LinWeizheDragon
/

PreFLMR_ViT-G

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 ---
 library_name: transformers
-tags: []
 ---
 # Model Card for Model ID
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ## Uses
@@ -41,159 +39,66 @@ This is the model card of a 🤗 transformers model that has been pushed on the
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 ## How to Get Started with the Model
 Use the code below to get started with the model.
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 ---
 library_name: transformers
+tags: [KBVQA, Multimodal, Retrieval, Knowledge Retrieval, RAG, FLMR, PreFLMR, ColBERT]
 ---
 # Model Card for Model ID
 <!-- Provide a longer summary of what this model is. -->
+This is the PreFLMR model
+- **Model type:** PreFLMR is an open-source model for general knowledge retrieval. It is a transformer-based model that uses a combination of text and image inputs to retrieve relevant documents from a large corpus.
+- **Language(s) (NLP):** English
 - **License:** [More Information Needed]
+### Model Sources
 <!-- Provide the basic links for the model. -->
+- **Repository:** https://github.com/LinWeizheDragon/FLMR
+- **Paper:** https://arxiv.org/abs/2402.08327
+- **Demo:** http://region-3.seetacloud.com:38703/
+- **Blog Post:** https://www.jinghong-chen.net/preflmr-sota-open-sourced-multi/
+- **Project Page:** https://preflmr.github.io/
 ## Uses
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+This model can be used directly to retrieve documents from a large corpus using a combination of text and image input queries. The retrieval useage can be found in the [official implementation](https://github.com/LinWeizheDragon/FLMR).
+### Downstream Use
 <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+This model can be used combined with language models to create a retrieval-augmented language model. The useage for Knowledge-based VQA can be found in https://github.com/linweizhedragon/retrieval-augmented-visual-question-answering
 ## How to Get Started with the Model
 Use the code below to get started with the model.
+```python
+from transformers import AutoConfig, AutoModel, AutoImageProcessor, AutoTokenizer
+import torch
+checkpoint_path = "LinWeizheDragon/PreFLMR_ViT-L"
+image_processor_name = "openai/clip-vit-large-patch14"
+query_tokenizer = AutoTokenizer.from_pretrained(checkpoint_path, subfolder="query_tokenizer", trust_remote_code=True)
+context_tokenizer = AutoTokenizer.from_pretrained(checkpoint_path, subfolder="context_tokenizer", trust_remote_code=True)
+model = AutoModel.from_pretrained(checkpoint_path,
+                                query_tokenizer=query_tokenizer,
+                                context_tokenizer=context_tokenizer,
+                                trust_remote_code=True,
+                                )
+image_processor = AutoImageProcessor.from_pretrained(image_processor_name)
+Q_encoding = query_tokenizer(["Using the provided image, obtain documents that address the subsequent question: What is the capital of France?", "Extract documents linked to the question provided in conjunction with the image: What is the capital of China?"])
+D_encoding = context_tokenizer(["Paris is the capital of France.", "Beijing is the capital of China.",
+                            "Paris is the capital of France.", "Beijing is the capital of China."])
+Q_pixel_values = torch.zeros(2, 3, 224, 224)
+inputs = dict(
+    query_input_ids=Q_encoding['input_ids'],
+    query_attention_mask=Q_encoding['attention_mask'],
+    query_pixel_values=Q_pixel_values,
+    context_input_ids=D_encoding['input_ids'],
+    context_attention_mask=D_encoding['attention_mask'],
+    use_in_batch_negatives=True,
+)
+res = model.forward(**inputs)
+print(res)
+```
+## Training datasets
+The model is trained on a combination of eight image-text datasets and a text-only dataset.
+## Citation
 **BibTeX:**
+```
+@article{Lin_Mei_Chen_Byrne_2024,
+        title={PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers},
+        url={http://arxiv.org/abs/2402.08327},
+        number={arXiv:2402.08327},
+        publisher={arXiv},
+        author={Lin, Weizhe and Mei, Jingbiao and Chen, Jinghong and Byrne, Bill},
+        year={2024}}
+```