Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+wandb/run-20250331_173929-oflhglry/run-oflhglry.wandb filter=lfs diff=lfs merge=lfs -text

arguments.yaml

@@ -0,0 +1,53 @@
+data_cfgs:
+  eval_data_files: {}
+  eval_datasets: {}
+  eval_optional_args: []
+  eval_size: {}
+  eval_split: {}
+  eval_subset: {}
+  eval_template: {}
+  train_data_files: {}
+  train_datasets: /aifs4su/yaodong/hantao/datasets/AA_preference_cosi/val/merged/top1-10
+  train_name: text-image-to-text
+  train_optional_args: []
+  train_size: {}
+  train_split: train
+  train_template: AA_TI2T_LLAVA
+logger_cfgs:
+  cache_dir: {}
+  log_project: align-anything
+  log_run_name: dpo
+  log_type: wandb
+  output_dir: ../outputs/llava_1.6_mistral_7b_val/top1-10
+  save_total_limit: 3
+model_cfgs:
+  model_max_length: 4096
+  model_name_or_path: /aifs4su/yaodong/hantao/models/llava-v1.6-mistral-7b-hf
+  trust_remote_code: true
+special_tokens: {}
+train_cfgs:
+  adam_betas:
+  - 0.9
+  - 0.95
+  bf16: true
+  ds_cfgs: ds_z3_config.json
+  epochs: 3
+  eval_interval: 10
+  eval_strategy: epoch
+  fp16: false
+  freeze_language_model: false
+  freeze_mm_proj: false
+  freeze_vision_tower: true
+  gradient_accumulation_steps: 1
+  gradient_checkpointing: true
+  learning_rate: 1.0e-06
+  load_checkpoint: false
+  lr_scheduler_type: cosine
+  lr_warmup_ratio: 0.03
+  per_device_eval_batch_size: 1
+  per_device_train_batch_size: 1
+  regularization: 0.001
+  save_checkpoint: false
+  scale_coeff: 0.1
+  seed: 42
+  weight_decay: 0.0

environ.txt

@@ -0,0 +1,252 @@
+ADDR2LINE=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/x86_64-conda-linux-gnu-addr2line
+AR=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/x86_64-conda-linux-gnu-ar
+AS=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/x86_64-conda-linux-gnu-as
+BASH_FUNC__module_raw%%=() {  unset _mlshdbg;
+ if [ "${MODULES_SILENT_SHELL_DEBUG:-0}" = '1' ]; then
+ case "$-" in 
+ *v*x*)
+ set +vx;
+ _mlshdbg='vx'
+ ;;
+ *v*)
+ set +v;
+ _mlshdbg='v'
+ ;;
+ *x*)
+ set +x;
+ _mlshdbg='x'
+ ;;
+ *)
+ _mlshdbg=''
+ ;;
+ esac;
+ fi;
+ unset _mlre _mlIFS;
+ if [ -n "${IFS+x}" ]; then
+ _mlIFS=$IFS;
+ fi;
+ IFS=' ';
+ for _mlv in ${MODULES_RUN_QUARANTINE:-};
+ do
+ if [ "${_mlv}" = "${_mlv##*[!A-Za-z0-9_]}" -a "${_mlv}" = "${_mlv#[0-9]}" ]; then
+ if [ -n "`eval 'echo ${'$_mlv'+x}'`" ]; then
+ _mlre="${_mlre:-}${_mlv}_modquar='`eval 'echo ${'$_mlv'}'`' ";
+ fi;
+ _mlrv="MODULES_RUNENV_${_mlv}";
+ _mlre="${_mlre:-}${_mlv}='`eval 'echo ${'$_mlrv':-}'`' ";
+ fi;
+ done;
+ if [ -n "${_mlre:-}" ]; then
+ eval `eval ${_mlre} /usr/bin/tclsh /cm/local/apps/environment-modules/4.5.3/libexec/modulecmd.tcl bash '"$@"'`;
+ else
+ eval `/usr/bin/tclsh /cm/local/apps/environment-modules/4.5.3/libexec/modulecmd.tcl bash "$@"`;
+ fi;
+ _mlstatus=$?;
+ if [ -n "${_mlIFS+x}" ]; then
+ IFS=$_mlIFS;
+ else
+ unset IFS;
+ fi;
+ unset _mlre _mlv _mlrv _mlIFS;
+ if [ -n "${_mlshdbg:-}" ]; then
+ set -$_mlshdbg;
+ fi;
+ unset _mlshdbg;
+ return $_mlstatus
+}
+BASH_FUNC_ml%%=() {  module ml "$@"
+}
+BASH_FUNC_module%%=() {  _module_raw "$@" 2>&1
+}
+BASH_FUNC_switchml%%=() {  typeset swfound=1;
+ if [ "${MODULES_USE_COMPAT_VERSION:-0}" = '1' ]; then
+ typeset swname='main';
+ if [ -e /cm/local/apps/environment-modules/4.5.3/libexec/modulecmd.tcl ]; then
+ typeset swfound=0;
+ unset MODULES_USE_COMPAT_VERSION;
+ fi;
+ else
+ typeset swname='compatibility';
+ if [ -e /cm/local/apps/environment-modules/4.5.3/libexec/modulecmd-compat ]; then
+ typeset swfound=0;
+ MODULES_USE_COMPAT_VERSION=1;
+ export MODULES_USE_COMPAT_VERSION;
+ fi;
+ fi;
+ if [ $swfound -eq 0 ]; then
+ echo "Switching to Modules $swname version";
+ source /cm/local/apps/environment-modules/4.5.3/init/bash;
+ else
+ echo "Cannot switch to Modules $swname version, command not found";
+ return 1;
+ fi
+}
+BUILD=x86_64-conda-linux-gnu
+CC=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/x86_64-conda-linux-gnu-cc
+CC_FOR_BUILD=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/x86_64-conda-linux-gnu-cc
+CFLAGS=-march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /aifs4su/yaodong/miniconda3/envs/hantao_anything/include  -I/aifs4su/yaodong/miniconda3/envs/hantao_llama/targets/x86_64-linux/include  -L/aifs4su/yaodong/miniconda3/envs/hantao_llama/targets/x86_64-linux/lib -L/aifs4su/yaodong/miniconda3/envs/hantao_llama/targets/x86_64-linux/lib/stubs
+CMAKE_ARGS=-DCMAKE_AR=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/x86_64-conda-linux-gnu-ar -DCMAKE_RANLIB=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/x86_64-conda-linux-gnu-ranlib -DCMAKE_LINKER=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/x86_64-conda-linux-gnu-ld -DCMAKE_STRIP=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/x86_64-conda-linux-gnu-strip -DCMAKE_BUILD_TYPE=Release
+CMAKE_PREFIX_PATH=/aifs4su/yaodong/miniconda3/envs/hantao_llama:/aifs4su/yaodong/miniconda3/envs/hantao_llama/x86_64-conda-linux-gnu/sysroot/usr
+CMD_WLM_CLUSTER_NAME=slurm
+CONDA_BACKUP_ADDR2LINE=/aifs4su/yaodong/miniconda3/envs/hantao_anything/bin/x86_64-conda-linux-gnu-addr2line
+CONDA_BACKUP_AR=/aifs4su/yaodong/miniconda3/envs/hantao_anything/bin/x86_64-conda-linux-gnu-ar
+CONDA_BACKUP_AS=/aifs4su/yaodong/miniconda3/envs/hantao_anything/bin/x86_64-conda-linux-gnu-as
+CONDA_BACKUP_BUILD=x86_64-conda-linux-gnu
+CONDA_BACKUP_CC=/aifs4su/yaodong/miniconda3/envs/hantao_anything/bin/x86_64-conda-linux-gnu-cc
+CONDA_BACKUP_CC_FOR_BUILD=/aifs4su/yaodong/miniconda3/envs/hantao_anything/bin/x86_64-conda-linux-gnu-cc
+CONDA_BACKUP_CFLAGS=-march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /aifs4su/yaodong/miniconda3/envs/hantao_anything/include
+CONDA_BACKUP_CMAKE_PREFIX_PATH=/aifs4su/yaodong/miniconda3/envs/hantao_anything:/aifs4su/yaodong/miniconda3/envs/hantao_anything/x86_64-conda-linux-gnu/sysroot/usr
+CONDA_BACKUP_CONDA_BUILD_SYSROOT=/aifs4su/yaodong/miniconda3/envs/hantao_anything/x86_64-conda-linux-gnu/sysroot
+CONDA_BACKUP_CPP=/aifs4su/yaodong/miniconda3/envs/hantao_anything/bin/x86_64-conda-linux-gnu-cpp
+CONDA_BACKUP_CPPFLAGS=-DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /aifs4su/yaodong/miniconda3/envs/hantao_anything/include
+CONDA_BACKUP_CXX=/aifs4su/yaodong/miniconda3/envs/hantao_anything/bin/x86_64-conda-linux-gnu-c++
+CONDA_BACKUP_CXXFILT=/aifs4su/yaodong/miniconda3/envs/hantao_anything/bin/x86_64-conda-linux-gnu-c++filt
+CONDA_BACKUP_CXXFLAGS=-fvisibility-inlines-hidden -std=c++17 -fmessage-length=0 -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /aifs4su/yaodong/miniconda3/envs/hantao_anything/include
+CONDA_BACKUP_CXX_FOR_BUILD=/aifs4su/yaodong/miniconda3/envs/hantao_anything/bin/x86_64-conda-linux-gnu-c++
+CONDA_BACKUP_DEBUG_CFLAGS=-march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-all -fno-plt -Og -g -Wall -Wextra -fvar-tracking-assignments -ffunction-sections -pipe -isystem /aifs4su/yaodong/miniconda3/envs/hantao_anything/include
+CONDA_BACKUP_DEBUG_CPPFLAGS=-D_DEBUG -D_FORTIFY_SOURCE=2 -Og -isystem /aifs4su/yaodong/miniconda3/envs/hantao_anything/include
+CONDA_BACKUP_DEBUG_CXXFLAGS=-fvisibility-inlines-hidden -std=c++17 -fmessage-length=0 -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-all -fno-plt -Og -g -Wall -Wextra -fvar-tracking-assignments -ffunction-sections -pipe -isystem /aifs4su/yaodong/miniconda3/envs/hantao_anything/include
+CONDA_BACKUP_ELFEDIT=/aifs4su/yaodong/miniconda3/envs/hantao_anything/bin/x86_64-conda-linux-gnu-elfedit
+CONDA_BACKUP_GCC=/aifs4su/yaodong/miniconda3/envs/hantao_anything/bin/x86_64-conda-linux-gnu-gcc
+CONDA_BACKUP_GCC_AR=/aifs4su/yaodong/miniconda3/envs/hantao_anything/bin/x86_64-conda-linux-gnu-gcc-ar
+CONDA_BACKUP_GCC_NM=/aifs4su/yaodong/miniconda3/envs/hantao_anything/bin/x86_64-conda-linux-gnu-gcc-nm
+CONDA_BACKUP_GCC_RANLIB=/aifs4su/yaodong/miniconda3/envs/hantao_anything/bin/x86_64-conda-linux-gnu-gcc-ranlib
+CONDA_BACKUP_GPROF=/aifs4su/yaodong/miniconda3/envs/hantao_anything/bin/x86_64-conda-linux-gnu-gprof
+CONDA_BACKUP_GXX=/aifs4su/yaodong/miniconda3/envs/hantao_anything/bin/x86_64-conda-linux-gnu-g++
+CONDA_BACKUP_HOST=x86_64-conda-linux-gnu
+CONDA_BACKUP_LD=/aifs4su/yaodong/miniconda3/envs/hantao_anything/bin/x86_64-conda-linux-gnu-ld
+CONDA_BACKUP_LDFLAGS=-Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,--disable-new-dtags -Wl,--gc-sections -Wl,-rpath,/aifs4su/yaodong/miniconda3/envs/hantao_anything/lib -Wl,-rpath-link,/aifs4su/yaodong/miniconda3/envs/hantao_anything/lib -L/aifs4su/yaodong/miniconda3/envs/hantao_anything/lib
+CONDA_BACKUP_LD_GOLD=/aifs4su/yaodong/miniconda3/envs/hantao_anything/bin/x86_64-conda-linux-gnu-ld.gold
+CONDA_BACKUP_NM=/aifs4su/yaodong/miniconda3/envs/hantao_anything/bin/x86_64-conda-linux-gnu-nm
+CONDA_BACKUP_OBJCOPY=/aifs4su/yaodong/miniconda3/envs/hantao_anything/bin/x86_64-conda-linux-gnu-objcopy
+CONDA_BACKUP_OBJDUMP=/aifs4su/yaodong/miniconda3/envs/hantao_anything/bin/x86_64-conda-linux-gnu-objdump
+CONDA_BACKUP_RANLIB=/aifs4su/yaodong/miniconda3/envs/hantao_anything/bin/x86_64-conda-linux-gnu-ranlib
+CONDA_BACKUP_READELF=/aifs4su/yaodong/miniconda3/envs/hantao_anything/bin/x86_64-conda-linux-gnu-readelf
+CONDA_BACKUP_SIZE=/aifs4su/yaodong/miniconda3/envs/hantao_anything/bin/x86_64-conda-linux-gnu-size
+CONDA_BACKUP_STRINGS=/aifs4su/yaodong/miniconda3/envs/hantao_anything/bin/x86_64-conda-linux-gnu-strings
+CONDA_BACKUP_STRIP=/aifs4su/yaodong/miniconda3/envs/hantao_anything/bin/x86_64-conda-linux-gnu-strip
+CONDA_BACKUP__CONDA_PYTHON_SYSCONFIGDATA_NAME=_sysconfigdata_x86_64_conda_cos7_linux_gnu
+CONDA_BACKUP_build_alias=x86_64-conda-linux-gnu
+CONDA_BACKUP_host_alias=x86_64-conda-linux-gnu
+CONDA_BUILD_SYSROOT=/aifs4su/yaodong/miniconda3/envs/hantao_llama/x86_64-conda-linux-gnu/sysroot
+CONDA_DEFAULT_ENV=hantao_llama
+CONDA_EXE=/aifs4su/yaodong/miniconda3/bin/conda
+CONDA_PREFIX=/aifs4su/yaodong/miniconda3/envs/hantao_llama
+CONDA_PREFIX_1=/aifs4su/yaodong/miniconda3
+CONDA_PREFIX_2=/aifs4su/yaodong/miniconda3/envs/hantao_anything
+CONDA_PROMPT_MODIFIER=(hantao_llama) 
+CONDA_PYTHON_EXE=/aifs4su/yaodong/miniconda3/bin/python
+CONDA_SHLVL=3
+CPATH=/cm/shared/apps/slurm/current/include
+CPATH_modshare=/cm/shared/apps/slurm/current/include:1
+CPP=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/x86_64-conda-linux-gnu-cpp
+CPPFLAGS=-DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /aifs4su/yaodong/miniconda3/envs/hantao_anything/include  -I/aifs4su/yaodong/miniconda3/envs/hantao_llama/targets/x86_64-linux/include  -L/aifs4su/yaodong/miniconda3/envs/hantao_llama/targets/x86_64-linux/lib -L/aifs4su/yaodong/miniconda3/envs/hantao_llama/targets/x86_64-linux/lib/stubs
+CROSS_RANK=0
+CROSS_SIZE=1
+CUDA_MODULE_LOADING=LAZY
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+CXX=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/x86_64-conda-linux-gnu-c++
+CXXFILT=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/x86_64-conda-linux-gnu-c++filt
+CXXFLAGS=-fvisibility-inlines-hidden -std=c++17 -fmessage-length=0 -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /aifs4su/yaodong/miniconda3/envs/hantao_anything/include  -I/aifs4su/yaodong/miniconda3/envs/hantao_llama/targets/x86_64-linux/include  -L/aifs4su/yaodong/miniconda3/envs/hantao_llama/targets/x86_64-linux/lib -L/aifs4su/yaodong/miniconda3/envs/hantao_llama/targets/x86_64-linux/lib/stubs
+CXX_FOR_BUILD=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/x86_64-conda-linux-gnu-c++
+DBUS_SESSION_BUS_ADDRESS=unix:path=/run/user/1028/bus
+DEBUG_CFLAGS=-march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-all -fno-plt -Og -g -Wall -Wextra -fvar-tracking-assignments -ffunction-sections -pipe -isystem /aifs4su/yaodong/miniconda3/envs/hantao_anything/include
+DEBUG_CPPFLAGS=-D_DEBUG -D_FORTIFY_SOURCE=2 -Og -isystem /aifs4su/yaodong/miniconda3/envs/hantao_anything/include
+DEBUG_CXXFLAGS=-fvisibility-inlines-hidden -std=c++17 -fmessage-length=0 -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-all -fno-plt -Og -g -Wall -Wextra -fvar-tracking-assignments -ffunction-sections -pipe -isystem /aifs4su/yaodong/miniconda3/envs/hantao_anything/include
+ELFEDIT=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/x86_64-conda-linux-gnu-elfedit
+ENABLE_LMOD=0
+GCC=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/x86_64-conda-linux-gnu-gcc
+GCC_AR=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/x86_64-conda-linux-gnu-gcc-ar
+GCC_NM=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/x86_64-conda-linux-gnu-gcc-nm
+GCC_RANLIB=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/x86_64-conda-linux-gnu-gcc-ranlib
+GPROF=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/x86_64-conda-linux-gnu-gprof
+GSETTINGS_SCHEMA_DIR=/aifs4su/yaodong/miniconda3/envs/hantao_llama/share/glib-2.0/schemas
+GSETTINGS_SCHEMA_DIR_CONDA_BACKUP=
+GXX=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/x86_64-conda-linux-gnu-g++
+HF_DATASETS_CACHE=/aifs4su/yaodong/.cache/huggingface/datasets
+HF_HOME=/aifs4su/yaodong/.cache/huggingface
+HISTTIMEFORMAT=%y/%m/%d %T 
+HOME=/home/yangyaodong
+HOST=x86_64-conda-linux-gnu
+KMP_DUPLICATE_LIB_OK=True
+KMP_INIT_AT_FORK=FALSE
+LANG=C.UTF-8
+LD=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/x86_64-conda-linux-gnu-ld
+LDFLAGS=-Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,--disable-new-dtags -Wl,--gc-sections -Wl,-rpath,/aifs4su/yaodong/miniconda3/envs/hantao_anything/lib -Wl,-rpath-link,/aifs4su/yaodong/miniconda3/envs/hantao_anything/lib -L/aifs4su/yaodong/miniconda3/envs/hantao_anything/lib  -L/aifs4su/yaodong/miniconda3/envs/hantao_llama/targets/x86_64-linux/lib -L/aifs4su/yaodong/miniconda3/envs/hantao_llama/targets/x86_64-linux/lib/stubs
+LD_GOLD=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/x86_64-conda-linux-gnu-ld.gold
+LD_LIBRARY_PATH=/aifs4su/yaodong/miniconda3/envs/hantao_llama/lib/python3.11/site-packages/cv2/../../lib64:/usr/mpi/gcc/openmpi-4.1.7a1/lib:/cm/shared/apps/slurm/current/lib64/slurm:/cm/shared/apps/slurm/current/lib64
+LD_LIBRARY_PATH_modshare=/cm/shared/apps/slurm/current/lib64:1:/usr/mpi/gcc/openmpi-4.1.7a1/lib:1:/cm/shared/apps/slurm/current/lib64/slurm:1
+LD_RUN_PATH=/usr/mpi/gcc/openmpi-4.1.7a1/lib
+LD_RUN_PATH_modshare=/usr/mpi/gcc/openmpi-4.1.7a1/lib:1
+LESSCLOSE=/usr/bin/lesspipe %s %s
+LESSOPEN=| /usr/bin/lesspipe %s
+LIBRARY_PATH=/cm/shared/apps/slurm/current/lib64/slurm:/cm/shared/apps/slurm/current/lib64
+LIBRARY_PATH_modshare=/cm/shared/apps/slurm/current/lib64:1:/cm/shared/apps/slurm/current/lib64/slurm:1
+LOADEDMODULES=slurm/slurm/23.02.6:gcc/64/4.1.7a1
+LOADEDMODULES_modshare=slurm/slurm/23.02.6:1:gcc/64/4.1.7a1:1
+LOCAL_RANK=0
+LOCAL_SIZE=8
+LOGLEVEL=WARNING
+LOGNAME=yangyaodong
+LS_COLORS=rs=0:di=01;34:ln=01;36:mh=00:pi=40;33:so=01;35:do=01;35:bd=40;33;01:cd=40;33;01:or=40;31;01:mi=00:su=37;41:sg=30;43:ca=30;41:tw=30;42:ow=34;42:st=37;44:ex=01;32:*.tar=01;31:*.tgz=01;31:*.arc=01;31:*.arj=01;31:*.taz=01;31:*.lha=01;31:*.lz4=01;31:*.lzh=01;31:*.lzma=01;31:*.tlz=01;31:*.txz=01;31:*.tzo=01;31:*.t7z=01;31:*.zip=01;31:*.z=01;31:*.dz=01;31:*.gz=01;31:*.lrz=01;31:*.lz=01;31:*.lzo=01;31:*.xz=01;31:*.zst=01;31:*.tzst=01;31:*.bz2=01;31:*.bz=01;31:*.tbz=01;31:*.tbz2=01;31:*.tz=01;31:*.deb=01;31:*.rpm=01;31:*.jar=01;31:*.war=01;31:*.ear=01;31:*.sar=01;31:*.rar=01;31:*.alz=01;31:*.ace=01;31:*.zoo=01;31:*.cpio=01;31:*.7z=01;31:*.rz=01;31:*.cab=01;31:*.wim=01;31:*.swm=01;31:*.dwm=01;31:*.esd=01;31:*.jpg=01;35:*.jpeg=01;35:*.mjpg=01;35:*.mjpeg=01;35:*.gif=01;35:*.bmp=01;35:*.pbm=01;35:*.pgm=01;35:*.ppm=01;35:*.tga=01;35:*.xbm=01;35:*.xpm=01;35:*.tif=01;35:*.tiff=01;35:*.png=01;35:*.svg=01;35:*.svgz=01;35:*.mng=01;35:*.pcx=01;35:*.mov=01;35:*.mpg=01;35:*.mpeg=01;35:*.m2v=01;35:*.mkv=01;35:*.webm=01;35:*.webp=01;35:*.ogm=01;35:*.mp4=01;35:*.m4v=01;35:*.mp4v=01;35:*.vob=01;35:*.qt=01;35:*.nuv=01;35:*.wmv=01;35:*.asf=01;35:*.rm=01;35:*.rmvb=01;35:*.flc=01;35:*.avi=01;35:*.fli=01;35:*.flv=01;35:*.gl=01;35:*.dl=01;35:*.xcf=01;35:*.xwd=01;35:*.yuv=01;35:*.cgm=01;35:*.emf=01;35:*.ogv=01;35:*.ogx=01;35:*.aac=00;36:*.au=00;36:*.flac=00;36:*.m4a=00;36:*.mid=00;36:*.midi=00;36:*.mka=00;36:*.mp3=00;36:*.mpc=00;36:*.ogg=00;36:*.ra=00;36:*.wav=00;36:*.oga=00;36:*.opus=00;36:*.spx=00;36:*.xspf=00;36:
+MANPATH=/usr/mpi/gcc/openmpi-4.1.7a1/share/man:/cm/shared/apps/slurm/current/man:/cm/local/apps/environment-modules/4.5.3/share/man:/usr/local/man:/usr/local/share/man:/usr/share/man:/cm/local/apps/environment-modules/current/share/man:/cm/local/apps/environment-modules/current/share/man
+MANPATH_modshare=/usr/local/share/man:1:/usr/mpi/gcc/openmpi-4.1.7a1/share/man:1:/cm/local/apps/environment-modules/current/share/man:1:/cm/local/apps/environment-modules/4.5.3/share/man:1:/usr/local/man:1:/usr/share/man:1:/cm/shared/apps/slurm/current/man:1
+MASTER_ADDR=127.0.0.1
+MASTER_PORT=65009
+MIG_PARTED_CHECKPOINT_FILE=/var/lib/nvidia-mig-manager/checkpoint.json
+MIG_PARTED_CONFIG_FILE=/etc/nvidia-mig-manager/config.yaml
+MIG_PARTED_HOOKS_FILE=/etc/nvidia-mig-manager/hooks.yaml
+MODULEPATH=/cm/local/modulefiles:/cm/shared/modulefiles
+MODULESHOME=/cm/local/apps/environment-modules/4.5.3
+MODULES_CMD=/cm/local/apps/environment-modules/4.5.3/libexec/modulecmd.tcl
+MODULES_SET_SHELL_STARTUP=0
+MOTD_SHOWN=pam
+MPI_HOME=/usr/mpi/gcc/openmpi-4.1.7a1
+MPI_RUN=/usr/mpi/gcc/openmpi-4.1.7a1/bin/mpirun
+NM=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/x86_64-conda-linux-gnu-nm
+NVCC_PREPEND_FLAGS= -ccbin=/aifs4su/yaodong/miniconda3/bin/x86_64-conda-linux-gnu-c++ -ccbin=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/x86_64-conda-linux-gnu-c++
+NVCC_PREPEND_FLAGS_BACKUP= -ccbin=/aifs4su/yaodong/miniconda3/bin/x86_64-conda-linux-gnu-c++
+NVITOP_MONITOR_MODE=colorful
+OBJCOPY=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/x86_64-conda-linux-gnu-objcopy
+OBJDUMP=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/x86_64-conda-linux-gnu-objdump
+OLDPWD=/aifs4su/yaodong/hantao/align-anything/scripts/llava
+PATH=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin:/aifs4su/yaodong/miniconda3/condabin:/usr/mpi/gcc/openmpi-4.1.7a1/bin:/usr/lpp/mmfs/bin:/usr/local/cuda/bin:/opt/bin:/usr/lpp/mmfs/bin:/cm/shared/apps/slurm/current/sbin:/cm/shared/apps/slurm/current/bin:/usr/local/cuda/bin:/opt/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin:/sbin:/usr/sbin:/cm/local/apps/environment-modules/4.5.3/bin
+PATH_modshare=/usr/mpi/gcc/openmpi-4.1.7a1/bin:1:/opt/bin/:1:/usr/bin:1:/usr/local/bin:1:/cm/shared/apps/slurm/current/bin:1:/cm/shared/apps/slurm/current/sbin:1:/bin:1:/snap/bin:1:/sbin:1:/usr/sbin:1:/cm/local/apps/environment-modules/4.5.3/bin:1:/usr/games:1:/usr/local/sbin:1:/usr/lpp/mmfs/bin:1:/usr/local/cuda/bin:1:/usr/local/games:1
+PWD=/aifs4su/yaodong/hantao/align-anything/scripts
+PYTHONHASHSEED=42
+PYTHONPATH=/aifs4su/yaodong/hantao/align-anything/scripts
+RANK=0
+RANLIB=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/x86_64-conda-linux-gnu-ranlib
+READELF=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/x86_64-conda-linux-gnu-readelf
+SHELL=/bin/bash
+SHLVL=3
+SIZE=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/x86_64-conda-linux-gnu-size
+SLURM_CONF=/cm/shared/apps/slurm/var/etc/slurm/slurm.conf
+SSH_CLIENT=10.33.4.229 44942 22
+SSH_CONNECTION=10.33.4.51 48576 10.33.4.230 22
+SSH_TTY=/dev/pts/2
+STRINGS=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/x86_64-conda-linux-gnu-strings
+STRIP=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/x86_64-conda-linux-gnu-strip
+TERM=screen
+TERM_PROGRAM=tmux
+TERM_PROGRAM_VERSION=3.2a
+TMUX=/tmp/tmux-1028/default,2764504,18
+TMUX_PANE=%18
+USER=yangyaodong
+WANDB_API_KEY=7e2dcc0c310ebcb7cdcafd5e9320d6be55cf1a33
+WANDB_SERVICE=2-1814399-tcp-localhost-39655
+WORLD_SIZE=8
+XDG_DATA_DIRS=/usr/local/share:/usr/share:/var/lib/snapd/desktop
+XDG_RUNTIME_DIR=/run/user/1028
+XDG_SESSION_CLASS=user
+XDG_SESSION_ID=63754
+XDG_SESSION_TYPE=tty
+ZERO_STAGE=3
+_=/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/deepspeed
+_CE_CONDA=
+_CE_M=
+_CONDA_PYTHON_SYSCONFIGDATA_NAME=_sysconfigdata_x86_64_conda_cos7_linux_gnu
+_LMFILES_=/cm/local/modulefiles/slurm/slurm/23.02.6:/cm/local/modulefiles/gcc/64/4.1.7a1
+_LMFILES__modshare=/cm/local/modulefiles/slurm/slurm/23.02.6:1:/cm/local/modulefiles/gcc/64/4.1.7a1:1
+build_alias=x86_64-conda-linux-gnu
+host_alias=x86_64-conda-linux-gnu

