledge.json\"\n\n# Evaluation settings\nevaluation:\n  metrics: [\"accuracy\", \"precision\", \"recall\", \"f1\"]\n\n# Logging configuration\nlogging:\n  level: \"INFO\"\n  log_file: \"experiment.log\""}}}}