![]() ![]() Thank you for making your code public! We also thank the OpenCLIP team as we use their data loading code and take inspiration from their library design. This code is based on Lucidrains' flamingo implementation and David Hansmair's flamingo-mini repo. The team is primarily from the University of Washington, Stanford, AI2, UCSB, and Google. To run evaluations on OKVQA you will need to run the following command:Īnas Awadalla*, Irena Gao*, Joshua Gardner, Jack Hessel, Yusuf Hanafy, Wanrong Zhu, Kalyani Marathe, Yonatan Bitton, Samir Gadre, Shiori Sagawa, Jenia Jitsev, Simon Kornblith, Pang Wei Koh, Gabriel Ilharco, Mitchell Wortsman, Ludwig Schmidt. Please see our evaluation README for more details. EvaluationĪn example evaluation script is at open_flamingo/scripts/run_eval.sh. We suggest using a modified version of the MPT-1B models found here and here.įor more details, see our training README. Note: The MPT-1B base and instruct modeling code does not accept the labels kwarg or compute cross-entropy loss directly within forward(), as expected by our codebase. laion_shards "/path/to/shards/shard-.tar" \ tokenizer_path anas-awadalla/mpt-1b-redpajama-200b \ lm_path anas-awadalla/mpt-1b-redpajama-200b \ Torchrun -nnodes=1 -nproc_per_node=4 open_flamingo/train/train.py \ We provide an example Slurm script in open_flamingo/scripts/run_train.py, as well as the following example command: We provide training scripts in open_flamingo/train. ![]() """ Step 4: Generate text """ generated_text = model. padding_side = "left" # For generation padding tokens should be on the left lang_x = tokenizer( We also expect an special token to indicate the end of the text portion associated with an image. """ Step 3: Preprocessing text Details: In the text we expect an special token to indicate where an image is. In this case batch_size = 1, num_media = 3, num_frames = 1, channels = 3, height = 224, width = 224. """ Step 2: Preprocessing images Details: For OpenFlamingo, we expect the image to be a torch tensor of shape batch_size x num_media x num_frames x channels x height x width. To instantiate an OpenFlamingo model with one of our released weights, initialize the model as above and use the following code.įrom PIL import Image import requests import torch """ Step 1: Load images """ demo_image_one = Image. However, you can continue to use our older checkpoint using the new codebase. Note: as part of our v2 release, we have deprecated a previous LLaMA-based checkpoint. ** 4-shot COCO and VQAv2 performances were calculated over a sample of 5000 test split examples, following the Flamingo paper. ![]() * Xattn interval refers to the -cross_attn_every_n_layers argument. Togethercomputer/RedPajama-INCITE-Instruct-3B-v1 Togethercomputer/RedPajama-INCITE-Base-3B-v1 We have trained the following OpenFlamingo models so far. Tokenizer_path = "anas-awadalla/mpt-1b-redpajama-200b", Lang_encoder_path = "anas-awadalla/mpt-1b-redpajama-200b", From open_flamingo import create_model_and_transforms model, image_processor, tokenizer = create_model_and_transforms(Ĭlip_vision_encoder_pretrained = "openai", ![]()
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