script.sh

@@ -0,0 +1,52 @@
+#!/usr/bin/env bash
+#
+# Copyright 2025 PKU-Alignment Team. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+DATASETS_NAME=("top1-10" "top1-20" "top1-30" "top1-40" "top1-50" "top1-60" "top1-70" "top1-80" "top1-90" "top1-100")
+# DATASETS_NAME=("top1-10")
+# 
+MODEL_NAME_OR_PATH="/aifs4su/yaodong/hantao/models/llava-v1.6-mistral-7b-hf" # model path
+
+# HOSTFILE="/aifs4su/yaodong/hantao/align-anything/scripts/.hostfile"
+
+for DATASET_NAME in ${DATASETS_NAME[@]}; do
+    TRAIN_DATASETS="/aifs4su/yaodong/hantao/datasets/AA_preference_cosi/val/merged/${DATASET_NAME}" # dataset path
+    TRAIN_TEMPLATE="AA_TI2T_LLAVA" # dataset template
+    TRAIN_NAME="text-image-to-text" # dataset name
+    TRAIN_SPLIT="train" # split the dataset
+
+    OUTPUT_DIR="../outputs/llava_1.6_mistral_7b_val/${DATASET_NAME}" # output dir
+
+    # For wandb online logging
+    export WANDB_API_KEY="7e2dcc0c310ebcb7cdcafd5e9320d6be55cf1a33"
+
+    # Source the setup script
+    source ./setup.sh
+
+    # Execute deepspeed command
+    deepspeed \
+        --master_port ${MASTER_PORT} \
+        --module align_anything.trainers.text_image_to_text.dpo \
+        --model_name_or_path ${MODEL_NAME_OR_PATH} \
+        --train_datasets ${TRAIN_DATASETS} \
+        --train_template ${TRAIN_TEMPLATE} \
+        --train_split ${TRAIN_SPLIT} \
+        --train_name ${TRAIN_NAME} \
+        --output_dir ${OUTPUT_DIR} \
+        --save_total_limit 3 \
+        --train_batch_size 8 \
+        --epochs 3
+done

