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