Open SKURA502 opened 5 months ago
日志中显示成功保存参数pytorch_model.bin： 但文档中找不到模型权重参数： 运行inference.py时报错：
No response
train.sh: PRE_SEQ_LEN=128 LR=2e-2 NUM_GPUS=1
CUDA_VISIBLE_DEVICES=1 python main.py \ --do_train \ --train_file /home/ns/chatbot/ChatGLM2-6B/ptuning/Chinese-medical-dialogue-data-master/train.json \ --validation_file /home/ns/chatbot/ChatGLM2-6B/ptuning/Chinese-medical-dialogue-data-master/dev.json \ --preprocessing_num_workers 10 \ --prompt_column context \ --response_column target \ --overwrite_cache \ --model_name_or_path /home/ns/chatbot/ChatGLM2-6B/chatglm2-6b \ --output_dir output/adgen-chatglm2-6b-pt-$PRE_SEQ_LEN-$LR \ --overwrite_output_dir \ --max_source_length 64 \ --max_target_length 128 \ --per_device_train_batch_size 1 \ --per_device_eval_batch_size 1 \ --gradient_accumulation_steps 16 \ --predict_with_generate \ --max_steps 3000 \ --logging_steps 10 \ --save_steps 10 \ --learning_rate $LR \ --pre_seq_len $PRE_SEQ_LEN \ --quantization_bit 4
- OS:win11 - Python:Python 3.8.16 - Transformers:4.36.2 - PyTorch:2.1.2 - CUDA Support (`python -c "import torch; print(torch.cuda.is_available())"`) : True
transformers==4.30.2
annpion
commented
5 months ago annpion
commented
5 months ago
Is there an existing issue for this?
Current Behavior
日志中显示成功保存参数pytorch_model.bin：
但文档中找不到模型权重参数：
运行inference.py时报错：
Expected Behavior
No response
Steps To Reproduce
train.sh: PRE_SEQ_LEN=128 LR=2e-2 NUM_GPUS=1
CUDA_VISIBLE_DEVICES=1 python main.py \ --do_train \ --train_file /home/ns/chatbot/ChatGLM2-6B/ptuning/Chinese-medical-dialogue-data-master/train.json \ --validation_file /home/ns/chatbot/ChatGLM2-6B/ptuning/Chinese-medical-dialogue-data-master/dev.json \ --preprocessing_num_workers 10 \ --prompt_column context \ --response_column target \ --overwrite_cache \ --model_name_or_path /home/ns/chatbot/ChatGLM2-6B/chatglm2-6b \ --output_dir output/adgen-chatglm2-6b-pt-$PRE_SEQ_LEN-$LR \ --overwrite_output_dir \ --max_source_length 64 \ --max_target_length 128 \ --per_device_train_batch_size 1 \ --per_device_eval_batch_size 1 \ --gradient_accumulation_steps 16 \ --predict_with_generate \ --max_steps 3000 \ --logging_steps 10 \ --save_steps 10 \ --learning_rate $LR \ --pre_seq_len $PRE_SEQ_LEN \ --quantization_bit 4
Environment
Anything else?
No response