Awesome

TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Data

We present TAT-LLM, a language model specialized in answering questions over financial Tabular and Textual Data.

Model	Size	FINQA	TAT-QA	TAT-DQA
GPT-3.5-Turbo	-	58.00	59.47	52.74
GPT-4	-	63.91	71.92	64.46
TAT-LLM-7B-LORA	7B	65.13	76.49	71.38
TAT-LLM-7B-FFT	7B	69.75	76.91	72.64
TAT-LLM-13B-LORA	13B	71.93	77.51	72.22
TAT-LLM-13B-FFT	13B	72.97	78.41	73.18
TAT-LLM-70B-LORA	70B	76.81	81.42	76.55
TAT-LLM-70B-FFT	70B	76.11	82.20	76.97

Refer to our TAT-LLM Paper for more information.

Requirements

To create an environment with MiniConda and activate it.

conda create -n tat-llm python=3.9
conda activate tat-llm
pip install torch --index-url https://download.pytorch.org/whl/cu118
pip install -r requirement.txt

Dataset

The TAT-LLM model was trained using data from the folder data/sft, and its predictions are stored in the folder data/prediction. The training data can also be accessed on 🤗Hugging Face.

Train

Parameter-Efficent finetuning Llama-2-7b on 1 X A100 80GB GPU

# Make sure you have access to llama2 so that lora layers can be created successfully

python tat_llm_train.py \
    --output_dir {output_folder_to_save_model_checkpoints_and_tensorboard_runs} \
    --model_name_or_path "meta-llama/Llama-2-7b-hf" \
    --report_to tensorboard \
    --group_by_length \
    --learning_rate 3e-4 \
    --warmup_ratio 0.03 \
    --per_device_train_batch_size 4 \
    --gradient_accumulation_steps 10 \
    --logging_steps 1 \
    --num_train_epochs 3 \
    --max_steps -1 \
    --gradient_checkpointing \
    --load_in_8bit \
    --use_peft \
    --lora_target_modules q_proj k_proj v_proj o_proj \
    --lora_alpha 16 \
    --log_level info \
    --evaluation_strategy steps \
    --save_strategy steps \
    --eval_steps 406 \
    --save_steps 406

<details> <summary>Parameter-Efficent finetuning Llama-2-13b/70b on 8 X A100 80GB GPU</summary>

# Make sure you have access to llama2 so that lora layers can be created successfully

torchrun --rdzv-backend c10d \
  --rdzv-endpoint localhost:7788 \
  --nnodes 1 \
  --nproc_per_node 8 \
  tat_llm_train.py \
  --output_dir {output_folder_to_save_model_checkpoints_and_tensorboard_runs} \
  --model_name_or_path "meta-llama/Llama-2-13b-hf" \
  --report_to tensorboard \
  --group_by_length \
  --learning_rate 3e-4 \
  --warmup_ratio 0.03 \
  --per_device_train_batch_size 1 \
  --gradient_accumulation_steps 5 \
  --logging_steps 1 \
  --num_train_epochs 3 \
  --max_steps -1 \
  --gradient_checkpointing \
  --use_peft \
  --lora_target_modules q_proj k_proj v_proj o_proj \
  --lora_alpha 16 \
  --log_level info \
  --evaluation_strategy steps \
  --save_strategy steps \
  --eval_steps 406 \
  --save_steps 406 \
  --bf16 \
  --deepspeed ds_config_lora.json

</details> <details> <summary>Full-Parameter finetuning Llama2-7b/13b/70b on 8 X A100 80GB GPU</summary>

torchrun --rdzv-backend c10d \
  --rdzv-endpoint localhost:7788 \
  --nnodes 1 \
  --nproc_per_node 8 \
  tat_llm_train.py \
  --output_dir {output_folder_to_save_model_checkpoints_and_tensorboard_runs} \
  --model_name_or_path "meta-llama/Llama-2-13b-hf" \
  --report_to tensorboard \
  --group_by_length \
  --learning_rate 3e-6 \
  --warmup_ratio 0.03 \
  --per_device_train_batch_size 1 \
  --gradient_accumulation_steps 5 \
  --logging_steps 1 \
  --num_train_epochs 3 \
  --max_steps -1 \
  --gradient_checkpointing \
  --bf16 \
  --deepspeed ds_config_fft.json

</details>

Inference

If you want to make inference with FFT model e.g., tat-llm-7b-fft

# test_data_type should be one of ['finqa', 'tatqa', 'tatdqa', 'all']

python tat_llm_infer.py \
    --model_name_or_path "next-tat/tat-llm-7b-fft" \
    --test_data_type {test_data_type} \
    --output_path {output_folder_to_save_prediction_files}

If you want to make inference with LoRa model e.g., tat-llm-7b-lora

# Make sure you have access to llama2 so that lora weights can be merged successfully

python tat_llm_infer.py \
    --model_name_or_path "meta-llama/Llama-2-7b-hf"
    --lora_name_or_path "next-tat/tat-llm-7b-lora" \
    --test_data_type {test_data_type} \
    --output_path {output_folder_to_save_prediction_files}

Remarks:

Depending on the GPU utilized and the packages installed, the final prediction results may exhibit slight variations
Regards with gpu resources, tat-llm-7b/13b model requires 1 X A100 80GB GPU while tat-llm-70b model requires 2 X A100 80GB GPU

Evaluation

To evaluate the prediction of the LLMs

python tat_llm_eval.py --dataset_name={dataset_name} --model_name={model_name} --model_type={model_type}

dataset_name

finqa
tatqa
tatdqa

model_name

tat-llm-7b
tat-llm-13b
tat-llm-70b

model_type

fft
lora

Citation

Please kindly consider citing our work if you are using this code repo in your work, thank you.

@misc{zhu2024tatllm,
      title={TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Data},
      author={Fengbin Zhu and Ziyang Liu and Fuli Feng and Chao Wang and Moxin Li and Tat-Seng Chua},
      year={2024},
      eprint={2401.13223},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}