OTTER MPT7B Init

🚀 Otter Model Initialization Weight

This project provides the initialization weight for training Otter. It is directly converted from Openflamingo, aiming to help users quickly start the training process of Otter.

🚀 Quick Start

Load and Try the Model

You can load and try this model using the following code:

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()

Start Training Otter

You can start training Otter via the following 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

Initialize Video Instruction Tuning

If you wish to init a video instruction tuning, you should add the following to config.json inside the folder:

"max_num_frames": 128

📄 License

This project is licensed under the MIT License.