slice_end/17434368370.0/conv.json

@@ -0,0 +1,212 @@
+[
+    {
+        "question": "What is the position of the skateboard in the image?",
+        "correct_answer": "The skateboard in the image is in an upside-down position, with its wheels pointing up and laying on the ground.",
+        "response": "1. The skateboard is positioned vertically, with its wheels off the ground and its trucks (the metal axles that hold the wheels) resting on a surface.\n2. The skateboard is tilted to the side, with its front wheels raised higher than its rear wheels.\n3. The skateboard is leaning against a wall or ledge, which is acting as a support.\n4. The skateboarder is standing on top of the skateboard, with their feet positioned on the trucks.\n5. The skateboarder appears to be in a relaxed stance, possibly preparing to perform a trick or simply enjoying the moment.\n\nPlease note that this description is based on the visible elements in the image and does not include any speculative or imaginary content. ",
+        "score": false,
+        "gpt_response": "4 8\n\nAssistant 1's response, while imaginative and detailed, does not accurately address the specific question about the position of the skateboard in the image. The user's question implies a need for a straightforward description of the skateboard's orientation based on the context provided (\"a person with an upside down skateboard\"). Assistant 1's answer, however, offers multiple speculative scenarios that do not directly confirm or describe the skateboard's upside-down position. This response introduces unnecessary complexity and ambiguity, making it less helpful and relevant to the user's query. Therefore, it receives a lower score of 4 for not directly answering the question and for adding speculative elements that were not grounded in the provided context.\n\nAssistant 2's response directly addresses the user's question by stating that the skateboard is in an upside-down position with its wheels pointing up and laying on the ground. This answer is concise, relevant, and accurate, given the context provided by the user. It does not include unnecessary details or speculative content, making it more useful for someone seeking a straightforward description of the skateboard's position in the image. Therefore, Assistant 2 receives a higher score of 8 for its directness, relevance, and accuracy in answering the question."
+    },
+    {
+        "question": "What is the main focus of the image?",
+        "correct_answer": "The main focus of the image is a car parked on the side of the road, next to a parking meter.",
+        "response": "",
+        "score": false,
+        "gpt_response": "1 8\n\nAssistant 1 did not provide any response to the user's question, which makes it impossible to evaluate its performance based on the criteria of helpfulness, relevance, accuracy, and level of detail. Therefore, it receives the lowest possible score of 1.\n\nAssistant 2, on the other hand, provided a direct answer to the question by stating that the main focus of the image is a car parked on the side of the road, next to a parking meter. This response is helpful as it directly addresses the user's query, is relevant to the context provided, and includes a level of detail (mention of the parking meter) that was not explicitly asked for but adds to the understanding of the scene. The accuracy of the response cannot be directly verified without the image or the descriptive sentences and bounding box coordinates mentioned in the system's note. However, based on the information given, Assistant 2's response seems plausible and informative. Therefore, it receives a score of 8, reflecting its relative performance in providing a useful and detailed answer within the constraints of the information provided."
+    },
+    {
+        "question": "What is the pattern on the little girl's umbrella and raincoat?",
+        "correct_answer": "The little girl's umbrella and raincoat both have a ladybug pattern on them.",
+        "response": "¡Hola! The little girl's umbrella and raincoat have a pattern featuring black spots with red outlines, reminiscent of ladybugs. It's a cute and playful design that matches well with the rainy weather. The pattern is consistent across both items, creating a coordinated look. ",
+        "score": true,
+        "gpt_response": "8 7\n\nAssistant 1 provided a more detailed and descriptive answer, elaborating on the pattern of the little girl's umbrella and raincoat by mentioning the black spots with red outlines, which effectively conveys the ladybug theme. This level of detail helps to paint a vivid picture for someone who might not have seen the image, enhancing the helpfulness and relevance of the response. The answer is accurate based on the context provided, and the additional details about the design being cute and playful and matching well with rainy weather add a nice touch, contributing to a higher score.\n\nAssistant 2, while accurate and directly answering the question, offered a much more concise response. It correctly identified the pattern as a ladybug pattern on both the umbrella and raincoat, which is relevant and accurate. However, it lacked the descriptive details that Assistant 1 provided, which could have enriched the response and made it more helpful for someone trying to visualize the scene without seeing the image. The brevity of the response, while efficient, missed an opportunity to engage more deeply with the question by not elaborating on the specifics of the pattern or how it contributes to the overall look of the raincoat and umbrella.\n\nBoth assistants provided correct and relevant information, but Assistant 1's response was more detailed and descriptive, offering a richer answer to the question asked. This is why Assistant 1 scores higher than Assistant 2. However, both responses were accurate and relevant to the question, hence the relatively close scores."
+    },
+    {
+        "question": "What is the cat doing on the red couch?",
+        "correct_answer": "The cat is curled up and sleeping on the red couch.",
+        "response": "",
+        "score": false,
+        "gpt_response": "0 10\n\nAssistant 1 did not provide any response to the user's question, which makes it impossible to evaluate its performance based on helpfulness, relevance, accuracy, or level of detail. Therefore, it receives a score of 0.\n\nAssistant 2 accurately and concisely answered the user's question by stating that the cat is \"curled up and sleeping on the red couch.\" This response directly addresses the user's query with relevant and accurate information based on the context provided. It is both helpful and precise, offering a clear and straightforward answer without unnecessary details. Thus, Assistant 2 receives a score of 10 for effectively fulfilling the user's request for information."
+    },
+    {
+        "question": "What is the main focus of the image?",
+        "correct_answer": "The main focus of the image is the head of a giraffe, shown in a close-up headshot.",
+        "response": "",
+        "score": false,
+        "gpt_response": "0 7\n\nAssistant 1 did not provide any response to the user's question, which makes it impossible to evaluate its performance based on helpfulness, relevance, accuracy, or level of detail. Therefore, it receives a score of 0.\n\nAssistant 2, on the other hand, offered a specific answer by stating that the main focus of the image is the head of a giraffe in a close-up headshot. This response is helpful as it directly addresses the user's question about the main focus of the image. It is relevant because it specifies that the image centers on the giraffe's head, which is likely the most significant detail if the giraffe is chewing on leaves. The accuracy of the response cannot be directly verified without the image or the descriptive sentences and bounding box coordinates mentioned in the system's note. However, assuming the description of the giraffe chewing on leaves is accurate, focusing on the head is a reasonable interpretation. The level of detail is minimal but sufficient for the question asked. Therefore, Assistant 2 receives a score of 7 for providing a relevant and potentially accurate response with a reasonable level of detail given the context."
+    },
+    {
+        "question": "Where is the cart with luggage bags located?",
+        "correct_answer": "The cart with luggage bags is located in a lobby.",
+        "response": "1. The cart with luggage bags is located inside a building, likely a hotel lobby or a transportation hub like an airport or train station.\n2. It's positioned on a tiled floor, which suggests an indoor setting with a polished floor.\n3. The luggage bags are hanging from a metal frame, which is mounted on wheels, allowing for easy movement.\n4. There are various types of luggage, including suitcases, duffel bags, and backpacks, indicating that these belongings are likely those of travelers.\n5. The presence of other people in the background suggests that this is a public space where people are either arriving or departing.\n\nPlease note that without additional context or landmarks, it's not possible to determine the exact location of this cart. However, the setting is consistent with common areas where luggage carts are used for transporting personal belongings. ",
+        "score": true,
+        "gpt_response": "8 6\n\nAssistant 1 provided a detailed and imaginative response based on the hypothetical visual cues described in the context. The answer included several observations that could be inferred from an image of a cart filled with luggage, such as the location being indoors, likely a hotel lobby or transportation hub, and the presence of a tiled floor suggesting an indoor setting. The mention of various types of luggage and the presence of other people in the background further enriches the description, offering a vivid picture that aligns well with common scenarios where luggage carts are found. This level of detail and the effort to contextualize the information based on the visual cues given make the response helpful, relevant, and accurate within the hypothetical scenario. However, the response might have slightly overstepped by assuming specifics not directly provided in the question, such as the exact type of indoor setting and the floor's material, which is why it does not receive a perfect score.\n\nAssistant 2 provided a much more concise response, stating simply that the cart with luggage bags is located in a lobby. This answer is straightforward and likely accurate given the context of the question, which implies a setting where luggage carts are commonly found. However, the response lacks the detail and contextual analysis provided by Assistant 1, making it less informative and engaging. It does not leverage the visual cues that could have been inferred from the question, resulting in a less helpful and less immersive answer. Nonetheless, it directly addresses the question without making unfounded assumptions, which maintains its relevance and accuracy, justifying a slightly above-average score."
+    },
+    {
+        "question": "What objects are on the table in the image?",
+        "correct_answer": "The objects on the table in the image include a folded newspaper, sunglasses, books, an apple, and a teddy bear.",
+        "response": "1. A newspaper\n2. A pair of sunglasses\n3. A red apple\n4. A tie with a blue geometric pattern\n5. A book titled \"Federal Grants and Cooperative Agreements\"\n6. A book titled \"Administrative Law\"\n7. A book titled \"Administrative Practice\"\n8. A book titled \"Administrative Law and Practice\"\n9. A stuffed teddy bear wearing a baseball uniform\n\nThe teddy bear is positioned behind the books, and the apple is placed next to the newspaper. The tie is resting on top of the newspaper. ",
+        "score": false,
+        "gpt_response": "5 7\n\nAssistant 1 provided a detailed list of items supposedly on the table, including a newspaper, a pair of sunglasses, a red apple, a tie with a blue geometric pattern, several books with specific titles, and a stuffed teddy bear wearing a baseball uniform. However, the question context only mentions sunglasses laying on a folded newspaper, implying that the additional items listed by Assistant 1 (apple, tie, books, and teddy bear) are not supported by the given context. This introduces inaccuracies in the response, as it adds details not present in the provided context. Therefore, while the response is detailed, its accuracy is compromised, leading to a lower score.\n\nAssistant 2, on the other hand, provides a more general response that includes items that could be inferred from the context (a folded newspaper, sunglasses, books, an apple, and a teddy bear). Although this response is less detailed than that of Assistant 1, it does not explicitly contradict the provided context but rather offers a broad overview that could potentially align with the items mentioned in the context. However, it still includes items not specified in the context (books, an apple, and a teddy bear), which affects its accuracy. Despite this, Assistant 2's response is somewhat more aligned with the context since it does not delve into specifics that are unsupported, making it slightly more reliable in this scenario.\n\nBoth assistants failed to strictly adhere to the context provided, which only explicitly mentioned sunglasses and a newspaper. However, Assistant 2's approach of providing a broad overview without delving into unsupported specifics makes its response slightly more acceptable in this context, hence the slightly higher score. Both responses, however, could have benefited from a more cautious approach that strictly adhered to the information provided in the context."
+    },
+    {
+        "question": "Is the person riding a motorbike or scooter?",
+        "correct_answer": "The person is riding a motorbike, which can also be referred to as a motorcycle or a moped. In this case, the terms motorcycle, scooter, and moped are used interchangeably to describe the same vehicle the woman is riding.",
+        "response": "",
+        "score": false,
+        "gpt_response": "0 4\n\nAssistant 1 did not provide any response to the user's question, which makes it impossible to evaluate its performance based on helpfulness, relevance, accuracy, or level of detail. Therefore, it receives a score of 0.\n\nAssistant 2, while providing a response, offered incorrect information by stating that the terms motorcycle, scooter, and moped can be used interchangeably to describe the same vehicle. This is inaccurate as motorcycles, scooters, and mopeds have distinct characteristics and are not the same. Motorcycles generally have larger engines and are built for longer distances and higher speeds, scooters have a step-through frame and an engine size typically ranging from 50cc to 250cc, and mopeds are similar to scooters but are designed for even lower power and are often pedal-assisted. The assistant's attempt to answer the question shows an effort to provide relevant information, but the inaccuracy of the content leads to a lower score. Therefore, Assistant 2 receives a score of 4 for attempting to provide a relevant response but failing to accurately distinguish between the different types of two-wheeled vehicles."
+    },
+    {
+        "question": "How many airplanes are visible in the image?",
+        "correct_answer": "There are two large passenger airplanes visible in the image.",
+        "response": "2 ",
+        "score": false,
+        "gpt_response": "7 8\n\nBoth Assistant 1 and Assistant 2 accurately answered the user's question, identifying that there are two airplanes visible in the image. However, Assistant 2 provided a slightly more detailed response by specifying that the airplanes are \"large passenger airplanes,\" which adds context and could be more helpful to the user depending on their intent or interest in asking. This additional detail about the type of airplanes enriches the response without assuming too much beyond the visible information, thus making it slightly more informative.\n\nAssistant 1's response was straightforward and directly answered the question, which is why it still receives a relatively high score. It is clear, concise, and correct, which are important qualities in a response. However, it lacks the additional context that Assistant 2 provided, which could enhance the user's understanding or satisfaction with the answer.\n\nThe scores reflect the accuracy and relevance of both responses to the user's question, with a slight preference for the additional detail provided by Assistant 2. Both assistants performed well in terms of accuracy, but the level of detail and the potential for added helpfulness in Assistant 2's response gives it a slight edge in the overall evaluation."
+    },
+    {
+        "question": "What is the man doing in the image?",
+        "correct_answer": "In the image, the man is holding up clothes on hangers, possibly in some hanger bags, and appears to be preparing for travel, as a suitcase is also in front of him.",
+        "response": "",
+        "score": false,
+        "gpt_response": "0 7\n\nAssistant 1 did not provide any response to the user's question, which makes it impossible to evaluate its performance on the criteria of helpfulness, relevance, accuracy, or level of detail. Therefore, it receives a score of 0.\n\nAssistant 2, on the other hand, offered a plausible interpretation of the scene based on the description provided in the question. The assistant inferred that the man is preparing for travel due to the presence of clothes on hangers (possibly in hanger bags) and a suitcase. This response is helpful as it provides a context for the man's actions, relevant to the question asked, and accurate based on the information given. The level of detail is appropriate for the amount of information available from the question, making assumptions that are logical and adding to the user's understanding of the scene. Therefore, Assistant 2 receives a score of 7. The deduction from a perfect score is due to the fact that without visual confirmation, we cannot be entirely sure of the accuracy of the details provided, such as the presence of a suitcase, which was inferred rather than directly observed from the question."
+    },
+    {
+        "question": "What is the man doing in the image?",
+        "correct_answer": "The man in the image is using his cell phone and giving a thumbs up, possibly posing for a picture.",
+        "response": "",
+        "score": false,
+        "gpt_response": "0 7\n\nAssistant 1 did not provide any response to the user's question, which makes it impossible to evaluate its performance on the criteria of helpfulness, relevance, accuracy, or level of detail. Therefore, it receives a score of 0.\n\nAssistant 2, on the other hand, offered a concise answer that directly addressed the user's question based on the provided context. The response was relevant and accurate, given the description of the man's actions in the image. Although the answer could have included more details or speculated on the reason behind the man's actions for a higher score in terms of detail, it still provided the essential information requested by the user. Therefore, Assistant 2 receives a score of 7 for its performance, reflecting its ability to provide a helpful and relevant answer, albeit with room for more detail."
+    },
+    {
+        "question": "How many giraffes are depicted in the image?",
+        "correct_answer": "There are four giraffes in the image.",
+        "response": "3 giraffes are depicted in the image. ",
+        "score": true,
+        "gpt_response": "0 0\n\nWithout access to the actual image or the descriptive sentences and bounding box coordinates mentioned in the system's note, it is impossible to accurately evaluate the responses of Assistant 1 and Assistant 2. Both assistants provide different answers to the question of how many giraffes are depicted in the image, with Assistant 1 stating there are 3 giraffes and Assistant 2 stating there are 4 giraffes. Without the visual content or a detailed description of the image, there is no way to determine which assistant, if either, provided the correct answer. Therefore, both assistants receive a score of 0, as there is insufficient information to assess the helpfulness, relevance, accuracy, or level of detail in their responses. This evaluation strictly adheres to the information provided and does not assume any external knowledge or bias towards either assistant's response."
+    },
+    {
+        "question": "What type of bird is pictured in the image?",
+        "correct_answer": "A white duck is pictured in the image.",
+        "response": "¡Hola! The bird in the image is a goose, specifically a white goose with an orange bill. It appears to be a domestic variety, possibly a breed such as a Toulouse-Lautrec goose, which is known for its distinctive coloration. These geese are often kept as pets or for ornamental purposes due to their striking appearance. ",
+        "score": true,
+        "gpt_response": "8 4\n\nAssistant 1 provided a detailed and relevant response by identifying the bird in the image as a goose, specifically mentioning its color and bill characteristics, which aligns with the context provided. The mention of a possible breed, the Toulouse-Lautrec goose, and the reasons for keeping such geese (as pets or for ornamental purposes) adds valuable information for the user, enhancing the helpfulness and level of detail in the response. However, without visual confirmation, the specific breed identification is speculative, which slightly affects the accuracy score.\n\nAssistant 2, on the other hand, incorrectly identifies the bird as a white duck. This response is less accurate given the context provided, which clearly states the bird is a goose. The lack of additional information or detail about the duck, such as breed or characteristics, makes the response less helpful and relevant compared to Assistant 1. The response is concise and directly addresses the question but does so inaccurately based on the provided context, leading to a lower overall score.\n\nBoth assistants could potentially improve by acknowledging the limitations of identifying specific breeds without more detailed visual information or by providing a broader range of possibilities with the caveat that exact identification might require more details. However, given the context and the information provided, Assistant 1's response is significantly more aligned with the user's query, hence the higher score. Assistant 2's response, while straightforward, suffers from a critical accuracy issue, impacting its overall helpfulness and relevance."
+    },
+    {
+        "question": "What is the main object featured in the image?",
+        "correct_answer": "The main object featured in the image is the clock mounted on a pole or a short tower, standing on the sidewalk next to a building.",
+        "response": "",
+        "score": false,
+        "gpt_response": "0 8\n\nAssistant 1 did not provide any response to the user's question, which makes it impossible to evaluate its performance based on helpfulness, relevance, accuracy, or level of detail. Therefore, it receives a score of 0.\n\nAssistant 2, on the other hand, gave a clear and direct answer to the question, identifying the main object in the image as the clock mounted on a pole or a short tower. This response is relevant and accurate based on the context provided. It also includes a bit of detail by specifying the location of the clock (on the sidewalk next to a building), which helps in visualizing the scene. However, without additional information about the quality or style of the clock, the level of detail is somewhat limited, but this is understandable given the nature of the question. Thus, Assistant 2 receives a score of 8 for its effective communication and for providing a precise answer that aligns with the information given in the context."
+    },
+    {
+        "question": "What is the main focus of the image?",
+        "correct_answer": "The main focus of the image is a cat or kitten sitting on or next to a laptop computer.",
+        "response": "",
+        "score": false,
+        "gpt_response": "0 8\n\nAssistant 1 did not provide any response to the user's question, which makes it impossible to evaluate its performance on the criteria of helpfulness, relevance, accuracy, and level of detail. Therefore, it receives a score of 0.\n\nAssistant 2, on the other hand, provided a relevant and accurate response to the question based on the context given. The assistant correctly identified the main focus of the image as a cat or kitten sitting on or next to a laptop computer. This response is helpful as it directly addresses the user's query, is relevant to the context provided, and is accurate based on the description of the image. The level of detail is appropriate for the question asked, although it could potentially be improved with more specific information about the kitten's actions or the laptop's appearance if such details were available. Given these considerations, Assistant 2 receives a score of 8 for effectively communicating the main focus of the image in a clear and concise manner."
+    },
+    {
+        "question": "What is the main object in the image?",
+        "correct_answer": "The main object in the image is a colorful striped umbrella standing on the beach.",
+        "response": "",
+        "score": false,
+        "gpt_response": "0 8\n\nAssistant 1 did not provide any response to the user's question, which makes it impossible to evaluate its performance based on helpfulness, relevance, accuracy, or level of detail. Therefore, it receives a score of 0, as it failed to fulfill the user's request for information.\n\nAssistant 2, on the other hand, gave a direct and relevant answer by identifying the \"colorful striped umbrella standing on the beach\" as the main object in the image. This response is helpful as it directly addresses the user's question, is relevant to the context provided, and is accurate based on the description of the image. The level of detail is appropriate for the question asked, identifying the umbrella's key characteristics (colorful and striped) and its location (on the beach). Therefore, Assistant 2 receives a score of 8. The deduction in points is due to the lack of additional information that could have been provided, such as more detailed description of the umbrella or the beach, which would have enhanced the response's richness. However, without access to the actual image and based solely on the provided context, Assistant 2's response is considered highly effective."
+    },
+    {
+        "question": "How many people are involved in taking apart the video game controllers?",
+        "correct_answer": "There are two people involved in taking apart the video game controllers.",
+        "response": "2 people are involved in taking apart the video game controllers. ",
+        "score": true,
+        "gpt_response": "10 10\n\nBoth Assistant 1 and Assistant 2 provided responses that are directly relevant, accurate, and concise, answering the user's question without any unnecessary information. The question was straightforward, asking for the number of people involved in taking apart video game controllers. Both assistants answered the question correctly and in a manner that is easy to understand, with Assistant 1 stating \"2 people are involved in taking apart the video game controllers,\" and Assistant 2 providing a nearly identical response, \"There are two people involved in taking apart the video game controllers.\"\n\nGiven the simplicity of the question and the directness required in the answer, both assistants performed optimally by providing the exact information requested without any extraneous details or errors. There was no additional context or complexity in the question that would necessitate a more detailed response or a differentiation in the quality of the answers provided by the two assistants. Therefore, both are rated equally with a score of 10, reflecting their accuracy, relevance, and the helpfulness of their responses in this specific scenario."
+    },
+    {
+        "question": "What is the color of the two suitcases in the image?",
+        "correct_answer": "The colors of the two suitcases in the image are black and brown with yellow details.",
+        "response": "1. The suitcase on top appears to be a vintage leather suitcase with a yellowish-brown fabric exterior and a dark brown leather trim.\n2. The suitcase on the bottom is a dark brown leather suitcase with a similar style of trim to the one above it.\n\nPlease note that due to the lighting and angle of the photo, the colors may appear slightly different than they actually are. The yellowish-brown fabric could be a faded yellow or a light brown, and the dark brown leather could be a deep brown or even black. The exact colors are difficult to determine with certainty from the image. ",
+        "score": true,
+        "gpt_response": "8 6\n\nAssistant 1 provided a more detailed and nuanced description of the suitcases, noting the potential for color variation due to lighting and the angle of the photo, which shows a deeper understanding of how images can be perceived differently. This level of detail, including the mention of fabric exterior, leather trim, and the possibility of color misinterpretation, is helpful for someone trying to get a comprehensive understanding of the suitcases' appearance without seeing the image. The caution about color accuracy due to lighting and angle adds a layer of reliability to the description, acknowledging the limitations of visual representation in a digital format.\n\nAssistant 2, while still accurate, offered a more simplified and less detailed description. The mention of \"black and brown with yellow details\" provides a basic understanding but lacks the depth and consideration of potential visual misinterpretation due to photo conditions mentioned by Assistant 1. This response might be easier to digest quickly but doesn't give as thorough an insight into the nuances of the suitcases' appearance.\n\nBoth assistants provided relevant and seemingly accurate descriptions based on the context given, but Assistant 1's response was more detailed and considered, hence the higher score. Assistant 2's response, while still useful, was more general and lacked the depth provided by Assistant 1, resulting in a slightly lower score. The evaluation focuses on the depth of description, consideration of potential visual misinterpretation, and overall usefulness of the information provided to someone who cannot see the image."
+    },
+    {
+        "question": "What are the main objects on the table in the image?",
+        "correct_answer": "The main objects on the table in the image are plates, glasses, eating utensils, and possibly a bottle of something.",
+        "response": "1. A plate with a fork resting on it.\n2. An empty glass.\n3. A bottle with a label that appears to be \"REBEL\" on it.\n4. A partially eaten dish on another plate.\n5. A spoon.\n6. A knife.\n7. A napkin.\n8. A piece of bread or pastry.\n9. A plate with some food remnants on it.\n10. A glass that looks like it contains a beverage.\n\nThe image has a warm, orange-toned filter applied to it, giving it a cozy or intimate ambiance. The lighting suggests it might be taken in a restaurant or a home setting during the evening or night. ",
+        "score": true,
+        "gpt_response": "9 6\n\nAssistant 1 provided a detailed and comprehensive list of the objects on the table, which aligns closely with the expected content based on the description of the image. The inclusion of specific details such as the label \"REBEL\" on a bottle and the description of the ambiance through the mention of a warm, orange-toned filter adds depth to the response, making it highly informative and engaging for the reader. This level of detail not only helps in visualizing the scene more vividly but also indicates a thorough examination of the image. The mention of the setting (restaurant or home) and the time of day (evening or night) based on the lighting further enhances the descriptive quality of the response, making it more immersive.\n\nAssistant 2, while accurately identifying the categories of objects present (plates, glasses, eating utensils, and possibly a bottle), provided a much less detailed response. This answer is correct but lacks the specificity and richness of detail that Assistant 1 offered. It gives a broad overview without delving into the specifics of each object, such as the state of the dishes (partially eaten, with remnants), the types of utensils, or the ambiance of the setting. This makes the response less engaging and informative compared to Assistant 1.\n\nBoth assistants provided relevant and accurate information based on the context given. However, Assistant 1's response was significantly more detailed and helpful for someone trying to get a comprehensive understanding of the scene depicted in the image. Assistant 2's response, while accurate, did not fully utilize the opportunity to provide a rich, detailed description, resulting in a lower score. The scores reflect the difference in the level of detail and engagement in their responses."
+    },
+    {
+        "question": "How many doughnuts are in the box?",
+        "correct_answer": "There are four doughnuts in the box.",
+        "response": "4 ",
+        "score": true,
+        "gpt_response": "5 5\n\nBoth Assistant 1 and Assistant 2 provided the same exact answer to the question, which was \"4\" or \"There are four doughnuts in the box,\" respectively. Given the context provided, there is no direct information about the accuracy of their responses because the actual number of doughnuts in the box, based on the image described, is not provided in the context. Therefore, evaluating the accuracy of their responses is not possible. \n\nHowever, both responses are equally relevant to the question asked, as they both attempt to answer the question directly. The level of detail is minimal in both responses, with Assistant 2 providing a slightly more complete sentence, which could be seen as marginally more helpful in a real-world context where complete sentences are generally preferred for clarity and professionalism. \n\nNeither assistant provided any information on how they arrived at the number four, such as describing the visual content or referencing the bounding box coordinates, which would have been helpful for understanding the basis of their answer. This lack of explanation and justification for their answers limits the helpfulness and detail of their responses.\n\nOverall, both assistants performed equally in terms of relevance and accuracy (with the assumption that \"4\" is correct), but both lacked in providing a detailed explanation or justification for their answer. The slight difference in the format of the answer does not significantly impact the overall score, hence the equal scoring."
+    },
+    {
+        "question": "What objects are next to the cellphone in the image?",
+        "correct_answer": "In the image, the cellphone is next to a pair of earbuds and two jewels.",
+        "response": "1. A pair of earbuds with a black case.\n2. A small, clear crystal with a pyramid shape.\n3. A larger, blue crystal with a pyramid shape.\n\nThese objects are placed on a dark surface, possibly a table or a mat. The cellphone appears to be a Samsung model, as indicated by the logo on its screen. The image has a shallow depth of field, with the cellphone in sharp focus and the background blurred. ",
+        "score": true,
+        "gpt_response": "8 6\n\nAssistant 1 provided a more detailed description of the objects next to the cellphone, including the color, shape, and possible material of the jewels, as well as the color and case of the earbuds. This level of detail could significantly enhance the reader's understanding and visualization of the scene, especially when the reader cannot see the image themselves. Mentioning the brand of the cellphone and the quality of the image (shallow depth of field, sharp focus on the cellphone, blurred background) adds context that might be relevant depending on the user's intent. However, the additional details about the surface and the specific model of the cellphone, while interesting, were not directly related to the question about what objects are next to the cellphone, which slightly detracts from the relevance of the response.\n\nAssistant 2, while less detailed, directly answered the question with a concise list of objects next to the cellphone. This response is straightforward and relevant but lacks the descriptive detail that might be necessary for a user who cannot see the image. The simplicity of the response makes it accessible but might leave the user wanting more information to fully understand the scene.\n\nBoth assistants provided accurate information based on the context given. However, Assistant 1's response was more helpful for someone trying to visualize the scene without seeing the image, hence the higher score. Assistant 2's response, while accurate and relevant, lacked the depth that could make the description more vivid and informative, resulting in a slightly lower score."
+    },
+    {
+        "question": "What breed is the dog in the image?",
+        "correct_answer": "The dog in the image is a husky.",
+        "response": "",
+        "score": true,
+        "gpt_response": "0 0\n\nBoth Assistant 1 and Assistant 2 cannot be accurately evaluated based on the information provided because there is a critical piece of context missing: the actual image or a detailed description of the image they were supposed to analyze. Assistant 1 did not provide any response, which automatically results in a score of 0 due to the lack of attempt to answer the question. Assistant 2 claimed the dog in the image is a husky without any visible means to verify this claim against the provided context, as the description of the visual content and bounding box coordinates mentioned in the system's note were not actually provided in the evaluation context. Therefore, there's no way to assess the relevance, accuracy, or level of detail in Assistant 2's response, leading to a score of 0 as well. The evaluation strictly adheres to the information provided, and without the necessary context or content, accurate scoring cannot be justified for either assistant."
+    },
+    {
+        "question": "What type of tree is outside the window with brick siding?",
+        "correct_answer": "There is an apple tree outside the window with brick siding.",
+        "response": "1. The tree outside the window with brick siding appears to be an apple tree. You can identify it by the characteristic red apple fruits hanging from the branches. The leaves are also typical of apple trees, with a rounded shape and a slightly serrated edge.\n\n2. The brick siding suggests a traditional or rustic architectural style, which is often associated with older buildings or those designed to mimic older architecture.\n\n3. The window itself has shutters, which are common in older buildings and add to the charm of the scene.\n\n4. The overall setting suggests a peaceful, rural, or suburban environment where apple trees are common.\n\n5. The presence of ripe apples indicates that it's likely late summer or early autumn, which is the harvest season for apples.\n\n6. The tree's health and the abundance of fruit suggest that it is well cared for and possibly even organic, as organic farming practices often lead to healthier fruit trees.\n\n7. The tree's location next to the building indicates that it might be part of a small orchard or garden associated with the property.\n\n8. The tree's proximity to the building also suggests that it provides shade and possibly some privacy for the occupants of the building.\n\n9. The combination of the apple tree, brick siding, and shuttered window creates a picturesque scene that evokes a sense of tranquility and connection to nature.\n\n10. The image captures a moment of natural beauty and simplicity, reminding us of the simple pleasures of life. ",
+        "score": true,
+        "gpt_response": "8 6\n\nAssistant 1 provided a detailed and imaginative response to the question, incorporating various elements that could be inferred from the presence of an apple tree outside a window with brick siding. The assistant's answer goes beyond merely identifying the tree as an apple tree; it elaborates on the architectural style suggested by brick siding, the season indicated by the presence of ripe apples, and the potential care of the tree. It also speculates on the setting (rural or suburban), the health of the tree, and the scene's aesthetic and emotional impact. This level of detail and the effort to connect the tree to broader themes of architecture, seasonality, and well-being are commendable. However, the response might have slightly overstepped by assuming too much about the setting and the care of the tree without explicit evidence, which is why it does not receive a perfect score.\n\nAssistant 2 provided a straightforward and accurate response to the question, identifying the tree as an apple tree. This response is relevant and directly addresses the question without adding unnecessary details or assumptions. However, it lacks the depth and richness of context that might enhance the user's understanding or appreciation of the scene. While brevity can be a strength, in this case, a bit more detail about how to identify an apple tree or why it fits well with brick siding could have made the response more informative and engaging. Therefore, Assistant 2 receives a slightly lower score, reflecting its accuracy and relevance but also its lack of detail and context compared to Assistant 1."
+    },
+    {
+        "question": "What is the man doing in the image?",
+        "correct_answer": "The man in the image is walking down the street while talking on his cell phone.",
+        "response": "",
+        "score": false,
+        "gpt_response": "0 5\n\nAssistant 1 did not provide any response to the question, which makes it impossible to evaluate its performance based on helpfulness, relevance, accuracy, or level of detail. Therefore, it receives a score of 0.\n\nAssistant 2, on the other hand, offered a specific action that the man in the image is performing, which is \"walking down the street while talking on his cell phone.\" This response is partially helpful as it addresses the question about what the man is doing. However, it does not mention the man's hair color, which was a significant detail provided in the context. This omission affects the relevance and accuracy of the response, as it does not fully acknowledge the provided context. Despite this, the assistant did provide some level of detail by including the action of talking on a cell phone, which adds to the scene's description. Therefore, Assistant 2 receives a score of 5 for at least contributing relevant information about the man's activity, even though it missed addressing the detail about the hair color."
+    },
+    {
+        "question": "What type of hats are the man and woman wearing?",
+        "correct_answer": "The man and woman are wearing umbrella hats, which are designed to provide protection from the rain.",
+        "response": "1. The person on the left appears to be wearing a baseball cap.\n2. The person on the right is wearing a bucket hat.\n\nBucket hats are characterized by their wide brim and rounded crown, which is designed to provide shade and protect the wearer's head from rain or sun. They are typically made from materials like cotton canvas or straw, and they have become a popular style of headwear for their practicality and casual appeal.\n\nBaseball caps, on the other hand, are a type of headwear with a flat crown and a curved bill that extends forward and slightly upward. They are typically made from materials like cotton twill or polyester, and they feature a six-panel design with a stiffened front panel that holds its shape. Baseball caps are often associated with sports teams due to their logo placement, but they are also widely worn as a fashion accessory.\n\nBoth types of hats are commonly worn for sun protection and as a fashion statement. ",
+        "score": false,
+        "gpt_response": "2 8\n\nAssistant 1 provided a detailed explanation of bucket hats and baseball caps, including their characteristics, materials, and common uses. However, this response is not relevant to the user's question about the type of hats being worn by the man and woman in the image, which was specified as \"umbrella hats.\" The detailed descriptions, while informative about bucket hats and baseball caps, do not address the actual query, leading to a low score for relevance and accuracy in the context of the question asked.\n\nAssistant 2 correctly identified the type of hats as \"umbrella hats,\" which directly answers the user's question. The response is concise and lacks the detailed descriptions found in Assistant 1's answer, but it is accurate and relevant to the question posed. Given the context that the question is about identifying a specific type of hat based on a visual observation, Assistant 2's response is more appropriate and helpful, despite its brevity.\n\nThe scores reflect the relevance and accuracy of each assistant's response to the specific question asked. Assistant 1's detailed but off-target answer receives a lower score due to its lack of relevance to the question. Assistant 2's correct but brief answer is rated higher because it directly addresses the user's query."
+    },
+    {
+        "question": "What are the main items of clothing visible in the image?",
+        "correct_answer": "The main items of clothing visible in the image are a white shirt, a red tie, and black shoes.",
+        "response": "1. A pair of black shoes\n2. A black suit jacket\n3. A white dress shirt\n4. A red necktie with a patterned design\n\nThese items are laid out on a bed, suggesting they are being prepared for an event or occasion. ",
+        "score": false,
+        "gpt_response": "6 8\n\nAssistant 1 provides a detailed description of the items, including a black suit jacket and a patterned design on the necktie, which were not mentioned in the user's context. This additional information suggests that Assistant 1 might be inferring or adding details not explicitly stated by the user, potentially leading to inaccuracies if the user did not intend for these items to be included in the description. The mention of the items being laid out on a bed for an event or occasion is speculative and not grounded in the user's provided context, which could mislead or confuse the user if their intention was merely to identify visible items without implying any specific setting or purpose.\n\nAssistant 2 directly addresses the user's question by listing the main items of clothing visible in the image as described in the user's context. This response is concise and directly relevant to the user's query, making no assumptions beyond the information provided by the user. However, Assistant 2's response lacks the detail and descriptive richness that might enhance the user's understanding or appreciation of the image. For instance, mentioning the style or specific features of the items (if visible in the image) could have made the response more informative.\n\nBoth assistants could improve by aligning their responses more closely with the user's context and avoiding assumptions or additions not supported by the user's input. Assistant 1's response, while detailed, risks adding potentially inaccurate or irrelevant information, thus receiving a lower score. Assistant 2's response is more aligned with the user's query but could benefit from additional detail to provide a fuller understanding of the items described, hence receiving a slightly higher score."
+    },
+    {
+        "question": "What stage is the house in regarding its construction?",
+        "correct_answer": "The house is in the beginning stages of its construction, with the wooden structure, studs, and staircase partially built but not yet completed.",
+        "response": "2021 Update: The image shows a house under construction. It appears to be in the framing stage, where the walls and structural elements are being built. The presence of exposed studs, the absence of drywall, and the unfinished state of the interior suggest that this is a building that is still in the process of being constructed.\n\nThe house features a combination of wooden studs and metal framing, which is common in modern construction. The floor appears to be laid out with plywood sheets, and there are insulation batts visible, indicating that the house is being insulated.\n\nThe presence of a large piece of lumber on the floor suggests that it may be a part of the construction process, possibly for framing or as a temporary support. The overall condition of the house indicates that it is still in the early stages of construction and is not yet ready for occupancy. ",
+        "score": true,
+        "gpt_response": "8 6\n\nAssistant 1 provided a detailed and comprehensive response, elaborating on the construction stage of the house by mentioning specific elements such as the framing stage, the presence of wooden studs and metal framing, plywood sheets for the floor, and insulation batts. This level of detail not only helps to accurately identify the construction stage but also offers insights into the materials and processes involved, which could be very informative for someone unfamiliar with construction. The mention of a large piece of lumber and its potential use adds to the depth of the analysis. The response is relevant and accurate based on the described image, making it highly helpful for someone trying to understand the construction stage of the house.\n\nAssistant 2, while accurate in identifying the house as being in the beginning stages of construction, provided a much less detailed response. It mentions the wooden structure, studs, and staircase but does not delve into the specifics of the construction process or the materials used, which makes the response less informative compared to Assistant 1. The brevity of the response might be sufficient for someone with a basic query, but it lacks the depth and detail that could provide a fuller understanding of the construction stage.\n\nBoth assistants provided relevant and accurate information based on the described image. However, Assistant 1's response was more detailed and informative, which justifies a higher score. Assistant 2's response, while still accurate and relevant, was less detailed, resulting in a slightly lower score. The scores reflect the level of detail and the amount of information provided by each assistant, which are important factors in assessing their performance in answering the user's question."
+    },
+    {
+        "question": "How many people are visible in the image?",
+        "correct_answer": "There are three people visible in the image - two women and one man.",
+        "response": "6 people are visible in the image. There are three individuals in the foreground who are smiling and holding wine glasses, and three others in the background who appear to be engaged in conversation. ",
+        "score": true,
+        "gpt_response": "5 3\n\nAssistant 1 provided a response indicating that six people are visible in the image, detailing that three individuals are in the foreground, smiling and holding wine glasses, and three others are in the background, appearing to be engaged in conversation. This response suggests a detailed observation of the scene, including both the foreground and background activities, which could be very helpful if the description matched the actual content of the image. However, without access to the image or its descriptive sentences and bounding box coordinates, it's impossible to verify the accuracy of Assistant 1's claim. Assuming the description provided by Assistant 1 accurately reflects the content of the image, the response would be highly relevant, accurate, and detailed, meriting a higher score. However, given the lack of verifiable information, a moderate score of 5 is assigned to reflect the potential for high-quality response tempered by the inability to confirm its accuracy.\n\nAssistant 2's response, indicating that three people are visible in the image (two women and one man), provides a straightforward answer but lacks the detail found in Assistant 1's response. This response could be seen as less helpful if indeed there are more individuals present in the image as suggested by Assistant 1. Without additional context or description of the scene beyond the number and gender of the visible individuals, Assistant 2's response is less informative. Given the lack of detail and the potential undercount of individuals present (based on Assistant 1's description), Assistant 2's response is rated lower, with a score of 3. This score reflects the response's potential relevance but acknowledges its limited detail and the possibility that it may not fully capture the scene's complexity.\n\nIn summary, the evaluation considers the potential helpfulness, relevance, accuracy, and level of detail in each response, with the understanding that without access to the image or its descriptive content, the accuracy of the assistants' responses cannot be directly verified. The scores reflect a balance between the perceived quality of the responses based on the information provided and the limitations imposed by the lack of direct access to the image content."
+    },
+    {
+        "question": "What is the color of the elephant in the image?",
+        "correct_answer": "The color of the elephant in the image is grey.",
+        "response": "",
+        "score": false,
+        "gpt_response": "0 5\n\nAssistant 1 did not provide any response to the user's question, which makes it impossible to evaluate its performance based on helpfulness, relevance, accuracy, or level of detail. Therefore, it receives a score of 0, as it failed to contribute any information or answer to the user's query.\n\nAssistant 2, on the other hand, provided a direct answer to the user's question by stating that the color of the elephant in the image is grey. This response is helpful and relevant to the user's question, assuming the typical coloration of elephants. However, without access to the image or the descriptive sentences and bounding box coordinates mentioned in the system's note, it's impossible to verify the accuracy of this statement. The response lacks detail, as it does not describe the context of the image, the elephant's surroundings, or any other characteristics that might have been visible. Given these considerations, Assistant 2 receives a score of 5 for providing a plausible answer but without any supporting details or acknowledgment of the limitations in its ability to visually verify the image's content."
+    },
+    {
+        "question": "What kind of objects are included in the art installation?",
+        "correct_answer": "The art installation includes a display of large artworks, such as an oversized bench, painted feline heads, and potentially other artistic sculptures or images.",
+        "response": "1. A large, colorful sculpture of a tiger's head.\n2. A wooden structure that resembles a bench or a piece of furniture with a weathered appearance.\n3. A large, blue sculpture with a face that appears to be a stylized animal or character.\n4. A small black car with a colorful roof rack.\n5. A person sitting on the edge of the sidewalk, facing away from the camera.\n6. A person standing on the sidewalk, facing towards the camera.\n7. A black car with a white license plate.\n8. A red car with a white license plate.\n9. A person standing on the street, facing away from the camera.\n10. A person standing on the street, facing towards the camera.\n\nThe setting appears to be an outdoor public space, possibly a pedestrian area or a street corner, with a mix of art installations and everyday life. The art installation seems to be a creative blend of organic and man-made elements, possibly commenting on themes of nature, urban life, and the relationship between humans and their environment. The presence of people and vehicles adds a dynamic element to the scene, suggesting a lively atmosphere despite the stillness of the artwork. ",
+        "score": true,
+        "gpt_response": "7 6\n\nAssistant 1 provided a detailed and imaginative description of the scene, including both the art installations and the surrounding environment, which includes people and vehicles. This response offers a vivid picture of the setting, suggesting a lively atmosphere and a blend of organic and man-made elements. However, the inclusion of people and vehicles as part of the art installation description could be misleading, as these are not typically considered elements of an art installation. This detracts slightly from the accuracy of the response in terms of directly answering the user's question about the objects included in the art installation.\n\nAssistant 2 offered a more concise response, focusing on the art installation itself without the additional context of the surrounding environment. This response is more directly aligned with the user's question, identifying key elements of the art installation. However, it lacks the vivid detail and imaginative description provided by Assistant 1, offering less insight into the nature and atmosphere of the installation. The response could benefit from more specific details about the artworks mentioned to enhance its helpfulness and relevance.\n\nBoth assistants could improve by focusing more precisely on the objects that are unambiguously part of the art installation, excluding elements like people and vehicles unless they are explicitly described as part of the installation. Assistant 1's response, while more detailed and engaging, could be more accurate by focusing on the art objects. Assistant 2's response, though more focused, could provide more detail to enhance its usefulness."
+    }
+]

