SylvanL/ChatTCM-7B-Pretrain

测试评估结果正在路上...

在2张A800-80G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
}
average_perplexity: 7.2646328377141005

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
}
average_perplexity: 5.800355962033688

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