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[**中文**](./README_ZH.md) | [**English**](./README.md) |
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<p align="center" width="100%"> |
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<a href="https://github.com/daiyizheng/TCMChat" target="_blank"><img src="assets/logo.png" alt="TCMChat" style="width: 25%; min-width: 300px; display: block; margin: auto;"></a> |
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</p> |
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# TCMChat: A Generative Large Language Model for Traditional Chinese Medicine |
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[![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg)](https://github.com/SCIR-HI/Huatuo-Llama-Med-Chinese/blob/main/LICENSE) [![Python 3.10.12](https://img.shields.io/badge/python-3.10.12-blue.svg)](https://www.python.org/downloads/release/python-390/) |
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## News |
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[2024-5-17] Open source model weight on HuggingFace. |
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## Application |
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### Install |
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``` |
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git clone https://github.com/daiyizheng/TCMChat |
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cd TCMChat |
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``` |
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First install the dependency package. python environment 3.10+ is recommended. |
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``` |
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pip install -r requirements.txt |
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``` |
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### Weights download |
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- [TCMChat](https://huggingface.co/daiyizheng/TCMChat): QA and recommendation of TCM knowledge based on baichuan2-7B-Chat. |
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### Inference |
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#### Command line |
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``` |
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python cli_infer.py \ |
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--model_name_or_path /your/model/path \ |
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--model_type chat |
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``` |
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#### Web demo |
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``` |
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python gradio_demo.py |
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``` |
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We provide an online tool:[https://xomics.com.cn/tcmchat](https://xomics.com.cn/tcmchat) |
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### Retrain |
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#### Dataset Download |
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- [Pretrain dataset](https://github.com/ZJUFanLab/TCMChat/tree/master/data/pretrain) |
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- [SFT dataset](https://github.com/ZJUFanLab/TCMChat/tree/master/data/sft) |
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- [Benchmark dataset](https://github.com/ZJUFanLab/TCMChat/tree/master/data/evaluate) |
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> Note: Currently only sample data is provided. In the near future, we will fully open source the original data. |
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#### Pre-training |
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```shell |
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train_type="pretrain" |
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train_file="data/pretrain/train" |
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validation_file="data/pretrain/test" |
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block_size="1024" |
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deepspeed_dir="data/resources/deepspeed_zero_stage2_config.yml" |
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num_train_epochs="2" |
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export WANDB_PROJECT="TCM-${train_type}" |
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date_time=$(date +"%Y%m%d%H%M%S") |
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run_name="${date_time}_${block_size}" |
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model_name_or_path="your/path/Baichuan2-7B-Chat" |
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output_dir="output/${train_type}/${date_time}_${block_size}" |
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accelerate launch --config_file ${deepspeed_dir} src/pretraining.py \ |
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--model_name_or_path ${model_name_or_path} \ |
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--train_file ${train_file} \ |
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--validation_file ${validation_file} \ |
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--preprocessing_num_workers 20 \ |
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--cache_dir ./cache \ |
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--block_size ${block_size} \ |
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--seed 42 \ |
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--do_train \ |
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--do_eval \ |
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--per_device_train_batch_size 32 \ |
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--per_device_eval_batch_size 32 \ |
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--num_train_epochs ${num_train_epochs} \ |
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--low_cpu_mem_usage True \ |
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--torch_dtype bfloat16 \ |
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--bf16 \ |
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--ddp_find_unused_parameters False \ |
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--gradient_checkpointing True \ |
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--learning_rate 2e-4 \ |
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--warmup_ratio 0.05 \ |
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--weight_decay 0.01 \ |
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--report_to wandb \ |
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--run_name ${run_name} \ |
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--logging_dir logs \ |
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--logging_strategy steps \ |
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--logging_steps 10 \ |
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--eval_steps 50 \ |
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--evaluation_strategy steps \ |
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--save_steps 100 \ |
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--save_strategy steps \ |
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--save_total_limit 13 \ |
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--output_dir ${output_dir} \ |
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--overwrite_output_dir |
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``` |
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#### Fine-tuning |
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```shell |
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train_type="SFT" |
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model_max_length="1024" |
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date_time=$(date +"%Y%m%d%H%M%S") |
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data_path="data/sft/sample_train_baichuan_data.json" |
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model_name_or_path="your/path/pretrain" |
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deepspeed_dir="data/resources/deepspeed_zero_stage2_confi_baichuan2.json" |
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export WANDB_PROJECT="TCM-${train_type}" |
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run_name="${train_type}_${date_time}" |
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output_dir="output/${train_type}/${date_time}_${model_max_length}" |
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deepspeed --hostfile="" src/fine-tune.py \ |
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--report_to "wandb" \ |
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--run_name ${run_name} \ |
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--data_path ${data_path} \ |
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--model_name_or_path ${model_name_or_path} \ |
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--output_dir ${output_dir} \ |
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--model_max_length ${model_max_length} \ |
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--num_train_epochs 4 \ |
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--per_device_train_batch_size 16 \ |
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--gradient_accumulation_steps 1 \ |
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--save_strategy epoch \ |
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--learning_rate 2e-5 \ |
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--lr_scheduler_type constant \ |
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--adam_beta1 0.9 \ |
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--adam_beta2 0.98 \ |
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--adam_epsilon 1e-8 \ |
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--max_grad_norm 1.0 \ |
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--weight_decay 1e-4 \ |
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--warmup_ratio 0.0 \ |
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--logging_steps 1 \ |
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--gradient_checkpointing True \ |
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--deepspeed ${deepspeed_dir} \ |
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--bf16 True \ |
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--tf32 True |
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``` |
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### Training details |
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Please refer to the experimental section of the paper for instructions. |
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