slice_end/Evaluation.log

@@ -0,0 +1,23 @@
+2025-04-01 00:05:26,600 - Evaluation - INFO - +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+2025-04-01 00:05:26,603 - Evaluation - INFO - task: conv
+2025-04-01 00:05:26,603 - Evaluation - INFO - model_id: 0,
+2025-04-01 00:05:26,604 - Evaluation - INFO - average: 62.6,
+2025-04-01 00:05:26,605 - Evaluation - INFO - question: 30,
+2025-04-01 00:05:26,605 - Evaluation - INFO - +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+2025-04-01 00:06:17,335 - Evaluation - INFO - +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+2025-04-01 00:06:17,336 - Evaluation - INFO - task: detail
+2025-04-01 00:06:17,336 - Evaluation - INFO - model_id: 0,
+2025-04-01 00:06:17,337 - Evaluation - INFO - average: 97.3,
+2025-04-01 00:06:17,338 - Evaluation - INFO - question: 30,
+2025-04-01 00:06:17,338 - Evaluation - INFO - +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+2025-04-01 00:06:41,500 - Evaluation - INFO - +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+2025-04-01 00:06:41,501 - Evaluation - INFO - task: complex
+2025-04-01 00:06:41,502 - Evaluation - INFO - model_id: 0,
+2025-04-01 00:06:41,502 - Evaluation - INFO - average: 110.7,
+2025-04-01 00:06:41,503 - Evaluation - INFO - question: 30,
+2025-04-01 00:06:41,503 - Evaluation - INFO - +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+2025-04-01 00:06:41,505 - Evaluation - INFO - +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+2025-04-01 00:06:41,505 - Evaluation - INFO - model_id: 0,
+2025-04-01 00:06:41,506 - Evaluation - INFO - total_average: 90.2,
+2025-04-01 00:06:41,507 - Evaluation - INFO - total_question: 90,
+2025-04-01 00:06:41,507 - Evaluation - INFO - +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

slice_end/added_tokens.json

@@ -0,0 +1,4 @@
+{
+  "<image>": 32000,
+  "<pad>": 32001
+}

slice_end/chat_template.json

@@ -0,0 +1,3 @@
+{
+  "chat_template": "{% for message in messages %}{% if message['role'] != 'system' %}{{ message['role'].upper() + ': '}}{% endif %}{# Render all images first #}{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}{{ '<image>\n' }}{% endfor %}{# Render all text next #}{% if message['role'] != 'assistant' %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{{ content['text'] + ' '}}{% endfor %}{% else %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{% generation %}{{ content['text'] + ' '}}{% endgeneration %}{% endfor %}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'ASSISTANT:' }}{% endif %}"
+}

slice_end/config.json

@@ -0,0 +1,85 @@
+{
+  "_attn_implementation_autoset": true,
+  "architectures": [
+    "LlavaNextForConditionalGeneration"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "ignore_index": -100,
+  "image_grid_pinpoints": [
+    [
+      336,
+      672
+    ],
+    [
+      672,
+      336
+    ],
+    [
+      672,
+      672
+    ],
+    [
+      1008,
+      336
+    ],
+    [
+      336,
+      1008
+    ]
+  ],
+  "image_seq_length": 576,
+  "image_token_index": 32000,
+  "model_type": "llava_next",
+  "multimodal_projector_bias": true,
+  "pad_token_id": 32001,
+  "projector_hidden_act": "gelu",
+  "text_config": {
+    "_name_or_path": "mistralai/Mistral-7B-Instruct-v0.2",
+    "architectures": [
+      "MistralForCausalLM"
+    ],
+    "attention_dropout": 0.0,
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 4096,
+    "initializer_range": 0.02,
+    "intermediate_size": 14336,
+    "max_position_embeddings": 32768,
+    "model_type": "mistral",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 32,
+    "num_key_value_heads": 8,
+    "rms_norm_eps": 1e-05,
+    "rope_theta": 1000000.0,
+    "sliding_window": null,
+    "torch_dtype": "bfloat16",
+    "use_cache": true,
+    "vocab_size": 32064
+  },
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.50.0",
+  "use_image_newline_parameter": true,
+  "vision_config": {
+    "attention_dropout": 0.0,
+    "hidden_act": "quick_gelu",
+    "hidden_size": 1024,
+    "image_size": 336,
+    "initializer_factor": 1.0,
+    "initializer_range": 0.02,
+    "intermediate_size": 4096,
+    "layer_norm_eps": 1e-05,
+    "model_type": "clip_vision_model",
+    "num_attention_heads": 16,
+    "num_channels": 3,
+    "num_hidden_layers": 24,
+    "patch_size": 14,
+    "projection_dim": 768,
+    "torch_dtype": "bfloat16",
+    "vocab_size": 32000
+  },
+  "vision_feature_layer": -2,
+  "vision_feature_select_strategy": "default",
+  "vocab_size": 32064
+}

slice_end/preprocessor_config.json

@@ -0,0 +1,52 @@
+{
+  "aspect_ratio_setting": "anyres",
+  "crop_size": {
+    "height": 336,
+    "width": 336
+  },
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_pad": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_grid_pinpoints": [
+    [
+      336,
+      672
+    ],
+    [
+      672,
+      336
+    ],
+    [
+      672,
+      672
+    ],
+    [
+      1008,
+      336
+    ],
+    [
+      336,
+      1008
+    ]
+  ],
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_processor_type": "LlavaNextImageProcessor",
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "processor_class": "LlavaNextProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "shortest_edge": 336
+  }
+}

slice_end/processor_config.json

@@ -0,0 +1,7 @@
+{
+  "image_token": "<image>",
+  "num_additional_image_tokens": 1,
+  "patch_size": 14,
+  "processor_class": "LlavaNextProcessor",
+  "vision_feature_select_strategy": "default"
+}

slice_end/pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2943a2f4dfd6a161bafdc470a8f46f2a0d48f957837d839d49640c30743aa903
+size 15133733934

slice_end/raw_outputs__aifs4su_yaodong_hantao_align-anything_outputs_llava_1.6_mistral_7b_val_top1-10_slice_end.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c735dd9a0aa7797ce78fddd7c2013a94be47b7df5e7ed6027b218c6e3e3b4281
+size 15661443

slice_end/special_tokens_map.json

@@ -0,0 +1,31 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "image_token": "<image>",
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

slice_end/tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dadfd56d766715c61d2ef780a525ab43b8e6da4de6865bda3d95fdef5e134055
+size 493443

slice_end/tokenizer_config.json

@@ -0,0 +1,69 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "add_prefix_space": null,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32000": {
+      "content": "<image>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32001": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [],
+  "bos_token": "<s>",
+  "chat_template": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "extra_special_tokens": {
+    "image_token": "<image>"
+  },
+  "image_token": "<image>",
+  "legacy": true,
+  "max_length": null,
+  "model_max_length": 4096,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "left",
+  "processor_class": "LlavaNextProcessor",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}

wandb/debug-internal.log

@@ -0,0 +1,16 @@
+{"time":"2025-03-31T17:39:29.357857467+08:00","level":"INFO","msg":"stream: starting","core version":"0.19.8","symlink path":"../outputs/llava_1.6_mistral_7b_val/top1-10/wandb/run-20250331_173929-oflhglry/logs/debug-core.log"}
+{"time":"2025-03-31T17:39:29.573098876+08:00","level":"INFO","msg":"created new stream","id":"oflhglry"}
+{"time":"2025-03-31T17:39:29.573167483+08:00","level":"INFO","msg":"stream: started","id":"oflhglry"}
+{"time":"2025-03-31T17:39:29.573190711+08:00","level":"INFO","msg":"handler: started","stream_id":"oflhglry"}
+{"time":"2025-03-31T17:39:29.573196411+08:00","level":"INFO","msg":"writer: Do: started","stream_id":"oflhglry"}
+{"time":"2025-03-31T17:39:29.573195051+08:00","level":"INFO","msg":"sender: started","stream_id":"oflhglry"}
+{"time":"2025-03-31T17:39:29.880478815+08:00","level":"INFO","msg":"Starting system monitor"}
+{"time":"2025-03-31T17:44:01.38994239+08:00","level":"INFO","msg":"Stopping system monitor"}
+{"time":"2025-03-31T17:44:01.39093428+08:00","level":"INFO","msg":"Stopped system monitor"}
+{"time":"2025-03-31T17:44:02.852694686+08:00","level":"INFO","msg":"fileTransfer: Close: file transfer manager closed"}
+{"time":"2025-03-31T17:44:02.902712341+08:00","level":"INFO","msg":"stream: closing","id":"oflhglry"}
+{"time":"2025-03-31T17:44:02.902739889+08:00","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
+{"time":"2025-03-31T17:44:03.099767177+08:00","level":"INFO","msg":"handler: closed","stream_id":"oflhglry"}
+{"time":"2025-03-31T17:44:03.099816798+08:00","level":"INFO","msg":"writer: Close: closed","stream_id":"oflhglry"}
+{"time":"2025-03-31T17:44:03.099920876+08:00","level":"INFO","msg":"sender: closed","stream_id":"oflhglry"}
+{"time":"2025-03-31T17:44:03.100069001+08:00","level":"INFO","msg":"stream: closed","id":"oflhglry"}

