From a3e7a7b7e6bb428e30a3a0954d910d94f4d34a12 Mon Sep 17 00:00:00 2001
From: xxl <505279206@qq.com>
Date: Tue, 21 Jan 2025 00:18:16 +0800
Subject: [PATCH] first commit
---
README.md | 283 +-
added_tokens.json | 16 +
chat_template.json | 3 +
config.json | 48 +
config_sentence_transformers.json | 13 +
cover.png | Bin 0 -> 210288 bytes
custom_st.py | 314 +
generation_config.json | 14 +
merges.txt | 151388 +++++++++++++++++++++++++++
model.safetensors | 3 +
modules.json | 18 +
ndcgtop.png | Bin 0 -> 47427 bytes
preprocessor_config.json | 29 +
special_tokens_map.json | 31 +
tokenizer.json | 3 +
tokenizer_config.json | 147 +
vocab.json | 1 +
17 files changed, 152310 insertions(+), 1 deletion(-)
create mode 100644 added_tokens.json
create mode 100644 chat_template.json
create mode 100644 config.json
create mode 100644 config_sentence_transformers.json
create mode 100644 cover.png
create mode 100644 custom_st.py
create mode 100644 generation_config.json
create mode 100644 merges.txt
create mode 100644 model.safetensors
create mode 100644 modules.json
create mode 100644 ndcgtop.png
create mode 100644 preprocessor_config.json
create mode 100644 special_tokens_map.json
create mode 100644 tokenizer.json
create mode 100644 tokenizer_config.json
create mode 100644 vocab.json
diff --git a/README.md b/README.md
index 5c49440..efe82c6 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,284 @@
+---
+license: apache-2.0
+language:
+- en
+- it
+- fr
+- de
+- es
+base_model:
+- MrLight/dse-qwen2-2b-mrl-v1
+tags:
+- transformers
+- sentence-transformers
+- Qwen2-VL
+datasets:
+- llamaindex/vdr-multilingual-train
+---
+
# vdr-2b-multi-v1
-vdr-2b-multi-v1
\ No newline at end of file
+
+
+vdr-2b-multi-v1 is a multilingual embedding model designed for visual document retrieval across multiple languages and domains. It encodes document page screenshots into dense single-vector representations, this will effectively allow to search and query visually rich multilingual documents without the need for any OCR, data extraction pipelines, chunking...
+
+
+- **Trained on 🇮🇹 Italian, 🇪🇸 Spanish, 🇬🇧 English, 🇫🇷 French and 🇩🇪 German:** together they form a new large, open-source, multilingual training dataset of 500k high-quality samples.
+
+- **Cross-lingual Retrieval**: substantially better on real-world scenarios. For example, this allows for searching german documents with italian queries.
+
+- **Matryoshka Representation Learning**: You can reduce the vectors size 3x and still keep 98% of the embeddings quality.
+
+# Usage
+
+The model uses bf16 tensors and allocates ~4.4GB of VRAM when loaded. You can easily run inference and generate embeddings using 768 image patches and a batch size of 16 even on a cheap NVIDIA T4 GPU. This table reports the memory footprint (GB) under conditions of different batch sizes with HuggingFace Transformers and maximum 768 image patches.
+
+| Batch Size | GPU Memory (GB) |
+|------------|-----------------|
+| 4 | 6.9 |
+| 8 | 8.8 |
+| 16 | 11.5 |
+| 32 | 19.7 |
+
+You can generate embeddings with this model in many different ways:
+
+
+
+via LlamaIndex
+
+
+```bash
+pip install -U llama-index-embeddings-huggingface
+```
+
+```python
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+
+model = HuggingFaceEmbedding(
+ model_name="llamaindex/vdr-2b-multi-v1",
+ device="cpu", # "mps" for mac, "cuda" for nvidia GPUs
+ trust_remote_code=True,
+)
+
+image_embedding = model.get_image_embedding("image.png")
+query_embedding = model.get_query_embedding("some query")
+```
+
+
+
+
+
+via HuggingFace Transformers
+
+
+```python
+from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
+from PIL import Image
+import torch
+import math
+
+# more pixels -> better embeddings -> more VRAM -> slower inference
+# From my experience, 768 image patches is the right spot for compute efficient embeddings.
