add multimodal LLM BLIP-2 and InstructBLIP

Former-commit-id: a730f89a972f1a9d37c718c716f199cb8d4903b2

examples/mllm/sft_blip2.sh

@@ -0,0 +1,34 @@
+#!/bin/bash
+
+CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
+    --stage sft_mm \
+    --do_train \
+    --model_name_or_path /home/LAB/fengzc/LLM/checkpoints/Salesforce/blip2-opt-2.7b \
+    --dataset llava_instruct_100 \
+    --dataset_dir data \
+    --template default \
+    --finetuning_type lora \
+    --lora_target q_proj,k_proj \
+    --output_dir saves/blip2-opt-2.7b/lora/sft \
+    --overwrite_cache \
+    --overwrite_output_dir \
+    --cutoff_len 1024 \
+    --preprocessing_num_workers 16 \
+    --per_device_train_batch_size 1 \
+    --per_device_eval_batch_size 1 \
+    --gradient_accumulation_steps 8 \
+    --lr_scheduler_type cosine \
+    --logging_steps 1 \
+    --warmup_steps 20 \
+    --save_steps 100 \
+    --eval_steps 100 \
+    --evaluation_strategy steps \
+    --load_best_model_at_end \
+    --learning_rate 5e-5 \
+    --num_train_epochs 3.0 \
+    --max_samples 3000 \
+    --val_size 0.1 \
+    --plot_loss \
+    --quantization_bit 8 \
+    --image_path /home/LAB/fengzc/LLM/checkpoints/liuhaotian/LLaVA-Instruct-150K/images/coco/train2017
+

examples/mllm/sft_instructblip.sh

@@ -0,0 +1,35 @@
+#!/bin/bash
+
+CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
+    --stage sft_mm \
+    --do_train \
+    --model_name_or_path /home/LAB/fengzc/LLM/checkpoints/Salesforce/instructblip-vicuna-7b \
+    --dataset llava_instruct_100 \
+    --dataset_dir data \
+    --template default \
+    --finetuning_type lora \
+    --lora_target q_proj,k_proj \
+    --output_dir saves/instructblip-vicuna-7b/lora/sft \
+    --overwrite_cache \
+    --overwrite_output_dir \
+    --cutoff_len 1024 \
+    --preprocessing_num_workers 16 \
+    --per_device_train_batch_size 1 \
+    --per_device_eval_batch_size 1 \
+    --gradient_accumulation_steps 8 \
+    --lr_scheduler_type cosine \
+    --logging_steps 1 \
+    --warmup_steps 20 \
+    --save_steps 100 \
+    --eval_steps 100 \
+    --evaluation_strategy steps \
+    --load_best_model_at_end \
+    --learning_rate 5e-5 \
+    --num_train_epochs 3.0 \
+    --max_samples 3000 \
+    --val_size 0.1 \
+    --plot_loss \
+    --quantization_bit 8 \
+    --image_path /home/LAB/fengzc/LLM/checkpoints/liuhaotian/LLaVA-Instruct-150K/images/coco/train2017 \
+    --use_qformer
+