wandb/debug.log

@@ -0,0 +1,47 @@
+2025-03-31 17:39:29,349 INFO    MainThread:1814399 [wandb_setup.py:_flush():67] Current SDK version is 0.19.8
+2025-03-31 17:39:29,349 INFO    MainThread:1814399 [wandb_setup.py:_flush():67] Configure stats pid to 1814399
+2025-03-31 17:39:29,349 INFO    MainThread:1814399 [wandb_setup.py:_flush():67] Loading settings from /home/yangyaodong/.config/wandb/settings
+2025-03-31 17:39:29,349 INFO    MainThread:1814399 [wandb_setup.py:_flush():67] Loading settings from /aifs4su/yaodong/hantao/align-anything/scripts/wandb/settings
+2025-03-31 17:39:29,349 INFO    MainThread:1814399 [wandb_setup.py:_flush():67] Loading settings from environment variables
+2025-03-31 17:39:29,349 INFO    MainThread:1814399 [wandb_init.py:setup_run_log_directory():647] Logging user logs to ../outputs/llava_1.6_mistral_7b_val/top1-10/wandb/run-20250331_173929-oflhglry/logs/debug.log
+2025-03-31 17:39:29,349 INFO    MainThread:1814399 [wandb_init.py:setup_run_log_directory():648] Logging internal logs to ../outputs/llava_1.6_mistral_7b_val/top1-10/wandb/run-20250331_173929-oflhglry/logs/debug-internal.log
+2025-03-31 17:39:29,349 INFO    MainThread:1814399 [wandb_init.py:init():761] calling init triggers
+2025-03-31 17:39:29,350 INFO    MainThread:1814399 [wandb_init.py:init():766] wandb.init called with sweep_config: {}
+config: {'train_cfgs': {'save_checkpoint': False, 'load_checkpoint': False, 'ds_cfgs': 'ds_z3_config.json', 'epochs': 3, 'seed': 42, 'per_device_train_batch_size': 1, 'per_device_eval_batch_size': 1, 'gradient_accumulation_steps': 1, 'gradient_checkpointing': True, 'learning_rate': 1e-06, 'lr_scheduler_type': 'cosine', 'lr_warmup_ratio': 0.03, 'weight_decay': 0.0, 'adam_betas': [0.9, 0.95], 'bf16': True, 'fp16': False, 'eval_strategy': 'epoch', 'eval_interval': 10, 'regularization': 0.001, 'scale_coeff': 0.1, 'freeze_mm_proj': False, 'freeze_vision_tower': True, 'freeze_language_model': False}, 'data_cfgs': {'train_datasets': '/aifs4su/yaodong/hantao/datasets/AA_preference_cosi/val/merged/top1-10', 'train_template': 'AA_TI2T_LLAVA', 'train_size': {}, 'train_split': 'train', 'train_name': 'text-image-to-text', 'train_data_files': {}, 'train_optional_args': [], 'eval_datasets': {}, 'eval_template': {}, 'eval_size': {}, 'eval_split': {}, 'eval_subset': {}, 'eval_data_files': {}, 'eval_optional_args': []}, 'logger_cfgs': {'log_type': 'wandb', 'log_project': 'align-anything', 'log_run_name': 'dpo', 'output_dir': '../outputs/llava_1.6_mistral_7b_val/top1-10', 'cache_dir': {}, 'save_total_limit': 3}, 'model_cfgs': {'model_name_or_path': '/aifs4su/yaodong/hantao/models/llava-v1.6-mistral-7b-hf', 'trust_remote_code': True, 'model_max_length': 4096}, 'special_tokens': {}, '_wandb': {}}
+2025-03-31 17:39:29,350 INFO    MainThread:1814399 [wandb_init.py:init():784] starting backend
+2025-03-31 17:39:29,350 INFO    MainThread:1814399 [wandb_init.py:init():788] sending inform_init request
+2025-03-31 17:39:29,355 INFO    MainThread:1814399 [backend.py:_multiprocessing_setup():101] multiprocessing start_methods=fork,spawn,forkserver, using: spawn
+2025-03-31 17:39:29,355 INFO    MainThread:1814399 [wandb_init.py:init():798] backend started and connected
+2025-03-31 17:39:29,357 INFO    MainThread:1814399 [wandb_init.py:init():891] updated telemetry
+2025-03-31 17:39:29,371 INFO    MainThread:1814399 [wandb_init.py:init():915] communicating run to backend with 90.0 second timeout
+2025-03-31 17:39:29,874 INFO    MainThread:1814399 [wandb_init.py:init():990] starting run threads in backend
+2025-03-31 17:39:30,115 INFO    MainThread:1814399 [wandb_run.py:_console_start():2375] atexit reg
+2025-03-31 17:39:30,115 INFO    MainThread:1814399 [wandb_run.py:_redirect():2227] redirect: wrap_raw
+2025-03-31 17:39:30,115 INFO    MainThread:1814399 [wandb_run.py:_redirect():2292] Wrapping output streams.
+2025-03-31 17:39:30,116 INFO    MainThread:1814399 [wandb_run.py:_redirect():2315] Redirects installed.
+2025-03-31 17:39:30,118 INFO    MainThread:1814399 [wandb_init.py:init():1032] run started, returning control to user process
+2025-03-31 17:44:01,349 INFO    MainThread:1814399 [wandb_run.py:_finish():2112] finishing run htlou/align-anything/oflhglry
+2025-03-31 17:44:01,350 INFO    MainThread:1814399 [wandb_run.py:_atexit_cleanup():2340] got exitcode: 0
+2025-03-31 17:44:01,350 INFO    MainThread:1814399 [wandb_run.py:_restore():2322] restore
+2025-03-31 17:44:01,351 INFO    MainThread:1814399 [wandb_run.py:_restore():2328] restore done
+2025-03-31 17:44:02,351 INFO    MainThread:1814399 [wandb_run.py:_restore():2322] restore
+2025-03-31 17:44:02,352 INFO    MainThread:1814399 [wandb_run.py:_restore():2328] restore done
+2025-03-31 17:44:02,352 ERROR   MainThread:1814399 [wandb_run.py:_atexit_cleanup():2361] Problem finishing run
+Traceback (most recent call last):
+  File "/aifs4su/yaodong/miniconda3/envs/hantao_llama/lib/python3.11/site-packages/wandb/sdk/wandb_run.py", line 2352, in _atexit_cleanup
+    self._on_finish()
+  File "/aifs4su/yaodong/miniconda3/envs/hantao_llama/lib/python3.11/site-packages/wandb/sdk/wandb_run.py", line 2609, in _on_finish
+    wait_with_progress(
+  File "/aifs4su/yaodong/miniconda3/envs/hantao_llama/lib/python3.11/site-packages/wandb/sdk/mailbox/wait_with_progress.py", line 24, in wait_with_progress
+    return wait_all_with_progress(
+           ^^^^^^^^^^^^^^^^^^^^^^^
+  File "/aifs4su/yaodong/miniconda3/envs/hantao_llama/lib/python3.11/site-packages/wandb/sdk/mailbox/wait_with_progress.py", line 87, in wait_all_with_progress
+    return asyncio_compat.run(progress_loop_with_timeout)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/aifs4su/yaodong/miniconda3/envs/hantao_llama/lib/python3.11/site-packages/wandb/sdk/lib/asyncio_compat.py", line 27, in run
+    future = executor.submit(runner.run, fn)
+             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/aifs4su/yaodong/miniconda3/envs/hantao_llama/lib/python3.11/concurrent/futures/thread.py", line 169, in submit
+    raise RuntimeError('cannot schedule new futures after '
+RuntimeError: cannot schedule new futures after interpreter shutdown
+2025-03-31 17:44:02,902 INFO    MsgRouterThr:1814399 [mailbox.py:close():129] Closing mailbox, abandoning 2 handles.

wandb/run-20250331_173510-7rsczbti/files/output.log

@@ -0,0 +1,16 @@
+***** Running training *****
+Resuming from checkpoint 1/3 epoch :   0%|                                                                                                                                 | 0/72 [00:00<?, ?it/s]/aifs4su/yaodong/miniconda3/envs/hantao_llama/lib/python3.11/site-packages/torch/utils/checkpoint.py:87: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
+  warnings.warn(
+`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.
+Training 1/3 epoch (loss 0.6172):   7%|████████▌                                                                                                                   | 5/72 [00:19<03:24,  3.05s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (4205 > 4096). Running this sequence through the model will result in indexing errors
+Training 1/3 epoch (loss 0.8398):  12%|███████████████▌                                                                                                            | 9/72 [00:58<07:20,  6.99s/it]
+Saving checkpoint at step 6 ...
+Saving model to "../outputs/llava_1.6_mistral_7b_val/top1-10/slice_6" ...
+Saving 16-bit model...
+[2025-03-31 17:35:42,883] [INFO] [logging.py:107:log_dist] [Rank 0] [Torch] Checkpoint global_step6 is about to be saved!
+[2025-03-31 17:35:42,884] [INFO] [engine.py:3831:save_16bit_model] Saving model weights to ../outputs/llava_1.6_mistral_7b_val/top1-10/slice_6/pytorch_model.bin, tag: global_step6
+[2025-03-31 17:35:42,884] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving ../outputs/llava_1.6_mistral_7b_val/top1-10/slice_6/pytorch_model.bin...
+[2025-03-31 17:36:03,605] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved ../outputs/llava_1.6_mistral_7b_val/top1-10/slice_6/pytorch_model.bin.
+[2025-03-31 17:36:03,608] [INFO] [torch_checkpoint_engine.py:33:commit] [Torch] Checkpoint global_step6 is ready now!
+Model saved!
+Checkpoint saved.

wandb/run-20250331_173510-7rsczbti/files/requirements.txt

@@ -0,0 +1,258 @@
+maskrcnn_benchmark==0.0.0
+webdataset==0.2.111
+websockets==15.0.1
+typer==0.15.2
+blobfile==3.0.0
+pooch==1.8.2
+filelock==3.18.0
+referencing==0.36.2
+matplotlib==3.10.1
+cachetools==5.5.2
+python-dateutil==2.9.0.post0
+gmpy2==2.2.1
+uvloop==0.21.0
+nvidia-cusparselt-cu12==0.6.2
+clip==0.2.0
+httpcore==1.0.7
+charset-normalizer==3.3.2
+torchlibrosa==0.1.0
+contourpy==1.3.1
+multiprocess==0.70.16
+nest-asyncio==1.6.0
+Werkzeug==3.1.3
+aiofiles==23.2.1
+six==1.17.0
+torch==2.6.0
+sse-starlette==2.2.1
+typing_extensions==4.12.2
+xgrammar==0.1.16
+psutil==7.0.0
+kiwisolver==1.4.8
+moviepy==2.1.2
+frozenlist==1.5.0
+jiter==0.9.0
+einops==0.8.1
+flash_attn==2.7.4.post1
+PySocks==1.7.1
+regex==2024.11.6
+markdown-it-py==3.0.0
+ruff==0.11.2
+docker-pycreds==0.4.0
+nvidia-nvtx-cu12==12.4.127
+pyparsing==3.2.3
+resampy==0.4.3
+tokenizers==0.21.0
+frechet-audio-distance==0.1.2
+aiohappyeyeballs==2.6.1
+llamafactory==0.9.3.dev0
+msgspec==0.19.0
+httpx==0.28.1
+encodec==0.1.1
+ffmpy==0.5.0
+jsonschema==4.23.0
+imageio-ffmpeg==0.6.0
+mkl_random==1.2.8
+fairscale==0.4.13
+soxr==0.5.0.post1
+lark==1.2.2
+gradio==5.21.0
+absl-py==2.2.1
+dnspython==2.7.0
+networkx==3.4.2
+h5py==3.13.0
+hjson==3.1.0
+tensorboard==2.19.0
+aiosignal==1.3.2
+pip==25.0
+nvidia-cublas-cu12==12.4.5.8
+llguidance==0.7.11
+zipp==3.21.0
+ftfy==6.3.1
+peft==0.15.0
+attrs==25.3.0
+trl==0.9.6
+requests==2.32.3
+progressbar==2.5
+sniffio==1.3.1
+pycountry==24.6.1
+lxml==5.3.1
+starlette==0.46.1
+pytest==7.2.0
+Markdown==3.7
+mdurl==0.1.2
+pyzmq==26.3.0
+safetensors==0.5.3
+opencv-python==4.6.0.66
+prometheus-fastapi-instrumentator==7.1.0
+shellingham==1.5.4
+torchvision==0.21.0
+pluggy==1.5.0
+timm==1.0.15
+multidict==6.2.0
+semantic-version==2.10.0
+airportsdata==20250224
+numba==0.60.0
+MarkupSafe==2.1.5
+pydantic_core==2.33.0
+imageio==2.37.0
+nvidia-nccl-cu12==2.21.5
+dill==0.3.8
+msgpack==1.1.0
+sentry-sdk==2.24.1
+rpds-py==0.24.0
+grpcio==1.71.0
+fastrlock==0.8.3
+python-json-logger==3.3.0
+cffi==1.17.1
+gradio_client==1.7.2
+PyYAML==6.0.2
+tensorboard-data-server==0.7.2
+termcolor==2.5.0
+torchaudio==2.6.0
+triton==3.2.0
+fastapi==0.115.12
+clint==0.5.1
+lazy_loader==0.4
+depyf==0.18.0
+mkl_fft==1.3.11
+annotated-types==0.7.0
+scikit-learn==1.6.1
+wget==3.2
+setuptools==75.8.0
+args==0.1.0
+certifi==2025.1.31
+click==8.1.8
+python-dotenv==1.1.0
+laion_clap==1.1.5
+Pygments==2.19.1
+tomlkit==0.13.2
+idna==3.7
+propcache==0.3.1
+platformdirs==4.3.7
+align-anything==0.0.1.dev0
+ray==2.44.1
+cloudpickle==3.1.1
+deepspeed==0.16.5
+smmap==5.0.2
+distro==1.9.0
+fonttools==4.56.0
+typing-inspection==0.4.0
+braceexpand==0.1.7
+decorator==5.2.1
+diskcache==5.6.3
+yt-dlp==2025.3.27
+shtab==1.7.1
+gguf==0.10.0
+interegular==0.3.3
+compressed-tensors==0.9.2
+pandas==2.2.3
+huggingface-hub==0.29.3
+pyarrow==19.0.1
+lm-format-enforcer==0.10.11
+GitPython==3.1.44
+xxhash==3.5.0
+packaging==24.2
+setproctitle==1.3.5
+llvmlite==0.43.0
+tiktoken==0.9.0
+mpmath==1.3.0
+email_validator==2.2.0
+nvidia-ml-py==12.570.86
+pydantic==2.11.0
+xformers==0.0.29.post2
+httptools==0.6.4
+librosa==0.11.0
+pytorch-fid==0.3.0
+hpsv2==1.2.0
+nvidia-cudnn-cu12==9.1.0.70
+iniconfig==2.1.0
+sympy==1.13.1
+safehttpx==0.1.6
+jsonschema-specifications==2024.10.1
+Jinja2==3.1.6
+tyro==0.8.14
+h11==0.14.0
+aiohttp==3.11.14
+diffusers==0.32.2
+tqdm==4.67.1
+blake3==1.0.4
+vllm==0.8.2
+scipy==1.10.1
+audioread==3.0.1
+proglog==0.1.10
+fire==0.7.0
+sentencepiece==0.2.0
+pytz==2025.2
+nvidia-nvjitlink-cu12==12.4.127
+nvidia-curand-cu12==10.3.5.147
+numpy==1.26.4
+tzdata==2025.2
+python-multipart==0.0.20
+urllib3==2.3.0
+pycryptodomex==3.22.0
+yarl==1.18.3
+outlines==0.1.11
+nvidia-cusolver-cu12==11.6.1.9
+pydub==0.25.1
+mistral_common==1.5.4
+pycparser==2.22
+pytest-split==0.8.0
+datasets==3.4.1
+soundfile==0.13.1
+transformers==4.50.0
+image-reward==1.5
+wcwidth==0.2.13
+nvidia-cuda-nvrtc-cu12==12.4.127
+groovy==0.1.2
+nvidia-cuda-runtime-cu12==12.4.127
+astor==0.8.1
+anyio==4.9.0
+wandb==0.19.8
+joblib==1.4.2
+fsspec==2024.12.0
+accelerate==1.5.2
+py-cpuinfo==9.0.0
+docstring_parser==0.16
+partial-json-parser==0.2.1.1.post5
+nvidia-cusparse-cu12==12.3.1.170
+protobuf==3.20.3
+outlines_core==0.1.26
+nvidia-cufft-cu12==11.2.1.3
+cycler==0.12.1
+uvicorn==0.34.0
+orjson==3.10.16
+av==14.2.0
+Brotli==1.0.9
+cupy-cuda12x==13.4.1
+openai==1.69.0
+rich==13.9.4
+importlib_metadata==8.6.1
+ninja==1.11.1.4
+wheel==0.45.1
+pillow==10.4.0
+prometheus_client==0.21.1
+threadpoolctl==3.6.0
+gitdb==4.0.12
+watchfiles==1.0.4
+nvidia-cuda-cupti-cu12==12.4.127
+opencv-python-headless==4.11.0.86
+mkl-service==2.4.0
+rich-toolkit==0.14.0
+fastapi-cli==0.0.7
+llamafactory==0.9.3.dev0
+typing_extensions==4.12.2
+tomli==2.0.1
+zipp==3.19.2
+wheel==0.43.0
+jaraco.text==3.12.1
+packaging==24.2
+autocommand==2.2.2
+jaraco.functools==4.0.1
+jaraco.collections==5.1.0
+platformdirs==4.2.2
+more-itertools==10.3.0
+inflect==7.3.1
+jaraco.context==5.3.0
+typeguard==4.3.0
+backports.tarfile==1.2.0
+importlib_metadata==8.0.0

wandb/run-20250331_173510-7rsczbti/files/wandb-metadata.json

@@ -0,0 +1,106 @@
+{
+  "os": "Linux-5.15.0-1040-nvidia-x86_64-with-glibc2.35",
+  "python": "CPython 3.11.11",
+  "startedAt": "2025-03-31T09:35:10.840464Z",
+  "args": [
+    "--local_rank=0",
+    "--model_name_or_path",
+    "/aifs4su/yaodong/hantao/models/llava-v1.6-mistral-7b-hf",
+    "--train_datasets",
+    "/aifs4su/yaodong/hantao/datasets/AA_preference_cosi/val/merged/top1-10",
+    "--train_template",
+    "AA_TI2T_LLAVA",
+    "--train_split",
+    "train",
+    "--train_name",
+    "text-image-to-text",
+    "--output_dir",
+    "../outputs/llava_1.6_mistral_7b_val/top1-10",
+    "--save_total_limit",
+    "12",
+    "--train_batch_size",
+    "8",
+    "--epochs",
+    "3"
+  ],
+  "program": "-m align_anything.trainers.text_image_to_text.dpo",
+  "git": {
+    "remote": "git@github.com-hantao:PKU-Alignment/align-anything.git",
+    "commit": "106588f9802757a3283c1aff1f33ea9afd737f31"
+  },
+  "email": "2200017789@stu.pku.edu.cn",
+  "root": "../outputs/llava_1.6_mistral_7b_val/top1-10",
+  "host": "dgx-092",
+  "executable": "/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/python",
+  "cpu_count": 112,
+  "cpu_count_logical": 224,
+  "gpu": "NVIDIA H800",
+  "gpu_count": 8,
+  "disk": {
+    "/": {
+      "total": "1888556142592",
+      "used": "149000343552"
+    }
+  },
+  "memory": {
+    "total": "2164195454976"
+  },
+  "cpu": {
+    "count": 112,
+    "countLogical": 224
+  },
+  "gpu_nvidia": [
+    {
+      "name": "NVIDIA H800",
+      "memoryTotal": "85520809984",
+      "cudaCores": 16896,
+      "architecture": "Hopper"
+    },
+    {
+      "name": "NVIDIA H800",
+      "memoryTotal": "85520809984",
+      "cudaCores": 16896,
+      "architecture": "Hopper"
+    },
+    {
+      "name": "NVIDIA H800",
+      "memoryTotal": "85520809984",
+      "cudaCores": 16896,
+      "architecture": "Hopper"
+    },
+    {
+      "name": "NVIDIA H800",
+      "memoryTotal": "85520809984",
+      "cudaCores": 16896,
+      "architecture": "Hopper"
+    },
+    {
+      "name": "NVIDIA H800",
+      "memoryTotal": "85520809984",
+      "cudaCores": 16896,
+      "architecture": "Hopper"
+    },
+    {
+      "name": "NVIDIA H800",
+      "memoryTotal": "85520809984",
+      "cudaCores": 16896,
+      "architecture": "Hopper"
+    },
+    {
+      "name": "NVIDIA H800",
+      "memoryTotal": "85520809984",
+      "cudaCores": 16896,
+      "architecture": "Hopper"
+    },
+    {
+      "name": "NVIDIA H800",
+      "memoryTotal": "85520809984",
+      "cudaCores": 16896,
+      "architecture": "Hopper"
+    }
+  ],
+  "slurm": {
+    "conf": "/cm/shared/apps/slurm/var/etc/slurm/slurm.conf"
+  },
+  "cudaVersion": "12.2"
+}

wandb/run-20250331_173510-7rsczbti/logs/debug-core.log

@@ -0,0 +1,7 @@
+{"time":"2025-03-31T17:35:10.259294371+08:00","level":"INFO","msg":"main: starting server","port-filename":"/tmp/tmpy9i5crdw/port-1800486.txt","pid":1800486,"log-level":0,"disable-analytics":false,"shutdown-on-parent-exit":false}
+{"time":"2025-03-31T17:35:10.260539464+08:00","level":"INFO","msg":"Will exit if parent process dies.","ppid":1800486}
+{"time":"2025-03-31T17:35:10.260534373+08:00","level":"INFO","msg":"server is running","addr":{"IP":"127.0.0.1","Port":34021,"Zone":""}}
+{"time":"2025-03-31T17:35:10.409005578+08:00","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"127.0.0.1:42092"}
+{"time":"2025-03-31T17:35:10.842218444+08:00","level":"INFO","msg":"handleInformInit: received","streamId":"7rsczbti","id":"127.0.0.1:42092"}
+{"time":"2025-03-31T17:35:11.059457668+08:00","level":"INFO","msg":"handleInformInit: stream started","streamId":"7rsczbti","id":"127.0.0.1:42092"}
+{"time":"2025-03-31T17:36:11.289689563+08:00","level":"INFO","msg":"received shutdown signal","signal":15}

