Fine tuning of MiniCPM-Llama3-V-2_5-int4

finetune/finetune_lora_int4.sh

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+#!/bin/bash
+#Fine tuning of MiniCPM-Llama3-V-2_5-int4 on single GPU
+GPUS_PER_NODE=1
+NNODES=1
+NODE_RANK=0
+MASTER_ADDR=localhost
+MASTER_PORT=6001
+
+MODEL="openbmb/MiniCPM-Llama3-V-2_5-int4"
+DATA="" #Path to json file
+EVAL_DATA=""#Path to json file
+LLM_TYPE="llama3"
+
+DISTRIBUTED_ARGS="
+    --nproc_per_node $GPUS_PER_NODE \
+    --nnodes $NNODES \
+    --node_rank $NODE_RANK \
+    --master_addr $MASTER_ADDR \
+    --master_port $MASTER_PORT
+"
+export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:128
+torchrun $DISTRIBUTED_ARGS finetune.py  \
+    --model_name_or_path $MODEL \
+    --llm_type $LLM_TYPE \
+    --data_path $DATA \
+    --eval_data_path $EVAL_DATA \
+    --remove_unused_columns false \
+    --label_names "labels" \
+    --prediction_loss_only false \
+    --bf16 true \
+    --bf16_full_eval true \
+    --fp16 false \
+    --fp16_full_eval false \
+    --do_train \
+    --do_eval \
+    --tune_vision true \
+    --tune_llm false \
+    --use_lora true \
+    --lora_target_modules "llm\..*layers\.\d+\.self_attn\.(q_proj|k_proj)" \
+    --model_max_length 2048 \
+    --max_slice_nums 9 \
+    --max_steps 1000 \
+    --eval_steps 100 \
+    --output_dir output/output_minicpmv2_lora \
+    --logging_dir output/output_minicpmv2_lora \
+    --logging_strategy "steps" \
+    --per_device_train_batch_size 1 \
+    --per_device_eval_batch_size 1 \
+    --gradient_accumulation_steps 4 \
+    --evaluation_strategy "steps" \
+    --save_strategy "steps" \
+    --save_steps 1000 \
+    --save_total_limit 10 \
+    --learning_rate 1e-6 \
+    --weight_decay 0.1 \
+    --adam_beta2 0.95 \
+    --warmup_ratio 0.01 \
+    --lr_scheduler_type "cosine" \
+    --logging_steps 1 \
+    --gradient_checkpointing true \
+    --deepspeed ds_config_zero2.json \
+    --report_to "tensorboard"