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CALM: Credit and Risk Assessment Large Language Model

• Due to licensing restrictions on LLaMA weights, the model cannot be used for commercial purposes. Please adhere strictly to LLaMA's usage policy.
• Considering the limitations of LLaMA's license, we cannot directly distribute the complete model weights. Here, we are only releasing the LoRA weights of CALM-7B.

Content

• CALM: Credit and Risk Assessment Large Language Model
  • Content
  • 1. Preparing the environment
  • 2. Run
    • 2.1 Download data
      • 2.1.1 Convert data format
    • 2.2 Model training
      • 2.2.1 LoRA
      • 2.2.2 Merge Model with LORA

1. Preparing the environment

Creating the environment using Conda, followed by installing the required packages using pip.

pip install -r requirements.txt

2. Run

2.1 Download data

Before running, please download rawdata to data/CRA_resample_0.045M.json

2.1.1 Convert data format

export raw_data=/path_to/CRA_resample_0.045M.json
export conv_data=/path_to/CRA_resample_0.045M_conv.json
export data_name=CRA
export dev_data=/path_to/CRA-resample-dev3k.json
export train_data=/path_to/CRA-resample-train4w.json

python scripts/convert_to_conv_data.py \
    --orig_data ${raw_data} \
    --write_data ${conv_data} \
    --dataset_name CRA
head -n 3000 ${conv_data} > ${dev_data}
tail -n +3001 ${conv_data} > ${train_data}

We designate the first 3000 entries as the validation set, while the remaining data serves as the training set.

2.2 Model training

Training strategy

• LoRA + int8

The initiation script for training is written in train/scripts/run.sh. You will need to modify the parameters in run.sh according to your specific requirements.

bash scripts/run_sft.sh

• model_name_or_path: The pretrained model (if it is an LLaMA model, it needs to be converted to the hf format beforehand in order to be loaded using from_pretrained)
• train_file: Training data
• validation_file: Validation data
• output_dir: Path to the training logs and model saves
• cache_dir: Path to the cache data processing process
• cutoff_len: Maximum input sequence length (LLaMA model suggests setting it to 1024 or above, Bloom model suggests setting it to 512 or above)

2.2.1 LoRA

nohup torchrun --nproc_per_node 2 src/entry_point/sft_train.py \
    --model_name_or_path ${model_name_or_path} \
    --bf16 True \
    --llama True \
    --use_lora True \
    --deepspeed configs/deepspeed_config_stage3.json \
    --lora_config configs/lora_config_llama.json \
    --train_file ${train_file} \
    --validation_file ${validation_file} \
    --per_device_train_batch_size 6 \
    --per_device_eval_batch_size 6 \
    --gradient_accumulation_steps 1 \
    --num_train_epochs 5 \
    --model_max_length ${cutoff_len} \
    --save_strategy "steps" \
    --save_total_limit 3 \
    --learning_rate 3e-4 \
    --weight_decay 0.00001 \
    --warmup_ratio 0.01 \
    --lr_scheduler_type "cosine" \
    --logging_steps 10 \
    --evaluation_strategy "steps" \
    --seed 1234 \
    --gradient_checkpointing \
    --cache_dir ${cache_dir} \
    --output_dir ${output_dir} \
    --overwrite_output_dir \
    > ${log_dir}/train.log 2>&1 &

Parameters

• use_lora: Training using LoRA
• use_int8_training: Training with 8-bit quantization, which significantly reduces memory usage
• lora_config: The parameter configuration for LoRA is provided. If training a Bloom model, it should be changed to "configs/lora_config_bloom.json"
• deepspeed When training sequences are long, it is recommended to utilize deepspeed stage 3, which effectively distributes model parameters across multiple cards, allowing room to load even longer sequences

Note: Please be aware that you can only choose between "use_int8_training" and "deepspeed"; they cannot be used simultaneously.

The structure of the output_dir:

output_dir/
├── checkpoint-244/
│   ├── pytorch_model.bin
│   └── trainer_state.json
├── checkpoint-527/
│   ├── pytorch_model.bin
│   └── trainer_state.json
├── adapter_model.bin
├── print_log.txt
└── adapter_config.json

The highest-level directory stores the final model obtained from the training process.

2.2.2 Merge Model with LORA

If you wish to merge the weights of LoRA with a pre-trained model, you can execute the following command:

model_name_or_path=model_path_to/llama-2-7b-chat-T/
lora_path=lora_path_to/checkpoint_2/3739
output_path=out_path_to/CRA__model_2/model_3739

CUDA_VISIBLE_DEVICES=0 python src/merge_llama_with_lora.py \
    --model_name_or_path ${model_name_or_path} \
    --output_path ${output_path} \
    --lora_path ${lora_path} \
    --llama

The merged weights will be saved in the "output_path" directory. You can subsequently load them directly using "from_pretrained".