wandb/run-20250331_173510-7rsczbti/logs/debug-internal.log

@@ -0,0 +1,7 @@
+{"time":"2025-03-31T17:35:10.84379093+08:00","level":"INFO","msg":"stream: starting","core version":"0.19.8","symlink path":"../outputs/llava_1.6_mistral_7b_val/top1-10/wandb/run-20250331_173510-7rsczbti/logs/debug-core.log"}
+{"time":"2025-03-31T17:35:11.059368118+08:00","level":"INFO","msg":"created new stream","id":"7rsczbti"}
+{"time":"2025-03-31T17:35:11.05945062+08:00","level":"INFO","msg":"stream: started","id":"7rsczbti"}
+{"time":"2025-03-31T17:35:11.059508775+08:00","level":"INFO","msg":"writer: Do: started","stream_id":"7rsczbti"}
+{"time":"2025-03-31T17:35:11.059511735+08:00","level":"INFO","msg":"handler: started","stream_id":"7rsczbti"}
+{"time":"2025-03-31T17:35:11.059514206+08:00","level":"INFO","msg":"sender: started","stream_id":"7rsczbti"}
+{"time":"2025-03-31T17:35:11.777198217+08:00","level":"INFO","msg":"Starting system monitor"}

wandb/run-20250331_173510-7rsczbti/logs/debug.log

@@ -0,0 +1,22 @@
+2025-03-31 17:35:10,830 INFO    MainThread:1800486 [wandb_setup.py:_flush():67] Current SDK version is 0.19.8
+2025-03-31 17:35:10,830 INFO    MainThread:1800486 [wandb_setup.py:_flush():67] Configure stats pid to 1800486
+2025-03-31 17:35:10,830 INFO    MainThread:1800486 [wandb_setup.py:_flush():67] Loading settings from /home/yangyaodong/.config/wandb/settings
+2025-03-31 17:35:10,830 INFO    MainThread:1800486 [wandb_setup.py:_flush():67] Loading settings from /aifs4su/yaodong/hantao/align-anything/scripts/wandb/settings
+2025-03-31 17:35:10,830 INFO    MainThread:1800486 [wandb_setup.py:_flush():67] Loading settings from environment variables
+2025-03-31 17:35:10,830 INFO    MainThread:1800486 [wandb_init.py:setup_run_log_directory():647] Logging user logs to ../outputs/llava_1.6_mistral_7b_val/top1-10/wandb/run-20250331_173510-7rsczbti/logs/debug.log
+2025-03-31 17:35:10,830 INFO    MainThread:1800486 [wandb_init.py:setup_run_log_directory():648] Logging internal logs to ../outputs/llava_1.6_mistral_7b_val/top1-10/wandb/run-20250331_173510-7rsczbti/logs/debug-internal.log
+2025-03-31 17:35:10,830 INFO    MainThread:1800486 [wandb_init.py:init():761] calling init triggers
+2025-03-31 17:35:10,831 INFO    MainThread:1800486 [wandb_init.py:init():766] wandb.init called with sweep_config: {}
+config: {'train_cfgs': {'save_checkpoint': False, 'load_checkpoint': False, 'ds_cfgs': 'ds_z3_config.json', 'epochs': 3, 'seed': 42, 'per_device_train_batch_size': 1, 'per_device_eval_batch_size': 1, 'gradient_accumulation_steps': 1, 'gradient_checkpointing': True, 'learning_rate': 1e-06, 'lr_scheduler_type': 'cosine', 'lr_warmup_ratio': 0.03, 'weight_decay': 0.0, 'adam_betas': [0.9, 0.95], 'bf16': True, 'fp16': False, 'eval_strategy': 'epoch', 'eval_interval': 10, 'regularization': 0.001, 'scale_coeff': 0.1, 'freeze_mm_proj': False, 'freeze_vision_tower': True, 'freeze_language_model': False}, 'data_cfgs': {'train_datasets': '/aifs4su/yaodong/hantao/datasets/AA_preference_cosi/val/merged/top1-10', 'train_template': 'AA_TI2T_LLAVA', 'train_size': {}, 'train_split': 'train', 'train_name': 'text-image-to-text', 'train_data_files': {}, 'train_optional_args': [], 'eval_datasets': {}, 'eval_template': {}, 'eval_size': {}, 'eval_split': {}, 'eval_subset': {}, 'eval_data_files': {}, 'eval_optional_args': []}, 'logger_cfgs': {'log_type': 'wandb', 'log_project': 'align-anything', 'log_run_name': 'dpo', 'output_dir': '../outputs/llava_1.6_mistral_7b_val/top1-10', 'cache_dir': {}, 'save_total_limit': 12}, 'model_cfgs': {'model_name_or_path': '/aifs4su/yaodong/hantao/models/llava-v1.6-mistral-7b-hf', 'trust_remote_code': True, 'model_max_length': 4096}, 'special_tokens': {}, '_wandb': {}}
+2025-03-31 17:35:10,831 INFO    MainThread:1800486 [wandb_init.py:init():784] starting backend
+2025-03-31 17:35:10,831 INFO    MainThread:1800486 [wandb_init.py:init():788] sending inform_init request
+2025-03-31 17:35:10,840 INFO    MainThread:1800486 [backend.py:_multiprocessing_setup():101] multiprocessing start_methods=fork,spawn,forkserver, using: spawn
+2025-03-31 17:35:10,840 INFO    MainThread:1800486 [wandb_init.py:init():798] backend started and connected
+2025-03-31 17:35:10,841 INFO    MainThread:1800486 [wandb_init.py:init():891] updated telemetry
+2025-03-31 17:35:10,853 INFO    MainThread:1800486 [wandb_init.py:init():915] communicating run to backend with 90.0 second timeout
+2025-03-31 17:35:11,770 INFO    MainThread:1800486 [wandb_init.py:init():990] starting run threads in backend
+2025-03-31 17:35:12,157 INFO    MainThread:1800486 [wandb_run.py:_console_start():2375] atexit reg
+2025-03-31 17:35:12,157 INFO    MainThread:1800486 [wandb_run.py:_redirect():2227] redirect: wrap_raw
+2025-03-31 17:35:12,157 INFO    MainThread:1800486 [wandb_run.py:_redirect():2292] Wrapping output streams.
+2025-03-31 17:35:12,157 INFO    MainThread:1800486 [wandb_run.py:_redirect():2315] Redirects installed.
+2025-03-31 17:35:12,162 INFO    MainThread:1800486 [wandb_init.py:init():1032] run started, returning control to user process

wandb/run-20250331_173726-4v7gx39g/files/output.log

@@ -0,0 +1,16 @@
+***** Running training *****
+Resuming from checkpoint 1/3 epoch :   0%|                                                                                                                                 | 0/72 [00:00<?, ?it/s]/aifs4su/yaodong/miniconda3/envs/hantao_llama/lib/python3.11/site-packages/torch/utils/checkpoint.py:87: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
+  warnings.warn(
+`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.
+Training 1/3 epoch (loss 0.6289):   7%|████████▌                                                                                                                   | 5/72 [00:17<03:10,  2.84s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (4205 > 4096). Running this sequence through the model will result in indexing errors
+Training 1/3 epoch (loss 0.8125):  10%|████████████                                                                                                                | 7/72 [00:49<12:21, 11.41s/it]
+Saving checkpoint at step 6 ...
+Saving model to "../outputs/llava_1.6_mistral_7b_val/top1-10/slice_6" ...
+Saving 16-bit model...
+[2025-03-31 17:37:56,524] [INFO] [logging.py:107:log_dist] [Rank 0] [Torch] Checkpoint global_step6 is about to be saved!
+[2025-03-31 17:37:56,525] [INFO] [engine.py:3831:save_16bit_model] Saving model weights to ../outputs/llava_1.6_mistral_7b_val/top1-10/slice_6/pytorch_model.bin, tag: global_step6
+[2025-03-31 17:37:56,526] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving ../outputs/llava_1.6_mistral_7b_val/top1-10/slice_6/pytorch_model.bin...
+[2025-03-31 17:38:14,981] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved ../outputs/llava_1.6_mistral_7b_val/top1-10/slice_6/pytorch_model.bin.
+[2025-03-31 17:38:14,981] [INFO] [torch_checkpoint_engine.py:33:commit] [Torch] Checkpoint global_step6 is ready now!
+Model saved!
+Checkpoint saved.

wandb/run-20250331_173726-4v7gx39g/files/requirements.txt

@@ -0,0 +1,258 @@
+maskrcnn_benchmark==0.0.0
+webdataset==0.2.111
+websockets==15.0.1
+typer==0.15.2
+blobfile==3.0.0
+pooch==1.8.2
+filelock==3.18.0
+referencing==0.36.2
+matplotlib==3.10.1
+cachetools==5.5.2
+python-dateutil==2.9.0.post0
+gmpy2==2.2.1
+uvloop==0.21.0
+nvidia-cusparselt-cu12==0.6.2
+clip==0.2.0
+httpcore==1.0.7
+charset-normalizer==3.3.2
+torchlibrosa==0.1.0
+contourpy==1.3.1
+multiprocess==0.70.16
+nest-asyncio==1.6.0
+Werkzeug==3.1.3
+aiofiles==23.2.1
+six==1.17.0
+torch==2.6.0
+sse-starlette==2.2.1
+typing_extensions==4.12.2
+xgrammar==0.1.16
+psutil==7.0.0
+kiwisolver==1.4.8
+moviepy==2.1.2
+frozenlist==1.5.0
+jiter==0.9.0
+einops==0.8.1
+flash_attn==2.7.4.post1
+PySocks==1.7.1
+regex==2024.11.6
+markdown-it-py==3.0.0
+ruff==0.11.2
+docker-pycreds==0.4.0
+nvidia-nvtx-cu12==12.4.127
+pyparsing==3.2.3
+resampy==0.4.3
+tokenizers==0.21.0
+frechet-audio-distance==0.1.2
+aiohappyeyeballs==2.6.1
+llamafactory==0.9.3.dev0
+msgspec==0.19.0
+httpx==0.28.1
+encodec==0.1.1
+ffmpy==0.5.0
+jsonschema==4.23.0
+imageio-ffmpeg==0.6.0
+mkl_random==1.2.8
+fairscale==0.4.13
+soxr==0.5.0.post1
+lark==1.2.2
+gradio==5.21.0
+absl-py==2.2.1
+dnspython==2.7.0
+networkx==3.4.2
+h5py==3.13.0
+hjson==3.1.0
+tensorboard==2.19.0
+aiosignal==1.3.2
+pip==25.0
+nvidia-cublas-cu12==12.4.5.8
+llguidance==0.7.11
+zipp==3.21.0
+ftfy==6.3.1
+peft==0.15.0
+attrs==25.3.0
+trl==0.9.6
+requests==2.32.3
+progressbar==2.5
+sniffio==1.3.1
+pycountry==24.6.1
+lxml==5.3.1
+starlette==0.46.1
+pytest==7.2.0
+Markdown==3.7
+mdurl==0.1.2
+pyzmq==26.3.0
+safetensors==0.5.3
+opencv-python==4.6.0.66
+prometheus-fastapi-instrumentator==7.1.0
+shellingham==1.5.4
+torchvision==0.21.0
+pluggy==1.5.0
+timm==1.0.15
+multidict==6.2.0
+semantic-version==2.10.0
+airportsdata==20250224
+numba==0.60.0
+MarkupSafe==2.1.5
+pydantic_core==2.33.0
+imageio==2.37.0
+nvidia-nccl-cu12==2.21.5
+dill==0.3.8
+msgpack==1.1.0
+sentry-sdk==2.24.1
+rpds-py==0.24.0
+grpcio==1.71.0
+fastrlock==0.8.3
+python-json-logger==3.3.0
+cffi==1.17.1
+gradio_client==1.7.2
+PyYAML==6.0.2
+tensorboard-data-server==0.7.2
+termcolor==2.5.0
+torchaudio==2.6.0
+triton==3.2.0
+fastapi==0.115.12
+clint==0.5.1
+lazy_loader==0.4
+depyf==0.18.0
+mkl_fft==1.3.11
+annotated-types==0.7.0
+scikit-learn==1.6.1
+wget==3.2
+setuptools==75.8.0
+args==0.1.0
+certifi==2025.1.31
+click==8.1.8
+python-dotenv==1.1.0
+laion_clap==1.1.5
+Pygments==2.19.1
+tomlkit==0.13.2
+idna==3.7
+propcache==0.3.1
+platformdirs==4.3.7
+align-anything==0.0.1.dev0
+ray==2.44.1
+cloudpickle==3.1.1
+deepspeed==0.16.5
+smmap==5.0.2
+distro==1.9.0
+fonttools==4.56.0
+typing-inspection==0.4.0
+braceexpand==0.1.7
+decorator==5.2.1
+diskcache==5.6.3
+yt-dlp==2025.3.27
+shtab==1.7.1
+gguf==0.10.0
+interegular==0.3.3
+compressed-tensors==0.9.2
+pandas==2.2.3
+huggingface-hub==0.29.3
+pyarrow==19.0.1
+lm-format-enforcer==0.10.11
+GitPython==3.1.44
+xxhash==3.5.0
+packaging==24.2
+setproctitle==1.3.5
+llvmlite==0.43.0
+tiktoken==0.9.0
+mpmath==1.3.0
+email_validator==2.2.0
+nvidia-ml-py==12.570.86
+pydantic==2.11.0
+xformers==0.0.29.post2
+httptools==0.6.4
+librosa==0.11.0
+pytorch-fid==0.3.0
+hpsv2==1.2.0
+nvidia-cudnn-cu12==9.1.0.70
+iniconfig==2.1.0
+sympy==1.13.1
+safehttpx==0.1.6
+jsonschema-specifications==2024.10.1
+Jinja2==3.1.6
+tyro==0.8.14
+h11==0.14.0
+aiohttp==3.11.14
+diffusers==0.32.2
+tqdm==4.67.1
+blake3==1.0.4
+vllm==0.8.2
+scipy==1.10.1
+audioread==3.0.1
+proglog==0.1.10
+fire==0.7.0
+sentencepiece==0.2.0
+pytz==2025.2
+nvidia-nvjitlink-cu12==12.4.127
+nvidia-curand-cu12==10.3.5.147
+numpy==1.26.4
+tzdata==2025.2
+python-multipart==0.0.20
+urllib3==2.3.0
+pycryptodomex==3.22.0
+yarl==1.18.3
+outlines==0.1.11
+nvidia-cusolver-cu12==11.6.1.9
+pydub==0.25.1
+mistral_common==1.5.4
+pycparser==2.22
+pytest-split==0.8.0
+datasets==3.4.1
+soundfile==0.13.1
+transformers==4.50.0
+image-reward==1.5
+wcwidth==0.2.13
+nvidia-cuda-nvrtc-cu12==12.4.127
+groovy==0.1.2
+nvidia-cuda-runtime-cu12==12.4.127
+astor==0.8.1
+anyio==4.9.0
+wandb==0.19.8
+joblib==1.4.2
+fsspec==2024.12.0
+accelerate==1.5.2
+py-cpuinfo==9.0.0
+docstring_parser==0.16
+partial-json-parser==0.2.1.1.post5
+nvidia-cusparse-cu12==12.3.1.170
+protobuf==3.20.3
+outlines_core==0.1.26
+nvidia-cufft-cu12==11.2.1.3
+cycler==0.12.1
+uvicorn==0.34.0
+orjson==3.10.16
+av==14.2.0
+Brotli==1.0.9
+cupy-cuda12x==13.4.1
+openai==1.69.0
+rich==13.9.4
+importlib_metadata==8.6.1
+ninja==1.11.1.4
+wheel==0.45.1
+pillow==10.4.0
+prometheus_client==0.21.1
+threadpoolctl==3.6.0
+gitdb==4.0.12
+watchfiles==1.0.4
+nvidia-cuda-cupti-cu12==12.4.127
+opencv-python-headless==4.11.0.86
+mkl-service==2.4.0
+rich-toolkit==0.14.0
+fastapi-cli==0.0.7
+llamafactory==0.9.3.dev0
+typing_extensions==4.12.2
+tomli==2.0.1
+zipp==3.19.2
+wheel==0.43.0
+jaraco.text==3.12.1
+packaging==24.2
+autocommand==2.2.2
+jaraco.functools==4.0.1
+jaraco.collections==5.1.0
+platformdirs==4.2.2
+more-itertools==10.3.0
+inflect==7.3.1
+jaraco.context==5.3.0
+typeguard==4.3.0
+backports.tarfile==1.2.0
+importlib_metadata==8.0.0

wandb/run-20250331_173726-4v7gx39g/files/wandb-metadata.json

@@ -0,0 +1,106 @@
+{
+  "os": "Linux-5.15.0-1040-nvidia-x86_64-with-glibc2.35",
+  "python": "CPython 3.11.11",
+  "startedAt": "2025-03-31T09:37:27.001749Z",
+  "args": [
+    "--local_rank=0",
+    "--model_name_or_path",
+    "/aifs4su/yaodong/hantao/models/llava-v1.6-mistral-7b-hf",
+    "--train_datasets",
+    "/aifs4su/yaodong/hantao/datasets/AA_preference_cosi/val/merged/top1-10",
+    "--train_template",
+    "AA_TI2T_LLAVA",
+    "--train_split",
+    "train",
+    "--train_name",
+    "text-image-to-text",
+    "--output_dir",
+    "../outputs/llava_1.6_mistral_7b_val/top1-10",
+    "--save_total_limit",
+    "12",
+    "--train_batch_size",
+    "8",
+    "--epochs",
+    "3"
+  ],
+  "program": "-m align_anything.trainers.text_image_to_text.dpo",
+  "git": {
+    "remote": "git@github.com-hantao:PKU-Alignment/align-anything.git",
+    "commit": "106588f9802757a3283c1aff1f33ea9afd737f31"
+  },
+  "email": "2200017789@stu.pku.edu.cn",
+  "root": "../outputs/llava_1.6_mistral_7b_val/top1-10",
+  "host": "dgx-092",
+  "executable": "/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/python",
+  "cpu_count": 112,
+  "cpu_count_logical": 224,
+  "gpu": "NVIDIA H800",
+  "gpu_count": 8,
+  "disk": {
+    "/": {
+      "total": "1888556142592",
+      "used": "149000749056"
+    }
+  },
+  "memory": {
+    "total": "2164195454976"
+  },
+  "cpu": {
+    "count": 112,
+    "countLogical": 224
+  },
+  "gpu_nvidia": [
+    {
+      "name": "NVIDIA H800",
+      "memoryTotal": "85520809984",
+      "cudaCores": 16896,
+      "architecture": "Hopper"
+    },
+    {
+      "name": "NVIDIA H800",
+      "memoryTotal": "85520809984",
+      "cudaCores": 16896,
+      "architecture": "Hopper"
+    },
+    {
+      "name": "NVIDIA H800",
+      "memoryTotal": "85520809984",
+      "cudaCores": 16896,
+      "architecture": "Hopper"
+    },
+    {
+      "name": "NVIDIA H800",
+      "memoryTotal": "85520809984",
+      "cudaCores": 16896,
+      "architecture": "Hopper"
+    },
+    {
+      "name": "NVIDIA H800",
+      "memoryTotal": "85520809984",
+      "cudaCores": 16896,
+      "architecture": "Hopper"
+    },
+    {
+      "name": "NVIDIA H800",
+      "memoryTotal": "85520809984",
+      "cudaCores": 16896,
+      "architecture": "Hopper"
+    },
+    {
+      "name": "NVIDIA H800",
+      "memoryTotal": "85520809984",
+      "cudaCores": 16896,
+      "architecture": "Hopper"
+    },
+    {
+      "name": "NVIDIA H800",
+      "memoryTotal": "85520809984",
+      "cudaCores": 16896,
+      "architecture": "Hopper"
+    }
+  ],
+  "slurm": {
+    "conf": "/cm/shared/apps/slurm/var/etc/slurm/slurm.conf"
+  },
+  "cudaVersion": "12.2"
+}

wandb/run-20250331_173726-4v7gx39g/logs/debug-core.log

@@ -0,0 +1,7 @@
+{"time":"2025-03-31T17:37:26.427202666+08:00","level":"INFO","msg":"main: starting server","port-filename":"/tmp/tmpp74thbzm/port-1807771.txt","pid":1807771,"log-level":0,"disable-analytics":false,"shutdown-on-parent-exit":false}
+{"time":"2025-03-31T17:37:26.428083467+08:00","level":"INFO","msg":"Will exit if parent process dies.","ppid":1807771}
+{"time":"2025-03-31T17:37:26.428083605+08:00","level":"INFO","msg":"server is running","addr":{"IP":"127.0.0.1","Port":44573,"Zone":""}}
+{"time":"2025-03-31T17:37:26.607453967+08:00","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"127.0.0.1:58146"}
+{"time":"2025-03-31T17:37:27.003133886+08:00","level":"INFO","msg":"handleInformInit: received","streamId":"4v7gx39g","id":"127.0.0.1:58146"}
+{"time":"2025-03-31T17:37:27.321009034+08:00","level":"INFO","msg":"handleInformInit: stream started","streamId":"4v7gx39g","id":"127.0.0.1:58146"}
+{"time":"2025-03-31T17:38:18.696367064+08:00","level":"INFO","msg":"received shutdown signal","signal":15}

