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Model: MrLight/dse-qwen2-2b-mrl-v1 Source: Original Platform
2026-07-25 07:33:12 +08:00
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+---
+language:
+- en
+- fr
+license: apache-2.0
+library_name: Tevatron
+tags:
+- vidore
+datasets:
+- Tevatron/docmatix-ir
+- HuggingFaceM4/Docmatix
+- Tevatron/msmarco-passage-aug
+- vidore/colpali_train_set
+- Tevatron/wiki-ss-nq
+base_model:
+- Qwen/Qwen2-VL-2B-Instruct
+pipeline_tag: visual-document-retrieval
+---
+
+
+# DSE-QWen2-2b-MRL-V1
+
+
+DSE-QWen2-2b-MRL-V1 is a bi-encoder model designed to encode document screenshots into dense vectors for document retrieval. 
+The Document Screenshot Embedding ([DSE](https://arxiv.org/abs/2406.11251)) approach captures documents in their original visual format, preserving all information such as text, images, and layout, thus avoiding tedious parsing and potential information loss.
+DSE aims to provide a generalizable embedding model for Text, PDF documents, Webpage, Slides retrieval.
+
+For example, DSE-QWen2-2b-MRL-V1 achieves **85.8** nDCG@5 on [ViDoRE](https://huggingface.co/spaces/vidore/vidore-leaderboard) leaderboard.
+
+
+
+## Note:
+QWen vision encoder may take high GPU memory if the input image is large. Adjust  `'resized_height':680 , 'resized_width':680` (see below) to fit VRAM based on GPU resources.
+
+## How to Use the Model
+
+To support better effectiveness--efficiency trade-off, this checkpoint is trained to support:
+
+1. Flexible representation dimension.
+2. Flexible input image size.
+
+
+### Load the Model and Processor
+
+```python
+import torch
+from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
+from qwen_vl_utils import process_vision_info
+
+min_pixels = 1*28*28
+max_pixels = 2560*28*28
+
+processor = AutoProcessor.from_pretrained("MrLight/dse-qwen2-2b-mrl-v1", min_pixels=min_pixels, max_pixels=max_pixels)
+model = Qwen2VLForConditionalGeneration.from_pretrained('MrLight/dse-qwen2-2b-mrl-v1', attn_implementation="flash_attention_2", torch_dtype=torch.bfloat16).to('cuda:0').eval()
+processor.tokenizer.padding_side = "left"
+model.padding_side = "left"
+
+def get_embedding(last_hidden_state: torch.Tensor, dimension: int) -> torch.Tensor:
+    reps = last_hidden_state[:, -1]
+    reps = torch.nn.functional.normalize(reps[:, :dimension], p=2, dim=-1)
+    return reps
+```
+
+### Encode Text Query
+
+```python
+from PIL import Image
+queries = ["Where can we see Llama?", "What is the LLaMA AI model?"]
+query_messages = []
+for query in queries:
+    message = [
+        {
+            'role': 'user',
+            'content': [
+                {'type': 'image', 'image': Image.new('RGB', (28, 28)), 'resized_height':1 , 'resized_width':1}, # need a dummy image here for an easier process.
