pip install -r requirements.txt
You can download the instruction data from the link
https://drive.google.com/file/d/1U_45qCHXpiArW_BCkKOhVfOQn23J4pUA/view?usp=drive_link
place it in the directory (training/toy_data_instruct/ReFICR_Instruct)
We will upload our model weight to huggingface later
sh run.sh
CUDA_VISIBLE_DEVICES=0 torchrun --nproc_per_node 1 --master_port 25900\
-m training.run \
--output_dir model_weights/ReFICR_qlora \
--model_name_or_path GritLM/GritLM-7B \
--train_data training/toy_data_instruct/ReFICR_Instruct\
--learning_rate 2e-5 \
--num_train_epochs 2 \
--warmup_ratio 0.03 \
--per_device_train_batch_size 2 \
--gradient_accumulation_steps 1 \
--dataloader_drop_last True \
--normalized True \
--temperature 0.02 \
--query_max_len 512 \
--passage_max_len 1024 \
--generative_max_len 2048 \
--train_group_size 10 \
--mode unified \
--lora True \
--attn bbcc \
--attn_implementation sdpa \
--pooling_method mean \
--gradient_checkpointing True \
--save_strategy "epoch" \
--save_steps 500 \
--bf16 True \
--qlora True \
--in_batch_neg False
CUDA_VISIBLE_DEVICES=0 python inference_ReRICR.py --config config/Conv2Item/inspired_config.yaml
CUDA_VISIBLE_DEVICES=0 python inference_ReRICR.py --config config/Conv2Conv/inspired_config.yaml
CUDA_VISIBLE_DEVICES=0 python inference_ReRICR.py --config config/Ranking/inspired_config.yaml
CUDA_VISIBLE_DEVICES=0 python inference_ReRICR.py --config config/Dialoge_Manage/inspired_config.yaml
CUDA_VISIBLE_DEVICES=0 python inference_ReRICR.py --config config/Response_Gen/inspired_config.yaml
you need to simply replace the modeling_mistral.py file in your transformers installation with modeling_mistral.py in order to use the bidirectional attention. More details can be found in ContextualAI/gritlm.
ContextualAI/gritlm This repository is built upon gritlm!