wandb/run-20250331_173726-4v7gx39g/logs/debug-internal.log

@@ -0,0 +1,7 @@
+{"time":"2025-03-31T17:37:27.005000621+08:00","level":"INFO","msg":"stream: starting","core version":"0.19.8","symlink path":"../outputs/llava_1.6_mistral_7b_val/top1-10/wandb/run-20250331_173726-4v7gx39g/logs/debug-core.log"}
+{"time":"2025-03-31T17:37:27.320946444+08:00","level":"INFO","msg":"created new stream","id":"4v7gx39g"}
+{"time":"2025-03-31T17:37:27.321000682+08:00","level":"INFO","msg":"stream: started","id":"4v7gx39g"}
+{"time":"2025-03-31T17:37:27.321021439+08:00","level":"INFO","msg":"writer: Do: started","stream_id":"4v7gx39g"}
+{"time":"2025-03-31T17:37:27.32102533+08:00","level":"INFO","msg":"handler: started","stream_id":"4v7gx39g"}
+{"time":"2025-03-31T17:37:27.321061059+08:00","level":"INFO","msg":"sender: started","stream_id":"4v7gx39g"}
+{"time":"2025-03-31T17:37:27.630894994+08:00","level":"INFO","msg":"Starting system monitor"}

wandb/run-20250331_173726-4v7gx39g/logs/debug.log

@@ -0,0 +1,22 @@
+2025-03-31 17:37:26,996 INFO    MainThread:1807771 [wandb_setup.py:_flush():67] Current SDK version is 0.19.8
+2025-03-31 17:37:26,997 INFO    MainThread:1807771 [wandb_setup.py:_flush():67] Configure stats pid to 1807771
+2025-03-31 17:37:26,997 INFO    MainThread:1807771 [wandb_setup.py:_flush():67] Loading settings from /home/yangyaodong/.config/wandb/settings
+2025-03-31 17:37:26,997 INFO    MainThread:1807771 [wandb_setup.py:_flush():67] Loading settings from /aifs4su/yaodong/hantao/align-anything/scripts/wandb/settings
+2025-03-31 17:37:26,997 INFO    MainThread:1807771 [wandb_setup.py:_flush():67] Loading settings from environment variables
+2025-03-31 17:37:26,997 INFO    MainThread:1807771 [wandb_init.py:setup_run_log_directory():647] Logging user logs to ../outputs/llava_1.6_mistral_7b_val/top1-10/wandb/run-20250331_173726-4v7gx39g/logs/debug.log
+2025-03-31 17:37:26,997 INFO    MainThread:1807771 [wandb_init.py:setup_run_log_directory():648] Logging internal logs to ../outputs/llava_1.6_mistral_7b_val/top1-10/wandb/run-20250331_173726-4v7gx39g/logs/debug-internal.log
+2025-03-31 17:37:26,997 INFO    MainThread:1807771 [wandb_init.py:init():761] calling init triggers
+2025-03-31 17:37:26,998 INFO    MainThread:1807771 [wandb_init.py:init():766] wandb.init called with sweep_config: {}
+config: {'train_cfgs': {'save_checkpoint': False, 'load_checkpoint': False, 'ds_cfgs': 'ds_z3_config.json', 'epochs': 3, 'seed': 42, 'per_device_train_batch_size': 1, 'per_device_eval_batch_size': 1, 'gradient_accumulation_steps': 1, 'gradient_checkpointing': True, 'learning_rate': 1e-06, 'lr_scheduler_type': 'cosine', 'lr_warmup_ratio': 0.03, 'weight_decay': 0.0, 'adam_betas': [0.9, 0.95], 'bf16': True, 'fp16': False, 'eval_strategy': 'epoch', 'eval_interval': 10, 'regularization': 0.001, 'scale_coeff': 0.1, 'freeze_mm_proj': False, 'freeze_vision_tower': True, 'freeze_language_model': False}, 'data_cfgs': {'train_datasets': '/aifs4su/yaodong/hantao/datasets/AA_preference_cosi/val/merged/top1-10', 'train_template': 'AA_TI2T_LLAVA', 'train_size': {}, 'train_split': 'train', 'train_name': 'text-image-to-text', 'train_data_files': {}, 'train_optional_args': [], 'eval_datasets': {}, 'eval_template': {}, 'eval_size': {}, 'eval_split': {}, 'eval_subset': {}, 'eval_data_files': {}, 'eval_optional_args': []}, 'logger_cfgs': {'log_type': 'wandb', 'log_project': 'align-anything', 'log_run_name': 'dpo', 'output_dir': '../outputs/llava_1.6_mistral_7b_val/top1-10', 'cache_dir': {}, 'save_total_limit': 12}, 'model_cfgs': {'model_name_or_path': '/aifs4su/yaodong/hantao/models/llava-v1.6-mistral-7b-hf', 'trust_remote_code': True, 'model_max_length': 4096}, 'special_tokens': {}, '_wandb': {}}
+2025-03-31 17:37:26,998 INFO    MainThread:1807771 [wandb_init.py:init():784] starting backend
+2025-03-31 17:37:26,998 INFO    MainThread:1807771 [wandb_init.py:init():788] sending inform_init request
+2025-03-31 17:37:27,001 INFO    MainThread:1807771 [backend.py:_multiprocessing_setup():101] multiprocessing start_methods=fork,spawn,forkserver, using: spawn
+2025-03-31 17:37:27,001 INFO    MainThread:1807771 [wandb_init.py:init():798] backend started and connected
+2025-03-31 17:37:27,002 INFO    MainThread:1807771 [wandb_init.py:init():891] updated telemetry
+2025-03-31 17:37:27,015 INFO    MainThread:1807771 [wandb_init.py:init():915] communicating run to backend with 90.0 second timeout
+2025-03-31 17:37:27,625 INFO    MainThread:1807771 [wandb_init.py:init():990] starting run threads in backend
+2025-03-31 17:37:27,862 INFO    MainThread:1807771 [wandb_run.py:_console_start():2375] atexit reg
+2025-03-31 17:37:27,862 INFO    MainThread:1807771 [wandb_run.py:_redirect():2227] redirect: wrap_raw
+2025-03-31 17:37:27,862 INFO    MainThread:1807771 [wandb_run.py:_redirect():2292] Wrapping output streams.
+2025-03-31 17:37:27,862 INFO    MainThread:1807771 [wandb_run.py:_redirect():2315] Redirects installed.
+2025-03-31 17:37:27,865 INFO    MainThread:1807771 [wandb_init.py:init():1032] run started, returning control to user process

wandb/run-20250331_173929-oflhglry/files/config.yaml

@@ -0,0 +1,95 @@
+_wandb:
+    value:
+        cli_version: 0.19.8
+        m: []
+        python_version: 3.11.11
+        t:
+            "1":
+                - 1
+                - 5
+                - 11
+                - 41
+                - 49
+                - 51
+                - 53
+                - 55
+                - 63
+                - 71
+                - 83
+                - 98
+                - 105
+            "2":
+                - 1
+                - 5
+                - 11
+                - 41
+                - 49
+                - 51
+                - 53
+                - 55
+                - 63
+                - 71
+                - 83
+                - 98
+                - 105
+            "3":
+                - 2
+                - 13
+                - 16
+                - 23
+                - 55
+                - 61
+            "4": 3.11.11
+            "5": 0.19.8
+            "6": 4.50.0
+            "8":
+                - 5
+            "12": 0.19.8
+            "13": linux-x86_64
+data_cfgs:
+    value:
+        eval_optional_args: []
+        train_datasets: /aifs4su/yaodong/hantao/datasets/AA_preference_cosi/val/merged/top1-10
+        train_name: text-image-to-text
+        train_optional_args: []
+        train_split: train
+        train_template: AA_TI2T_LLAVA
+logger_cfgs:
+    value:
+        log_project: align-anything
+        log_run_name: dpo
+        log_type: wandb
+        output_dir: ../outputs/llava_1.6_mistral_7b_val/top1-10
+        save_total_limit: 3
+model_cfgs:
+    value:
+        model_max_length: 4096
+        model_name_or_path: /aifs4su/yaodong/hantao/models/llava-v1.6-mistral-7b-hf
+        trust_remote_code: true
+train_cfgs:
+    value:
+        adam_betas:
+            - 0.9
+            - 0.95
+        bf16: true
+        ds_cfgs: ds_z3_config.json
+        epochs: 3
+        eval_interval: 10
+        eval_strategy: epoch
+        fp16: false
+        freeze_language_model: false
+        freeze_mm_proj: false
+        freeze_vision_tower: true
+        gradient_accumulation_steps: 1
+        gradient_checkpointing: true
+        learning_rate: 1e-06
+        load_checkpoint: false
+        lr_scheduler_type: cosine
+        lr_warmup_ratio: 0.03
+        per_device_eval_batch_size: 1
+        per_device_train_batch_size: 1
+        regularization: 0.001
+        save_checkpoint: false
+        scale_coeff: 0.1
+        seed: 42
+        weight_decay: 0

wandb/run-20250331_173929-oflhglry/files/output.log

@@ -0,0 +1,58 @@
+***** Running training *****
+Resuming from checkpoint 1/3 epoch :   0%|                                                                                                                                 | 0/72 [00:00<?, ?it/s]/aifs4su/yaodong/miniconda3/envs/hantao_llama/lib/python3.11/site-packages/torch/utils/checkpoint.py:87: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
+  warnings.warn(
+`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.
+Training 1/3 epoch (loss 0.6211):   7%|████████▌                                                                                                                   | 5/72 [00:18<03:14,  2.91s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (4205 > 4096). Running this sequence through the model will result in indexing errors
+Training 3/3 epoch (loss 0.0005):  82%|████████████████████████████████████████████████████████████████████████████████████████████████████▊                      | 59/72 [03:15<00:35,  2.70s/it]
+[2025-03-31 17:40:01,717] [INFO] [logging.py:107:log_dist] [Rank 0] step=10, skipped=0, lr=[9.681174353198686e-07, 9.681174353198686e-07], mom=[[0.9, 0.95], [0.9, 0.95]]
+[2025-03-31 17:40:01,718] [INFO] [timer.py:264:stop] epoch=0/micro_step=10/global_step=10, RunningAvgSamplesPerSec=3.722313847981008, CurrSamplesPerSec=2.5449022257494556, MemAllocated=16.38GB, MaxMemAllocated=21.84GB
+[2025-03-31 17:40:23,917] [INFO] [logging.py:107:log_dist] [Rank 0] step=20, skipped=0, lr=[8.455313244934324e-07, 8.455313244934324e-07], mom=[[0.9, 0.95], [0.9, 0.95]]
+[2025-03-31 17:40:23,918] [INFO] [timer.py:264:stop] epoch=0/micro_step=20/global_step=20, RunningAvgSamplesPerSec=4.122870965859232, CurrSamplesPerSec=4.743181933673598, MemAllocated=16.38GB, MaxMemAllocated=21.84GB
+Saving checkpoint at step 24 ...
+Saving model to "../outputs/llava_1.6_mistral_7b_val/top1-10/slice_24" ...
+Saving 16-bit model...
+[2025-03-31 17:40:42,727] [INFO] [logging.py:107:log_dist] [Rank 0] [Torch] Checkpoint global_step24 is about to be saved!
+[2025-03-31 17:40:42,727] [INFO] [engine.py:3831:save_16bit_model] Saving model weights to ../outputs/llava_1.6_mistral_7b_val/top1-10/slice_24/pytorch_model.bin, tag: global_step24
+[2025-03-31 17:40:42,727] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving ../outputs/llava_1.6_mistral_7b_val/top1-10/slice_24/pytorch_model.bin...
+[2025-03-31 17:40:57,986] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved ../outputs/llava_1.6_mistral_7b_val/top1-10/slice_24/pytorch_model.bin.
+[2025-03-31 17:40:57,987] [INFO] [torch_checkpoint_engine.py:33:commit] [Torch] Checkpoint global_step24 is ready now!
+Model saved!
+Checkpoint saved.
+[2025-03-31 17:41:12,404] [INFO] [logging.py:107:log_dist] [Rank 0] step=30, skipped=0, lr=[6.545084971874736e-07, 6.545084971874736e-07], mom=[[0.9, 0.95], [0.9, 0.95]]
+[2025-03-31 17:41:12,405] [INFO] [timer.py:264:stop] epoch=1/micro_step=6/global_step=30, RunningAvgSamplesPerSec=4.02456931626677, CurrSamplesPerSec=3.240653361782424, MemAllocated=16.38GB, MaxMemAllocated=21.84GB
+[2025-03-31 17:41:36,676] [INFO] [logging.py:107:log_dist] [Rank 0] step=40, skipped=0, lr=[4.328833670911724e-07, 4.328833670911724e-07], mom=[[0.9, 0.95], [0.9, 0.95]]
+[2025-03-31 17:41:36,677] [INFO] [timer.py:264:stop] epoch=1/micro_step=16/global_step=40, RunningAvgSamplesPerSec=4.025850941473662, CurrSamplesPerSec=4.080984649502892, MemAllocated=16.38GB, MaxMemAllocated=21.84GB
+Saving checkpoint at step 48 ...
+Saving model to "../outputs/llava_1.6_mistral_7b_val/top1-10/slice_48" ...
+Saving 16-bit model...
+[2025-03-31 17:42:04,512] [INFO] [logging.py:107:log_dist] [Rank 0] [Torch] Checkpoint global_step48 is about to be saved!
+[2025-03-31 17:42:04,513] [INFO] [engine.py:3831:save_16bit_model] Saving model weights to ../outputs/llava_1.6_mistral_7b_val/top1-10/slice_48/pytorch_model.bin, tag: global_step48
+[2025-03-31 17:42:04,514] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving ../outputs/llava_1.6_mistral_7b_val/top1-10/slice_48/pytorch_model.bin...
+[2025-03-31 17:42:18,415] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved ../outputs/llava_1.6_mistral_7b_val/top1-10/slice_48/pytorch_model.bin.
+[2025-03-31 17:42:18,416] [INFO] [torch_checkpoint_engine.py:33:commit] [Torch] Checkpoint global_step48 is ready now!
+Model saved!
+Checkpoint saved.
+[2025-03-31 17:42:22,734] [INFO] [logging.py:107:log_dist] [Rank 0] step=50, skipped=0, lr=[2.2455150927394878e-07, 2.2455150927394878e-07], mom=[[0.9, 0.95], [0.9, 0.95]]
+[2025-03-31 17:42:22,735] [INFO] [timer.py:264:stop] epoch=2/micro_step=2/global_step=50, RunningAvgSamplesPerSec=4.051799326557334, CurrSamplesPerSec=5.4580888670132115, MemAllocated=16.37GB, MaxMemAllocated=21.84GB
+[2025-03-31 17:42:47,922] [INFO] [logging.py:107:log_dist] [Rank 0] step=60, skipped=0, lr=[7.077560319906694e-08, 7.077560319906694e-08], mom=[[0.9, 0.95], [0.9, 0.95]]
+[2025-03-31 17:42:47,923] [INFO] [timer.py:264:stop] epoch=2/micro_step=12/global_step=60, RunningAvgSamplesPerSec=4.025901311666018, CurrSamplesPerSec=3.85640209809824, MemAllocated=16.38GB, MaxMemAllocated=21.84GB
+[2025-03-31 17:43:11,317] [INFO] [logging.py:107:log_dist] [Rank 0] step=70, skipped=0, lr=[2.0128530023804656e-09, 2.0128530023804656e-09], mom=[[0.9, 0.95], [0.9, 0.95]]
+[2025-03-31 17:43:11,317] [INFO] [timer.py:264:stop] epoch=2/micro_step=22/global_step=70, RunningAvgSamplesPerSec=4.050512963952101, CurrSamplesPerSec=4.751966789154519, MemAllocated=16.38GB, MaxMemAllocated=21.84GB
+Saving checkpoint at step 72 ...
+Saving model to "../outputs/llava_1.6_mistral_7b_val/top1-10/slice_72" ...
+Saving 16-bit model...
+[2025-03-31 17:43:25,067] [INFO] [logging.py:107:log_dist] [Rank 0] [Torch] Checkpoint global_step72 is about to be saved!
+[2025-03-31 17:43:25,068] [INFO] [engine.py:3831:save_16bit_model] Saving model weights to ../outputs/llava_1.6_mistral_7b_val/top1-10/slice_72/pytorch_model.bin, tag: global_step72
+[2025-03-31 17:43:25,068] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving ../outputs/llava_1.6_mistral_7b_val/top1-10/slice_72/pytorch_model.bin...
+[2025-03-31 17:43:39,343] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved ../outputs/llava_1.6_mistral_7b_val/top1-10/slice_72/pytorch_model.bin.
+[2025-03-31 17:43:39,344] [INFO] [torch_checkpoint_engine.py:33:commit] [Torch] Checkpoint global_step72 is ready now!
+Model saved!
+Checkpoint saved.
+Saving model to "../outputs/llava_1.6_mistral_7b_val/top1-10/slice_end" ...
+Saving 16-bit model...
+[2025-03-31 17:43:47,490] [INFO] [logging.py:107:log_dist] [Rank 0] [Torch] Checkpoint global_step72 is about to be saved!
+[2025-03-31 17:43:47,491] [INFO] [engine.py:3831:save_16bit_model] Saving model weights to ../outputs/llava_1.6_mistral_7b_val/top1-10/slice_end/pytorch_model.bin, tag: global_step72
+[2025-03-31 17:43:47,491] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving ../outputs/llava_1.6_mistral_7b_val/top1-10/slice_end/pytorch_model.bin...
+[2025-03-31 17:44:01,346] [INFO] [torch_checkpoint_engine.py:23:save] [Torch] Saved ../outputs/llava_1.6_mistral_7b_val/top1-10/slice_end/pytorch_model.bin.
+[2025-03-31 17:44:01,347] [INFO] [torch_checkpoint_engine.py:33:commit] [Torch] Checkpoint global_step72 is ready now!
+Model saved!