+                {'type': 'text', 'text': f'Query: {query}'},
+            ]
+        }
+    ]
+    query_messages.append(message)
+query_texts = [
+    processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True) + "<|endoftext|>"
+    for msg in query_messages
+]
+query_image_inputs, query_video_inputs = process_vision_info(query_messages)
+query_inputs = processor(text=query_texts, images=query_image_inputs, videos=query_video_inputs, padding='longest', return_tensors='pt').to('cuda:0')
+cache_position = torch.arange(0, len(query_texts))
+query_inputs = model.prepare_inputs_for_generation(**query_inputs, cache_position=cache_position, use_cache=False)
+with torch.no_grad():
+  output = model(**query_inputs, return_dict=True, output_hidden_states=True)
+query_embeddings = get_embedding(output.hidden_states[-1], 1536) # adjust dimensionality for efficiency trade-off, e.g. 512
+```
+
+### Encode Document Screenshot
+
+```python
+import requests
+from io import BytesIO
+
+# URLs of the images
+url1 = "https://huggingface.co/Tevatron/dse-phi3-docmatix-v2/resolve/main/animal-llama.png"
+url2 = "https://huggingface.co/Tevatron/dse-phi3-docmatix-v2/resolve/main/meta-llama.png"
+
+# Download and open images
+response1 = requests.get(url1)
+response2 = requests.get(url2)
+
+doc_image1 = Image.open(BytesIO(response1.content))
+doc_image2 = Image.open(BytesIO(response2.content))
+
+doc_images = [doc_image1, doc_image2]
+doc_messages = []
+for doc in doc_images:
+    message = [
+        {
+            'role': 'user',
+            'content': [
+                {'type': 'image', 'image': doc}, #'resized_height':680 , 'resized_width':680} # adjust the image size for efficiency trade-off
+                {'type': 'text', 'text': 'What is shown in this image?'}
+            ]
+        }
+    ]
+    doc_messages.append(message)
+doc_texts = [
+    processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True) + "<|endoftext|>"
+    for msg in doc_messages
+]
+doc_image_inputs, doc_video_inputs = process_vision_info(doc_messages)
+doc_inputs = processor(text=doc_texts, images=doc_image_inputs, videos=doc_video_inputs, padding='longest', return_tensors='pt').to('cuda:0')
+cache_position = torch.arange(0, len(doc_texts))
+doc_inputs = model.prepare_inputs_for_generation(**doc_inputs, cache_position=cache_position, use_cache=False)
+with torch.no_grad():
+    output = model(**doc_inputs, return_dict=True, output_hidden_states=True)
+doc_embeddings = get_embedding(output.hidden_states[-1], 1536) # adjust dimensionality for efficiency trade-off e.g. 512
+
+```
+
+### Compute Similarity
+
+```python
+from torch.nn.functional import cosine_similarity
+num_queries = query_embeddings.size(0)
+num_passages = doc_embeddings.size(0)
+
+for i in range(num_queries):
+    query_embedding = query_embeddings[i].unsqueeze(0)
+    similarities = cosine_similarity(query_embedding, doc_embeddings)
+    print(f"Similarities for Query {i+1}: {similarities.cpu().float().numpy()}")
+```
+
+### Encode Document Text
+This DSE checkpoint is warm-up with `Tevatron/msmarco-passage-aug`, thus the model can also effectively encode document as text input.
+```python
+doc_texts = [
+  "The llama (/ˈlɑːmə/; Spanish pronunciation: [ˈʎama] or [ˈʝama]) (Lama glama) is a domesticated South American camelid, widely used as a meat and pack animal by Andean cultures since the pre-Columbian era.",
+  "Llama (acronym for Large Language Model Meta AI, and formerly stylized as LLaMA) is a family of autoregressive large language models (LLMs) released by Meta AI starting in February 2023.[2][3] The latest version is Llama 3.1, released in July 2024.[4]"
+]
+doc_messages = []
+for doc in doc_texts:
+    message = [
+        {
+            'role': 'user',
+            'content': [
+                {'type': 'image', 'image': Image.new('RGB', (28, 28)), 'resized_height':1 , 'resized_width':1}, # need a dummy image here for an easier process.
+                {'type': 'text', 'text': f'Document: {doc}'}
+            ]
+        }
+    ]
+    doc_messages.append(message)
+doc_texts = [
+    processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True) + "<|endoftext|>"
+    for msg in doc_messages
+]
+doc_image_inputs, doc_video_inputs = process_vision_info(doc_messages)
+doc_inputs = processor(text=doc_texts, images=doc_image_inputs, videos=doc_video_inputs, padding='longest', return_tensors='pt').to('cuda:0')
+cache_position = torch.arange(0, len(doc_texts))
+doc_inputs = model.prepare_inputs_for_generation(**doc_inputs, cache_position=cache_position, use_cache=False)
+with torch.no_grad():
+    output = model(**doc_inputs, return_dict=True, output_hidden_states=True)
+doc_embeddings = get_embedding(output.hidden_states[-1], 1536) # adjust dimensionality for efficiency trade-off e.g. 512
+
+for i in range(num_queries):
+    query_embedding = query_embeddings[i].unsqueeze(0)
+    similarities = cosine_similarity(query_embedding, doc_embeddings)
+    print(f"Similarities for Query {i+1}: {similarities.cpu().float().numpy()}")
+```
+
+### Citation
+If you find this checkpoint is helpful, please consider citing QWen2, Docmatix, ViDoRe, and our DSE work.