--- license: apache-2.0 datasets: - SylvanL/Traditional-Chinese-Medicine-Dataset-Pretrain language: - zh base_model: - Qwen/Qwen2-7B-Instruct tags: - medical --- ### 测试评估结果正在路上... 在2张V800-80G上, 基于Qwen/Qwen2-7B-Instruct, 在llamafactory框架上, 使用SylvanL/Traditional-Chinese-Medicine-Dataset-Pretrain进行了2个epoch的继续预训练(Continue Pre-train). 在保留模型原有通用能力的前提下,使模型熟悉、记住,并更倾向于输出以下内容: 1. 中医问诊单、处方笺、医生诊断及多种格式的病案、医案内容 2. 中医领域教材与典籍 3. 中成药、中药材、中医方剂、中医术语、中医疾病、中医症状、药膳食疗相关的知识点 ``` epoch 1: { "num_input_tokens_seen": 442925056, "total_flos": 885678736932864.0, "train_loss": 1.658593576353242, "train_runtime": 133293.1729, "train_samples_per_second": 3.246, "train_steps_per_second": 0.014 } epoch 2: { "num_input_tokens_seen": 442925056, "total_flos": 885678736932864.0, "train_loss": 1.3894652060929016, "train_runtime": 139124.2076, "train_samples_per_second": 3.11, "train_steps_per_second": 0.014 } ``` ``` llamafactory-cli train \ --stage pt \ --do_train True \ --model_name_or_path Qwen/Qwen2-7B-Instruct \ --preprocessing_num_workers 16 \ --finetuning_type full \ --template default \ --flash_attn auto \ --dataset_dir {dataset_dir} \ --dataset CPT_generalMedical_362420,{shibing624/huatuo_medical_qa_sharegpt},CPT_medicalRecord_source1_61486,CPT_medicalRecord_source2_15307,CPT_medicalRecord_source3_230000,CPT_tcmKnowledge_source1_17921,CPT_tcmKnowledge_source2_12889,CPT_tcmBooks_source1_146244 \ --cutoff_len 1024 \ --learning_rate 6e-06 \ --num_train_epochs 2.0 \ --max_samples 1000000 \ --per_device_train_batch_size 28 \ --gradient_accumulation_steps 4 \ --lr_scheduler_type cosine \ --max_grad_norm 1.0 \ --logging_steps 1 \ --save_steps 1000 \ --warmup_steps 0 \ --optim adamw_torch \ --packing True \ --report_to none \ --output_dir {output_dir} \ --bf16 True \ --plot_loss True \ --ddp_timeout 180000000 \ --include_num_input_tokens_seen True \ --deepspeed cache/ds_z3_offload_config.json ```