wandb/run-20250331_173929-oflhglry/files/requirements.txt

@@ -0,0 +1,258 @@
+maskrcnn_benchmark==0.0.0
+webdataset==0.2.111
+websockets==15.0.1
+typer==0.15.2
+blobfile==3.0.0
+pooch==1.8.2
+filelock==3.18.0
+referencing==0.36.2
+matplotlib==3.10.1
+cachetools==5.5.2
+python-dateutil==2.9.0.post0
+gmpy2==2.2.1
+uvloop==0.21.0
+nvidia-cusparselt-cu12==0.6.2
+clip==0.2.0
+httpcore==1.0.7
+charset-normalizer==3.3.2
+torchlibrosa==0.1.0
+contourpy==1.3.1
+multiprocess==0.70.16
+nest-asyncio==1.6.0
+Werkzeug==3.1.3
+aiofiles==23.2.1
+six==1.17.0
+torch==2.6.0
+sse-starlette==2.2.1
+typing_extensions==4.12.2
+xgrammar==0.1.16
+psutil==7.0.0
+kiwisolver==1.4.8
+moviepy==2.1.2
+frozenlist==1.5.0
+jiter==0.9.0
+einops==0.8.1
+flash_attn==2.7.4.post1
+PySocks==1.7.1
+regex==2024.11.6
+markdown-it-py==3.0.0
+ruff==0.11.2
+docker-pycreds==0.4.0
+nvidia-nvtx-cu12==12.4.127
+pyparsing==3.2.3
+resampy==0.4.3
+tokenizers==0.21.0
+frechet-audio-distance==0.1.2
+aiohappyeyeballs==2.6.1
+llamafactory==0.9.3.dev0
+msgspec==0.19.0
+httpx==0.28.1
+encodec==0.1.1
+ffmpy==0.5.0
+jsonschema==4.23.0
+imageio-ffmpeg==0.6.0
+mkl_random==1.2.8
+fairscale==0.4.13
+soxr==0.5.0.post1
+lark==1.2.2
+gradio==5.21.0
+absl-py==2.2.1
+dnspython==2.7.0
+networkx==3.4.2
+h5py==3.13.0
+hjson==3.1.0
+tensorboard==2.19.0
+aiosignal==1.3.2
+pip==25.0
+nvidia-cublas-cu12==12.4.5.8
+llguidance==0.7.11
+zipp==3.21.0
+ftfy==6.3.1
+peft==0.15.0
+attrs==25.3.0
+trl==0.9.6
+requests==2.32.3
+progressbar==2.5
+sniffio==1.3.1
+pycountry==24.6.1
+lxml==5.3.1
+starlette==0.46.1
+pytest==7.2.0
+Markdown==3.7
+mdurl==0.1.2
+pyzmq==26.3.0
+safetensors==0.5.3
+opencv-python==4.6.0.66
+prometheus-fastapi-instrumentator==7.1.0
+shellingham==1.5.4
+torchvision==0.21.0
+pluggy==1.5.0
+timm==1.0.15
+multidict==6.2.0
+semantic-version==2.10.0
+airportsdata==20250224
+numba==0.60.0
+MarkupSafe==2.1.5
+pydantic_core==2.33.0
+imageio==2.37.0
+nvidia-nccl-cu12==2.21.5
+dill==0.3.8
+msgpack==1.1.0
+sentry-sdk==2.24.1
+rpds-py==0.24.0
+grpcio==1.71.0
+fastrlock==0.8.3
+python-json-logger==3.3.0
+cffi==1.17.1
+gradio_client==1.7.2
+PyYAML==6.0.2
+tensorboard-data-server==0.7.2
+termcolor==2.5.0
+torchaudio==2.6.0
+triton==3.2.0
+fastapi==0.115.12
+clint==0.5.1
+lazy_loader==0.4
+depyf==0.18.0
+mkl_fft==1.3.11
+annotated-types==0.7.0
+scikit-learn==1.6.1
+wget==3.2
+setuptools==75.8.0
+args==0.1.0
+certifi==2025.1.31
+click==8.1.8
+python-dotenv==1.1.0
+laion_clap==1.1.5
+Pygments==2.19.1
+tomlkit==0.13.2
+idna==3.7
+propcache==0.3.1
+platformdirs==4.3.7
+align-anything==0.0.1.dev0
+ray==2.44.1
+cloudpickle==3.1.1
+deepspeed==0.16.5
+smmap==5.0.2
+distro==1.9.0
+fonttools==4.56.0
+typing-inspection==0.4.0
+braceexpand==0.1.7
+decorator==5.2.1
+diskcache==5.6.3
+yt-dlp==2025.3.27
+shtab==1.7.1
+gguf==0.10.0
+interegular==0.3.3
+compressed-tensors==0.9.2
+pandas==2.2.3
+huggingface-hub==0.29.3
+pyarrow==19.0.1
+lm-format-enforcer==0.10.11
+GitPython==3.1.44
+xxhash==3.5.0
+packaging==24.2
+setproctitle==1.3.5
+llvmlite==0.43.0
+tiktoken==0.9.0
+mpmath==1.3.0
+email_validator==2.2.0
+nvidia-ml-py==12.570.86
+pydantic==2.11.0
+xformers==0.0.29.post2
+httptools==0.6.4
+librosa==0.11.0
+pytorch-fid==0.3.0
+hpsv2==1.2.0
+nvidia-cudnn-cu12==9.1.0.70
+iniconfig==2.1.0
+sympy==1.13.1
+safehttpx==0.1.6
+jsonschema-specifications==2024.10.1
+Jinja2==3.1.6
+tyro==0.8.14
+h11==0.14.0
+aiohttp==3.11.14
+diffusers==0.32.2
+tqdm==4.67.1
+blake3==1.0.4
+vllm==0.8.2
+scipy==1.10.1
+audioread==3.0.1
+proglog==0.1.10
+fire==0.7.0
+sentencepiece==0.2.0
+pytz==2025.2
+nvidia-nvjitlink-cu12==12.4.127
+nvidia-curand-cu12==10.3.5.147
+numpy==1.26.4
+tzdata==2025.2
+python-multipart==0.0.20
+urllib3==2.3.0
+pycryptodomex==3.22.0
+yarl==1.18.3
+outlines==0.1.11
+nvidia-cusolver-cu12==11.6.1.9
+pydub==0.25.1
+mistral_common==1.5.4
+pycparser==2.22
+pytest-split==0.8.0
+datasets==3.4.1
+soundfile==0.13.1
+transformers==4.50.0
+image-reward==1.5
+wcwidth==0.2.13
+nvidia-cuda-nvrtc-cu12==12.4.127
+groovy==0.1.2
+nvidia-cuda-runtime-cu12==12.4.127
+astor==0.8.1
+anyio==4.9.0
+wandb==0.19.8
+joblib==1.4.2
+fsspec==2024.12.0
+accelerate==1.5.2
+py-cpuinfo==9.0.0
+docstring_parser==0.16
+partial-json-parser==0.2.1.1.post5
+nvidia-cusparse-cu12==12.3.1.170
+protobuf==3.20.3
+outlines_core==0.1.26
+nvidia-cufft-cu12==11.2.1.3
+cycler==0.12.1
+uvicorn==0.34.0
+orjson==3.10.16
+av==14.2.0
+Brotli==1.0.9
+cupy-cuda12x==13.4.1
+openai==1.69.0
+rich==13.9.4
+importlib_metadata==8.6.1
+ninja==1.11.1.4
+wheel==0.45.1
+pillow==10.4.0
+prometheus_client==0.21.1
+threadpoolctl==3.6.0
+gitdb==4.0.12
+watchfiles==1.0.4
+nvidia-cuda-cupti-cu12==12.4.127
+opencv-python-headless==4.11.0.86
+mkl-service==2.4.0
+rich-toolkit==0.14.0
+fastapi-cli==0.0.7
+llamafactory==0.9.3.dev0
+typing_extensions==4.12.2
+tomli==2.0.1
+zipp==3.19.2
+wheel==0.43.0
+jaraco.text==3.12.1
+packaging==24.2
+autocommand==2.2.2
+jaraco.functools==4.0.1
+jaraco.collections==5.1.0
+platformdirs==4.2.2
+more-itertools==10.3.0
+inflect==7.3.1
+jaraco.context==5.3.0
+typeguard==4.3.0
+backports.tarfile==1.2.0
+importlib_metadata==8.0.0

wandb/run-20250331_173929-oflhglry/files/wandb-metadata.json

@@ -0,0 +1,106 @@
+{
+  "os": "Linux-5.15.0-1040-nvidia-x86_64-with-glibc2.35",
+  "python": "CPython 3.11.11",
+  "startedAt": "2025-03-31T09:39:29.355313Z",
+  "args": [
+    "--local_rank=0",
+    "--model_name_or_path",
+    "/aifs4su/yaodong/hantao/models/llava-v1.6-mistral-7b-hf",
+    "--train_datasets",
+    "/aifs4su/yaodong/hantao/datasets/AA_preference_cosi/val/merged/top1-10",
+    "--train_template",
+    "AA_TI2T_LLAVA",
+    "--train_split",
+    "train",
+    "--train_name",
+    "text-image-to-text",
+    "--output_dir",
+    "../outputs/llava_1.6_mistral_7b_val/top1-10",
+    "--save_total_limit",
+    "3",
+    "--train_batch_size",
+    "8",
+    "--epochs",
+    "3"
+  ],
+  "program": "-m align_anything.trainers.text_image_to_text.dpo",
+  "git": {
+    "remote": "git@github.com-hantao:PKU-Alignment/align-anything.git",
+    "commit": "106588f9802757a3283c1aff1f33ea9afd737f31"
+  },
+  "email": "2200017789@stu.pku.edu.cn",
+  "root": "../outputs/llava_1.6_mistral_7b_val/top1-10",
+  "host": "dgx-092",
+  "executable": "/aifs4su/yaodong/miniconda3/envs/hantao_llama/bin/python",
+  "cpu_count": 112,
+  "cpu_count_logical": 224,
+  "gpu": "NVIDIA H800",
+  "gpu_count": 8,
+  "disk": {
+    "/": {
+      "total": "1888556142592",
+      "used": "149001129984"
+    }
+  },
+  "memory": {
+    "total": "2164195454976"
+  },
+  "cpu": {
+    "count": 112,
+    "countLogical": 224
+  },
+  "gpu_nvidia": [
+    {
+      "name": "NVIDIA H800",
+      "memoryTotal": "85520809984",
+      "cudaCores": 16896,
+      "architecture": "Hopper"
+    },
+    {
+      "name": "NVIDIA H800",
+      "memoryTotal": "85520809984",
+      "cudaCores": 16896,
+      "architecture": "Hopper"
+    },
+    {
+      "name": "NVIDIA H800",
+      "memoryTotal": "85520809984",
+      "cudaCores": 16896,
+      "architecture": "Hopper"
+    },
+    {
+      "name": "NVIDIA H800",
+      "memoryTotal": "85520809984",
+      "cudaCores": 16896,
+      "architecture": "Hopper"
+    },
+    {
+      "name": "NVIDIA H800",
+      "memoryTotal": "85520809984",
+      "cudaCores": 16896,
+      "architecture": "Hopper"
+    },
+    {
+      "name": "NVIDIA H800",
+      "memoryTotal": "85520809984",
+      "cudaCores": 16896,
+      "architecture": "Hopper"
+    },
+    {
+      "name": "NVIDIA H800",
+      "memoryTotal": "85520809984",
+      "cudaCores": 16896,
+      "architecture": "Hopper"
+    },
+    {
+      "name": "NVIDIA H800",
+      "memoryTotal": "85520809984",
+      "cudaCores": 16896,
+      "architecture": "Hopper"
+    }
+  ],
+  "slurm": {
+    "conf": "/cm/shared/apps/slurm/var/etc/slurm/slurm.conf"
+  },
+  "cudaVersion": "12.2"
+}

wandb/run-20250331_173929-oflhglry/files/wandb-summary.json

@@ -0,0 +1 @@
+{"train/reward_accuracy":1,"train/better_sample_reward":12.8125,"train/lr":0,"train/loss":2.372264862060547e-05,"_timestamp":1.7434141965928717e+09,"train/reward_margin":23.25,"train/reward":2.53125,"_wandb":{"runtime":272},"train/epoch":3,"train/step":72,"_runtime":272.034614709,"train/worse_sample_reward":-10.25,"_step":72}

wandb/run-20250331_173929-oflhglry/logs/debug-core.log

@@ -0,0 +1,14 @@
+{"time":"2025-03-31T17:39:28.778210084+08:00","level":"INFO","msg":"main: starting server","port-filename":"/tmp/tmpv0i9nviv/port-1814399.txt","pid":1814399,"log-level":0,"disable-analytics":false,"shutdown-on-parent-exit":false}
+{"time":"2025-03-31T17:39:28.779087954+08:00","level":"INFO","msg":"Will exit if parent process dies.","ppid":1814399}
+{"time":"2025-03-31T17:39:28.77907394+08:00","level":"INFO","msg":"server is running","addr":{"IP":"127.0.0.1","Port":39655,"Zone":""}}
+{"time":"2025-03-31T17:39:28.95893708+08:00","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"127.0.0.1:59496"}
+{"time":"2025-03-31T17:39:29.356525972+08:00","level":"INFO","msg":"handleInformInit: received","streamId":"oflhglry","id":"127.0.0.1:59496"}
+{"time":"2025-03-31T17:39:29.573174417+08:00","level":"INFO","msg":"handleInformInit: stream started","streamId":"oflhglry","id":"127.0.0.1:59496"}
+{"time":"2025-03-31T17:44:02.902545963+08:00","level":"INFO","msg":"handleInformTeardown: server teardown initiated","id":"127.0.0.1:59496"}
+{"time":"2025-03-31T17:44:02.902715372+08:00","level":"INFO","msg":"server is shutting down"}
+{"time":"2025-03-31T17:44:02.902714048+08:00","level":"INFO","msg":"connection: closing","id":"127.0.0.1:59496"}
+{"time":"2025-03-31T17:44:02.902792273+08:00","level":"INFO","msg":"connection: closed successfully","id":"127.0.0.1:59496"}
+{"time":"2025-03-31T17:44:03.099880037+08:00","level":"ERROR","msg":"processOutgoingData: flush error","error":"write tcp 127.0.0.1:39655->127.0.0.1:59496: use of closed network connection","id":"127.0.0.1:59496"}
+{"time":"2025-03-31T17:44:03.100151996+08:00","level":"INFO","msg":"handleInformTeardown: server shutdown complete","id":"127.0.0.1:59496"}
+{"time":"2025-03-31T17:44:03.100161974+08:00","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"127.0.0.1:59496"}
+{"time":"2025-03-31T17:44:03.100166112+08:00","level":"INFO","msg":"server is closed"}

wandb/run-20250331_173929-oflhglry/logs/debug-internal.log

@@ -0,0 +1,16 @@
+{"time":"2025-03-31T17:39:29.357857467+08:00","level":"INFO","msg":"stream: starting","core version":"0.19.8","symlink path":"../outputs/llava_1.6_mistral_7b_val/top1-10/wandb/run-20250331_173929-oflhglry/logs/debug-core.log"}
+{"time":"2025-03-31T17:39:29.573098876+08:00","level":"INFO","msg":"created new stream","id":"oflhglry"}
+{"time":"2025-03-31T17:39:29.573167483+08:00","level":"INFO","msg":"stream: started","id":"oflhglry"}
+{"time":"2025-03-31T17:39:29.573190711+08:00","level":"INFO","msg":"handler: started","stream_id":"oflhglry"}
+{"time":"2025-03-31T17:39:29.573196411+08:00","level":"INFO","msg":"writer: Do: started","stream_id":"oflhglry"}
+{"time":"2025-03-31T17:39:29.573195051+08:00","level":"INFO","msg":"sender: started","stream_id":"oflhglry"}
+{"time":"2025-03-31T17:39:29.880478815+08:00","level":"INFO","msg":"Starting system monitor"}
+{"time":"2025-03-31T17:44:01.38994239+08:00","level":"INFO","msg":"Stopping system monitor"}
+{"time":"2025-03-31T17:44:01.39093428+08:00","level":"INFO","msg":"Stopped system monitor"}
+{"time":"2025-03-31T17:44:02.852694686+08:00","level":"INFO","msg":"fileTransfer: Close: file transfer manager closed"}
+{"time":"2025-03-31T17:44:02.902712341+08:00","level":"INFO","msg":"stream: closing","id":"oflhglry"}
+{"time":"2025-03-31T17:44:02.902739889+08:00","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
+{"time":"2025-03-31T17:44:03.099767177+08:00","level":"INFO","msg":"handler: closed","stream_id":"oflhglry"}
+{"time":"2025-03-31T17:44:03.099816798+08:00","level":"INFO","msg":"writer: Close: closed","stream_id":"oflhglry"}
+{"time":"2025-03-31T17:44:03.099920876+08:00","level":"INFO","msg":"sender: closed","stream_id":"oflhglry"}
+{"time":"2025-03-31T17:44:03.100069001+08:00","level":"INFO","msg":"stream: closed","id":"oflhglry"}

wandb/run-20250331_173929-oflhglry/logs/debug.log

@@ -0,0 +1,47 @@
+2025-03-31 17:39:29,349 INFO    MainThread:1814399 [wandb_setup.py:_flush():67] Current SDK version is 0.19.8
+2025-03-31 17:39:29,349 INFO    MainThread:1814399 [wandb_setup.py:_flush():67] Configure stats pid to 1814399
+2025-03-31 17:39:29,349 INFO    MainThread:1814399 [wandb_setup.py:_flush():67] Loading settings from /home/yangyaodong/.config/wandb/settings
+2025-03-31 17:39:29,349 INFO    MainThread:1814399 [wandb_setup.py:_flush():67] Loading settings from /aifs4su/yaodong/hantao/align-anything/scripts/wandb/settings
+2025-03-31 17:39:29,349 INFO    MainThread:1814399 [wandb_setup.py:_flush():67] Loading settings from environment variables
+2025-03-31 17:39:29,349 INFO    MainThread:1814399 [wandb_init.py:setup_run_log_directory():647] Logging user logs to ../outputs/llava_1.6_mistral_7b_val/top1-10/wandb/run-20250331_173929-oflhglry/logs/debug.log
+2025-03-31 17:39:29,349 INFO    MainThread:1814399 [wandb_init.py:setup_run_log_directory():648] Logging internal logs to ../outputs/llava_1.6_mistral_7b_val/top1-10/wandb/run-20250331_173929-oflhglry/logs/debug-internal.log
+2025-03-31 17:39:29,349 INFO    MainThread:1814399 [wandb_init.py:init():761] calling init triggers
+2025-03-31 17:39:29,350 INFO    MainThread:1814399 [wandb_init.py:init():766] wandb.init called with sweep_config: {}
+config: {'train_cfgs': {'save_checkpoint': False, 'load_checkpoint': False, 'ds_cfgs': 'ds_z3_config.json', 'epochs': 3, 'seed': 42, 'per_device_train_batch_size': 1, 'per_device_eval_batch_size': 1, 'gradient_accumulation_steps': 1, 'gradient_checkpointing': True, 'learning_rate': 1e-06, 'lr_scheduler_type': 'cosine', 'lr_warmup_ratio': 0.03, 'weight_decay': 0.0, 'adam_betas': [0.9, 0.95], 'bf16': True, 'fp16': False, 'eval_strategy': 'epoch', 'eval_interval': 10, 'regularization': 0.001, 'scale_coeff': 0.1, 'freeze_mm_proj': False, 'freeze_vision_tower': True, 'freeze_language_model': False}, 'data_cfgs': {'train_datasets': '/aifs4su/yaodong/hantao/datasets/AA_preference_cosi/val/merged/top1-10', 'train_template': 'AA_TI2T_LLAVA', 'train_size': {}, 'train_split': 'train', 'train_name': 'text-image-to-text', 'train_data_files': {}, 'train_optional_args': [], 'eval_datasets': {}, 'eval_template': {}, 'eval_size': {}, 'eval_split': {}, 'eval_subset': {}, 'eval_data_files': {}, 'eval_optional_args': []}, 'logger_cfgs': {'log_type': 'wandb', 'log_project': 'align-anything', 'log_run_name': 'dpo', 'output_dir': '../outputs/llava_1.6_mistral_7b_val/top1-10', 'cache_dir': {}, 'save_total_limit': 3}, 'model_cfgs': {'model_name_or_path': '/aifs4su/yaodong/hantao/models/llava-v1.6-mistral-7b-hf', 'trust_remote_code': True, 'model_max_length': 4096}, 'special_tokens': {}, '_wandb': {}}
+2025-03-31 17:39:29,350 INFO    MainThread:1814399 [wandb_init.py:init():784] starting backend
+2025-03-31 17:39:29,350 INFO    MainThread:1814399 [wandb_init.py:init():788] sending inform_init request
+2025-03-31 17:39:29,355 INFO    MainThread:1814399 [backend.py:_multiprocessing_setup():101] multiprocessing start_methods=fork,spawn,forkserver, using: spawn
+2025-03-31 17:39:29,355 INFO    MainThread:1814399 [wandb_init.py:init():798] backend started and connected
+2025-03-31 17:39:29,357 INFO    MainThread:1814399 [wandb_init.py:init():891] updated telemetry
+2025-03-31 17:39:29,371 INFO    MainThread:1814399 [wandb_init.py:init():915] communicating run to backend with 90.0 second timeout
+2025-03-31 17:39:29,874 INFO    MainThread:1814399 [wandb_init.py:init():990] starting run threads in backend
+2025-03-31 17:39:30,115 INFO    MainThread:1814399 [wandb_run.py:_console_start():2375] atexit reg
+2025-03-31 17:39:30,115 INFO    MainThread:1814399 [wandb_run.py:_redirect():2227] redirect: wrap_raw
+2025-03-31 17:39:30,115 INFO    MainThread:1814399 [wandb_run.py:_redirect():2292] Wrapping output streams.
+2025-03-31 17:39:30,116 INFO    MainThread:1814399 [wandb_run.py:_redirect():2315] Redirects installed.
+2025-03-31 17:39:30,118 INFO    MainThread:1814399 [wandb_init.py:init():1032] run started, returning control to user process
+2025-03-31 17:44:01,349 INFO    MainThread:1814399 [wandb_run.py:_finish():2112] finishing run htlou/align-anything/oflhglry
+2025-03-31 17:44:01,350 INFO    MainThread:1814399 [wandb_run.py:_atexit_cleanup():2340] got exitcode: 0
+2025-03-31 17:44:01,350 INFO    MainThread:1814399 [wandb_run.py:_restore():2322] restore
+2025-03-31 17:44:01,351 INFO    MainThread:1814399 [wandb_run.py:_restore():2328] restore done
+2025-03-31 17:44:02,351 INFO    MainThread:1814399 [wandb_run.py:_restore():2322] restore
+2025-03-31 17:44:02,352 INFO    MainThread:1814399 [wandb_run.py:_restore():2328] restore done
+2025-03-31 17:44:02,352 ERROR   MainThread:1814399 [wandb_run.py:_atexit_cleanup():2361] Problem finishing run
+Traceback (most recent call last):
+  File "/aifs4su/yaodong/miniconda3/envs/hantao_llama/lib/python3.11/site-packages/wandb/sdk/wandb_run.py", line 2352, in _atexit_cleanup
+    self._on_finish()
+  File "/aifs4su/yaodong/miniconda3/envs/hantao_llama/lib/python3.11/site-packages/wandb/sdk/wandb_run.py", line 2609, in _on_finish
+    wait_with_progress(
+  File "/aifs4su/yaodong/miniconda3/envs/hantao_llama/lib/python3.11/site-packages/wandb/sdk/mailbox/wait_with_progress.py", line 24, in wait_with_progress
+    return wait_all_with_progress(
+           ^^^^^^^^^^^^^^^^^^^^^^^
+  File "/aifs4su/yaodong/miniconda3/envs/hantao_llama/lib/python3.11/site-packages/wandb/sdk/mailbox/wait_with_progress.py", line 87, in wait_all_with_progress
+    return asyncio_compat.run(progress_loop_with_timeout)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/aifs4su/yaodong/miniconda3/envs/hantao_llama/lib/python3.11/site-packages/wandb/sdk/lib/asyncio_compat.py", line 27, in run
+    future = executor.submit(runner.run, fn)
+             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/aifs4su/yaodong/miniconda3/envs/hantao_llama/lib/python3.11/concurrent/futures/thread.py", line 169, in submit
+    raise RuntimeError('cannot schedule new futures after '
+RuntimeError: cannot schedule new futures after interpreter shutdown
+2025-03-31 17:44:02,902 INFO    MsgRouterThr:1814399 [mailbox.py:close():129] Closing mailbox, abandoning 2 handles.

wandb/run-20250331_173929-oflhglry/run-oflhglry.wandb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a26e6c684540f750d23ff07cee6a7862cb1eec1d4e30a0eb65cf9720a19af204
+size 204949