![]() ![]() cat ( vision_x, dim = 0 ) vision_x = vision_x. ![]() In this case batch_size = 1, num_media = 3, num_frames = 1, channels = 3, height = 224, width = 224. raw ) """ 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. from PIL import Image import requests import torch """ Step 1: Load images """ demo_image_one = Image. In particular, let's try few-shot image captioning. ![]() load ( checkpoint_path ), strict = False ) Generating textīelow is an example of generating text conditioned on interleaved images/text. # grab model checkpoint from huggingface hub from huggingface_hub import hf_hub_download import torch checkpoint_path = hf_hub_download ( "openflamingo/OpenFlamingo-3B-vitl-mpt1b", "checkpoint.pt" ) model. To instantiate an OpenFlamingo model with one of our released weights, initialize the model as above and use the following code. 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. from open_flamingo import create_model_and_transforms model, image_processor, tokenizer = create_model_and_transforms ( clip_vision_encoder_path = "ViT-L-14", clip_vision_encoder_pretrained = "openai", lang_encoder_path = "anas-awadalla/mpt-1b-redpajama-200b", tokenizer_path = "anas-awadalla/mpt-1b-redpajama-200b", cross_attn_every_n_layers = 1 ) Released OpenFlamingo models We also support pretrained language models from the transformers package, such as MPT, RedPajama, LLaMA, OPT, GPT-Neo, GPT-J, and Pythia models. We support pretrained vision encoders from the OpenCLIP package, which includes OpenAI's pretrained models. The model architecture is shown below.Ĭredit: Flamingo Usage Initializing an OpenFlamingo model OpenFlamingo combines a pretrained vision encoder and a language model using cross attention layers. The benefit of this approach is that we are able to rapidly adapt to new tasks using in-context learning. For example, OpenFlamingo can be used to generate a caption for an image, or to generate a question given an image and a text passage. Multimodal C4) and can be used to generate text conditioned on interleaved images/text. It is trained on a large multimodal dataset (e.g. OpenFlamingo is a multimodal language model that can be used for a variety of tasks. Or to create a conda environment for running OpenFlamingo, run conda env create -f environment.yml To install the package in an existing environment, run pip install open-flamingo We also welcome contributions! Table of Contents If you have any questions, please feel free to open an issue. In this repository, we provide a PyTorch implementation for training and evaluating OpenFlamingo models. Welcome to our open source implementation of DeepMind's Flamingo! Blog posts: 1, 2 | Paper (coming soon) | Demo ![]()
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