README.md

metadata

license: mit

Bo Li*¹  Yuanhan Zhang*^,1  Liangyu Chen*^,1  Jinghao Wang*^,1  Fanyi Pu*^,1 
Jingkang Yang¹  Chunyuan Li²  Ziwei Liu¹

¹S-Lab, Nanyang Technological University  ²Microsoft Research, Redmond

This weight is for initilizing training for Otter. It's directly converted from Openflamingo.

You can load and try this model using

model = OtterForConditionalGeneration.from_pretrained("luodian/OTTER-MPT7B-Init", device_map="sequential")
model.text_tokenizer.padding_side = "left"
tokenizer = model.text_tokenizer
image_processor = transformers.CLIPImageProcessor()
model.eval()

You can also start training Otter via the commands

python -m accelerate.commands.launch --config_file=./pipeline/accelerate_configs/accelerate_config_fsdp.yaml \
pipeline/train/instruction_following.py \
--pretrained_model_name_or_path=luodian/OTTER-MPT7B-Init \
--mimicit_path=/data/azure_storage/otter/mimicit/xx/xx_instructions.json \
--images_path=/data/azure_storage/otter/mimicit/xx/xx.json \
--batch_size=4 --num_epochs=1 --report_to_wandb \
--wandb_entity=ntu-slab \
--external_save_dir=/data/bli/checkpoints \
--save_hf_model \
--run_name=OTTER-MPT1B \
--wandb_project=OTTER-MPT1B \
--workers=4 \
--lr_scheduler=cosine \
--learning_rate=1e-5 \
--warmup_steps_ratio=0.01

If you wish to init a video instruction tuning, you should add

"max_num_frames": 128

to config.json inside the folder.