+max_pixels = 768 * 28 * 28
+min_pixels = 1 * 28 * 28
+
+# Load the embedding model and processor
+model = Qwen2VLForConditionalGeneration.from_pretrained(
+ 'llamaindex/vdr-2b-multi-v1',
+ # These are the recommended kwargs for the model, but change them as needed
+ attn_implementation="flash_attention_2",
+ torch_dtype=torch.bfloat16,
+ device_map="cuda:0"
+).eval()
+
+processor = AutoProcessor.from_pretrained(
+ 'llamaindex/vdr-2b-multi-v1',
+ min_pixels=min_pixels,
+ max_pixels=max_pixels
+)
+
+model.padding_side = "left"
+processor.tokenizer.padding_side = "left"
+
+document_prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>What is shown in this image?<|im_end|>\n<|endoftext|>"
+
+query_prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>Query: %s<|im_end|>\n<|endoftext|>"
+```
+
+**Encode queries**
+
+```python
+def encode_queries(queries: list[str], dimension: int) -> torch.Tensor:
+ """
+ Encode a list of queries into a tensor of embeddings.
+
+ Args:
+ queries: A list of strings, each representing a query.
+ dimension: The desired dimension of the output embeddings.
+
+ Returns:
+ A tensor of shape (num_queries, dimension) containing the encoded queries.
+ """
+
+ dummy_image = Image.new('RGB', (56, 56))
+ inputs = processor(
+ text=[query_prompt % x for x in queries],
+ images=[dummy_image for _ in queries],
+ videos=None,
+ padding='longest',
+ return_tensors='pt'
+ ).to('cuda:0')
+
+ cache_position = torch.arange(0, len(queries))
+ inputs = model.prepare_inputs_for_generation(
+ **inputs, cache_position=cache_position, use_cache=False)
+
+ with torch.no_grad():
+ output = self.model(
+ **inputs,
+ return_dict=True,
+ output_hidden_states=True
+ )
+
+ embeddings = output.hidden_states[-1][:, -1]
+ return torch.nn.functional.normalize(embeddings[:, :dimension], p=2, dim=-1)
+```
+
+**Encode documents**
+
+```python
+def round_by_factor(number: float, factor: int) -> int:
+ return round(number / factor) * factor
+
+def ceil_by_factor(number: float, factor: int) -> int:
+ return math.ceil(number / factor) * factor
+
+def floor_by_factor(number: float, factor: int) -> int:
+ return math.floor(number / factor) * factor
+
+def smart_resize(height: int, width: int) -> tuple[int, int]:
+ h_bar = max(28, round_by_factor(height, 28))
+ w_bar = max(28, round_by_factor(width, 28))
+ if h_bar * w_bar > max_pixels:
+ beta = math.sqrt((height * width) / max_pixels)
+ h_bar = floor_by_factor(height / beta, 28)
+ w_bar = floor_by_factor(width / beta, 28)
+ elif h_bar * w_bar < min_pixels:
+ beta = math.sqrt(min_pixels / (height * width))
+ h_bar = ceil_by_factor(height * beta, 28)
+ w_bar = ceil_by_factor(width * beta, 28)
+ return w_bar, h_bar
+
+def resize(image: Image.Image):
+ new_size = smart_resize(image.height, image.width)
+ return image.resize(new_size)
+
+def encode_documents(documents: list[Image.Image], dimension: int):
+ """
+ Encode a list of images into a tensor of embeddings.
+
+ Args:
+ documents: A list of PIL Image objects.
+ dimension: The desired dimension of the output embeddings.
+
+ Returns:
+ A tensor of shape (num_documents, dimension) containing the encoded images.
