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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

.ipynb_checkpoints/README-checkpoint.md

@@ -0,0 +1,213 @@
+---
+license: apache-2.0
+language:
+- en
+- it
+- fr
+- de
+- es
+base_model:
+- MrLight/dse-qwen2-2b-mrl-v1
+tags:
+- transformers
+- Qwen2-VL
+---
+
+# vdr-2b-multi-v1
+
+![](cover.png)
+
+vdr-2b-multi-v1 is a multilingual model designed for visual document retrieval across multiple languages and domains. This model is designed to encode 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.
+
+- **Low VRAM and Faster Inference**: english model achieves better results on synthetic vidore benchmarks with just 30% of the base model image resolution. This results in 3x faster inference and much lower VRAM usage.
+
+- **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
+
+**Initialize model and processor**
+
+```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',
+    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)
+```
+
+# 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** |

README.md

@@ -1,3 +1,213 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+- it
+- fr
+- de
+- es
+base_model:
+- MrLight/dse-qwen2-2b-mrl-v1
+tags:
+- transformers
+- Qwen2-VL
+---
+
+# vdr-2b-multi-v1
+
+![](cover.png)
+
+vdr-2b-multi-v1 is a multilingual model designed for visual document retrieval across multiple languages and domains. This model is designed to encode 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.
+
+- **Low VRAM and Faster Inference**: english model achieves better results on synthetic vidore benchmarks with just 30% of the base model image resolution. This results in 3x faster inference and much lower VRAM usage.
+
+- **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
+
+**Initialize model and processor**
+
+```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',
+    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)
+```
+
+# 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** |

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
+}

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 %}"
+}

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
+}

generation_config.json

@@ -0,0 +1,14 @@
+{
+  "attn_implementation": "flash_attention_2",
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.01,
+  "top_k": 1,
+  "top_p": 0.001,
+  "transformers_version": "4.47.1"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f44d6a049be8cfa671c99e936d8cdcd6878f5db3ccf998aca6423abdd31604cd
+size 4418050848

preprocessor_config.json

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+{
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_processor_type": "Qwen2VLImageProcessor",
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "max_pixels": 602112,
+  "merge_size": 2,
+  "min_pixels": 784,
+  "patch_size": 14,
+  "processor_class": "Qwen2VLProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "max_pixels": 12845056,
+    "min_pixels": 3136
+  },
+  "temporal_patch_size": 2
+}

special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:091aa7594dc2fcfbfa06b9e3c22a5f0562ac14f30375c13af7309407a0e67b8a
+size 11420371

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "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 %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "max_pixels": 602112,
+  "min_pixels": 784,
+  "model_max_length": 32768,
+  "pad_token": "<|endoftext|>",
+  "padding_side": "left",
+  "processor_class": "Qwen2VLProcessor",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}