royokong
/

e5-v

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+# [E5-V: Universal Embeddings with Multimodal Large Language Models](https://arxiv.org/abs/2407.12580)
+## Overview
+We propose a framework, called E5-V, to adpat MLLMs for achieving multimodal embeddings. E5-V effectively bridges the modality gap between different types of inputs, demonstrating strong performance in multimodal embeddings even without fine-tuning. We also propose a single modality training approach for E5-V, where the model is trained exclusively on text pairs, demonstrating better performance than multimodal training.
+More details can be found in https://github.com/kongds/E5-V
+## Example
+``` python
+import torch
+import torch.nn.functional as F
+import requests
+from PIL import Image
+from transformers import AutoTokenizer
+from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration
+llama3_template = '<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n \n'
+processor = LlavaNextProcessor.from_pretrained('royokong/e5-v')
+model = LlavaNextForConditionalGeneration.from_pretrained('royokong/e5-v', torch_dtype=torch.float16).cuda()
+img_prompt = llama3_template.format('<image>\nSummary above image in one word: ')
+text_prompt = llama3_template.format('<sent>\nSummary above sentence in one word: ')
+urls = ['https://upload.wikimedia.org/wikipedia/commons/thumb/4/47/American_Eskimo_Dog.jpg/360px-American_Eskimo_Dog.jpg',
+        'https://upload.wikimedia.org/wikipedia/commons/thumb/b/b6/Felis_catus-cat_on_snow.jpg/179px-Felis_catus-cat_on_snow.jpg']
+images = [Image.open(requests.get(url, stream=True).raw) for url in urls]
+texts = ['A dog sitting in the grass.',
+         'A cat standing in the snow.']
+text_inputs = processor([text_prompt.replace('<sent>', text) for text in texts], return_tensors="pt", padding=True).to('cuda')
+img_inputs = processor([img_prompt]*len(images), images, return_tensors="pt", padding=True).to('cuda')
+with torch.no_grad():
+    text_embs = model(**text_inputs, output_hidden_states=True, return_dict=True).hidden_states[-1][:, -1, :]
+    img_embs = model(**img_inputs, output_hidden_states=True, return_dict=True).hidden_states[-1][:, -1, :]
+    text_embs = F.normalize(text_embs, dim=-1)
+    img_embs = F.normalize(img_embs, dim=-1)
+print(text_embs @ img_embs.t())
+```