+ """
+
+ inputs = processor(
+ text=[document_prompt] * len(documents),
+ images=[resize(x) for x in documents],
+ videos=None,
+ padding='longest',
+ return_tensors='pt'
+ ).to('cuda:0')
+
+ cache_position = torch.arange(0, len(queries))
+ inputs = model.prepare_inputs_for_generation(
+ **inputs, cache_position=cache_position, use_cache=False)
+
+ with torch.no_grad():
+ output = self.model(
+ **inputs,
+ return_dict=True,
+ output_hidden_states=True
+ )
+
+ embeddings = output.hidden_states[-1][:, -1]
+ return torch.nn.functional.normalize(embeddings[:, :dimension], p=2, dim=-1)
+```
+
+
+
+
+
+
+via SentenceTransformers
+
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer(
+ model_name_or_path="llamaindex/vdr-2b-multi-v1",
+ device="cuda",
+ trust_remote_code=True,
+ # These are the recommended kwargs for the model, but change them as needed if you don't have CUDA
+ model_kwargs={
+ "torch_dtype": torch.bfloat16,
+ "device_map": "cuda:0",
+ "attn_implementation": "flash_attention_2"
+ },
+)
+
+embeddings = model.encode("image.png")
+```
+
+
+
+# Training
+
+The model is based on [MrLight/dse-qwen2-2b-mrl-v1](https://huggingface.co/MrLight/dse-qwen2-2b-mrl-v1) and it was trained on the new [vdr-multilingual-train](https://huggingface.co/datasets/llamaindex/vdr-multilingual-train) dataset that consinsists of 500k high quality, multilingual query image pairs. It was trained for 1 epoch using the [DSE approach](https://arxiv.org/abs/2406.11251), with a batch size of 128 and hard-mined negatives.
+
+# Results
+
+
+
+The model has been evaluated on the Vidore benchmark and on custom-built evaluation sets that allow testing its multilingual capabilities on text-only, visual-only and mixed page screenshots. The evaluation dataset is publicly available [here on HuggingFace](https://huggingface.co/datasets/llamaindex/vdr-multilingual-test).
+
+All evaluations are performed by calculating **NDCG@5** scores using **1536 dimensions** vectors and an image resolution that can be represented with **maximum 768 tokens**.
+
+| | Avg | Italian (text) | Italian (visual) | Italian (mix) |
+|---------------------|----------|----------------|------------------|---------------|
+| dse-qwen2-2b-mrl-v1 | 95.1 | 95.1 | 94 | 96.2 |
+| vdr-2b-multi-v1 | **97.0** | **96.4** | **96.3** | **98.4** |
+| | **+2%** | | | |
+
+| | Avg | French (text) | French (visual) | French (mix) |
+|---------------------|-----------|---------------|-----------------|--------------|
+| dse-qwen2-2b-mrl-v1 | 93.5 | 94.7 | 90.8 | 95.1 |
+| vdr-2b-multi-v1 | **95.6** | **95.6** | **93.3** | **97.9** |
+| | **+2.2%** | | | |
+
+| | Avg | Spanish (text) | Spanish (visual) | Spanish (mix) |
+|---------------------|-----------|----------------|------------------|---------------|
+| dse-qwen2-2b-mrl-v1 | 96.7 | 97.2 | 94.7 | 98.2 |
+| vdr-2b-multi-v1 | **98.1** | **98.3** | **96.9** | **99.1** |
+| | **+1.4%** | | | |
+
+| | Avg | German (text) | German (visual) | German (mix) |
+|---------------------|-----------|---------------|-----------------|--------------|
+| dse-qwen2-2b-mrl-v1 | 93.0 | 93.4 | 90 | 95.5 |
+| vdr-2b-multi-v1 | **96.2** | **94.8** | **95.7** | **98.1** |
+| | **+3.4%** | | | |
+
+| | Avg | English (text) | English (visual) | English (mix) |
+|---------------------|-----------|----------------|------------------|---------------|
+| dse-qwen2-2b-mrl-v1 | 98.0 | **98.3** | 98.5 | 97.1 |
