Update to MiniCPM-V 2.6

finetune/finetune_ds.sh

@@ -6,12 +6,15 @@ NODE_RANK=0
 MASTER_ADDR=localhost
 MASTER_PORT=6001
 
-MODEL="openbmb/MiniCPM-Llama3-V-2_5" # or openbmb/MiniCPM-V-2
+MODEL="openbmb/MiniCPM-V-2_6"
+# or openbmb/MiniCPM-V-2, openbmb/MiniCPM-Llama3-V-2_5
 # ATTENTION: specify the path to your training data, which should be a json file consisting of a list of conversations.
 # See the section for finetuning in README for more information.
 DATA="path/to/trainging_data"
 EVAL_DATA="path/to/test_data"
-LLM_TYPE="llama3" # if use openbmb/MiniCPM-V-2, please set LLM_TYPE=minicpm
+LLM_TYPE="qwen2" # if use openbmb/MiniCPM-V-2, please set LLM_TYPE=minicpm, if use openbmb/MiniCPM-Llama3-V-2_5, please set LLM_TYPE="llama3"
+
+
 
 DISTRIBUTED_ARGS="
     --nproc_per_node $GPUS_PER_NODE \
@@ -28,10 +31,10 @@ torchrun $DISTRIBUTED_ARGS finetune.py  \
     --remove_unused_columns false \
     --label_names "labels" \
     --prediction_loss_only false \
-    --bf16 false \
-    --bf16_full_eval false \
-    --fp16 true \
-    --fp16_full_eval true \
+    --bf16 true \
+    --bf16_full_eval true \
+    --fp16 false \
+    --fp16_full_eval false \
     --do_train \
     --do_eval \
     --tune_vision true \
@@ -40,8 +43,8 @@ torchrun $DISTRIBUTED_ARGS finetune.py  \
     --max_slice_nums 9 \
     --max_steps 10000 \
     --eval_steps 1000 \
-    --output_dir output/output_minicpmv2 \
-    --logging_dir output/output_minicpmv2 \
+    --output_dir output/output_minicpmv26 \
+    --logging_dir output/output_minicpmv26 \
     --logging_strategy "steps" \
     --per_device_train_batch_size 1 \
     --per_device_eval_batch_size 1 \