+| vdr-2b-multi-v1 | **98.1** | 97.9 | **99.1** | **97.3** |
+| | **+0.1%** | | | |
+
+| | **Avg** | **shiftproject** | **government** | **healthcare** | **energy** | **ai** | **docvqa** | **arxivqa** | **tatdqa** | **infovqa** | **tabfquad** |
+|--------------------:|---------:|-----------------:|---------------:|---------------:|-----------:|-----------:|-----------:|------------:|-----------:|------------:|-------------:|
+| dse-qwen2-2b-mrl-v1 | 83.6 | 79.8 | **95.7** | **96.9** | **92** | 98.2 | 56.3 | **85.2** | **53.9** | **87.5** | 90.3 |
+| vdr-2b-multi-v1 | **84.0** | **82.4** | 95.5 | 96.5 | 91.2 | **98.5** | **58.5** | 84.7 | 53.6 | 87.1 | **92.2** |
\ No newline at end of file
diff --git a/added_tokens.json b/added_tokens.json
new file mode 100644
index 0000000..caa8130
--- /dev/null
+++ b/added_tokens.json
@@ -0,0 +1,16 @@
+{
+ "<|box_end|>": 151649,
+ "<|box_start|>": 151648,
+ "<|endoftext|>": 151643,
+ "<|im_end|>": 151645,
+ "<|im_start|>": 151644,
+ "<|image_pad|>": 151655,
+ "<|object_ref_end|>": 151647,
+ "<|object_ref_start|>": 151646,
+ "<|quad_end|>": 151651,
+ "<|quad_start|>": 151650,
+ "<|video_pad|>": 151656,
+ "<|vision_end|>": 151653,
+ "<|vision_pad|>": 151654,
+ "<|vision_start|>": 151652
+}
diff --git a/chat_template.json b/chat_template.json
new file mode 100644
index 0000000..13303be
--- /dev/null
+++ b/chat_template.json
@@ -0,0 +1,3 @@
+{
+ "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
+}
diff --git a/config.json b/config.json
new file mode 100644
index 0000000..dde40f3
--- /dev/null
+++ b/config.json
@@ -0,0 +1,48 @@
+{
+ "_name_or_path": "MrLight/dse-qwen2-2b-mrl-v1",
+ "architectures": [
+ "Qwen2VLForConditionalGeneration"
+ ],
+ "attention_dropout": 0.0,
+ "bos_token_id": 151643,
+ "eos_token_id": 151645,
+ "hidden_act": "silu",
+ "hidden_size": 1536,
+ "image_token_id": 151655,
+ "initializer_range": 0.02,
+ "intermediate_size": 8960,
+ "max_position_embeddings": 32768,
+ "max_window_layers": 28,
+ "model_type": "qwen2_vl",
+ "num_attention_heads": 12,
+ "num_hidden_layers": 28,
+ "num_key_value_heads": 2,
+ "rms_norm_eps": 1e-06,
+ "rope_scaling": {
+ "mrope_section": [
+ 16,
+ 24,
+ 24
+ ],
+ "rope_type": "default",
+ "type": "default"
+ },
+ "rope_theta": 1000000.0,
+ "sliding_window": 32768,
+ "tie_word_embeddings": true,
+ "torch_dtype": "bfloat16",
+ "transformers_version": "4.47.1",
+ "use_cache": true,
+ "use_sliding_window": false,
+ "video_token_id": 151656,
+ "vision_config": {
+ "hidden_size": 1536,
+ "in_chans": 3,
+ "model_type": "qwen2_vl",
+ "spatial_patch_size": 14
+ },
+ "vision_end_token_id": 151653,
+ "vision_start_token_id": 151652,
+ "vision_token_id": 151654,
+ "vocab_size": 151936
+}
diff --git a/config_sentence_transformers.json b/config_sentence_transformers.json
new file mode 100644
index 0000000..df793ff
--- /dev/null
+++ b/config_sentence_transformers.json
@@ -0,0 +1,13 @@
+{
+ "__version__": {
+ "sentence_transformers": "3.3.0",
+ "transformers": "4.46.2",
+ "pytorch": "2.2.2"
+ },
+ "prompts":{
+ "image": "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>What is shown in this image?<|im_end|>\n<|endoftext|>",
+ "query": "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>Query: %s<|im_end|>\n<|endoftext|>"
+ },
+ "default_prompt_name": null,
+ "similarity_fn_name": "cosine"
+ }
\ No newline at end of file
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