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# gme-Qwen2-VL-2B-Instruct_a14122304306802688376722
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---
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frameworks:
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- Pytorch
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license: Apache License 2.0
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tasks:
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- multi-modal-embedding
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gme-Qwen2-VL-2B-Instruct
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#model-type:
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##如 gpt、phi、llama、chatglm、baichuan 等
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#- gpt
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#domain:
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##如 nlp、cv、audio、multi-modal
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#- nlp
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#language:
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##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
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#- cn
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#metrics:
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##如 CIDEr、Blue、ROUGE 等
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#- CIDEr
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#tags:
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##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
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#- pretrained
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#tools:
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##如 vllm、fastchat、llamacpp、AdaSeq 等
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#- vllm
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language:
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- zh
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- en
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base_model:
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- Qwen/Qwen2-VL-2B-Instruct
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metrics:
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- accuracy
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---
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<p align="center">
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<img src="images/gme_logo.png" alt="GME Logo" style="width: 100%; max-width: 450px;">
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</p>
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<p align="center"><b>GME: General Multimodal Embedding</b></p>
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## GME-Qwen2-VL-2B
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`GME-Qwen2VL` 系列统一的**多模态Embedding模型**基于[Qwen2-VL](https://modelscope.cn/models/Qwen/Qwen2-VL-7B-Instruct) 多模态大型语言模型 (MLLMs)训练。`GME` 模型支持三种类型的输入：**文本**、**图像**和**图像-文本对**，所有这些输入类型都可以生成通用的向量表示，并具有优秀的检索性能。
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**GME模型主要特点**:
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- **统一的多模态表示**：GME 模型可以处理单一模态和组合模态的输入，生成统一的向量表示。这使得各种检索场景（Any2Any 搜索）成为可能，支持如文本检索、从文本检索图像以及图像之间的检索等任务。
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- **高性能**：在我们的通用多模态检索基准 (**UMRB**) 中达到了最先进 (SOTA) 的结果，并在多语言文本评估基准 (**MTEB**) 中表现优异。
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- **动态图像分辨率**：受益于 `Qwen2-VL`的特性，GME模型支持动态分辨率的图像输入。
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- **强大的视觉检索性能**：得益于Qwen2-VL模型系列和训练数据，我们的模型在视觉文档检索任务(例如表格PDF检索)中表现优异。这一能力对于复杂的文档理解场景尤为重要，例如专注于学术论文的多模态检索增强生成 (RAG) 应用。
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**Paper**: [GME: Improving Universal Multimodal Retrieval by Multimodal LLMs](http://arxiv.org/abs/2412.16855)
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## Model List
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| Models | Model Size | Max Seq. Length | Dimension | MTEB-en| MTEB-zh | UMRB |
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|:-----: | :-----: |:-----: |:-----: |:-----: | :-----: | :-----: |
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|[`gme-Qwen2-VL-2B`](https://modelscope.cn/models/iic/gme-Qwen2-VL-2B-Instruct) | 2.21B | 32768 | 1536 | 65.27 | 66.92 | 64.45 |
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|[`gme-Qwen2-VL-7B`](https://modelscope.cn/models/iic/gme-Qwen2-VL-7B-Instruct) | 8.29B | 32768 | 3584 | 67.48 | 69.73 | 67.44 |
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## Usage
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**Use with custom code**
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```python
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# You can find the script gme_inference.py in https://modelscope.cn/models/iic/gme-Qwen2-VL-7B-Instruct/file/view/master?fileName=gme_inference.py
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from gme_inference import GmeQwen2VL
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texts = [
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"What kind of car is this?",
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"The Tesla Cybertruck is a battery electric pickup truck built by Tesla, Inc. since 2023."
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]
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images = [
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'https://en.wikipedia.org/wiki/File:Tesla_Cybertruck_damaged_window.jpg',
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'https://en.wikipedia.org/wiki/File:2024_Tesla_Cybertruck_Foundation_Series,_front_left_(Greenwich).jpg',
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]
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gme = GmeQwen2VL("gme-Qwen2-VL-7B-Instruct")
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# Single-modal embedding
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e_text = gme.get_text_embeddings(texts=texts)
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e_image = gme.get_image_embeddings(images=images)
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print((e_text * e_image).sum(-1))
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## tensor([0.2281, 0.6001], dtype=torch.float16)
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# How to set embedding instruction
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e_query = gme.get_text_embeddings(texts=texts, instruction='Find an image that matches the given text.')
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# If is_query=False, we always use the default instruction.
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e_corpus = gme.get_image_embeddings(images=images, is_query=False)
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print((e_query * e_corpus).sum(-1))
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## tensor([0.2433, 0.7051], dtype=torch.float16)
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# Fused-modal embedding
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e_fused = gme.get_fused_embeddings(texts=texts, images=images)
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print((e_fused[0] * e_fused[1]).sum())
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## tensor(0.6108, dtype=torch.float16)
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```
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## UMRB评测
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| | | Single-modal | | Cross-modal | | | Fused-modal | | | | Avg. |
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|--------------------|------|:------------:|:---------:|:-----------:|:-----------:|:---------:|:-----------:|:----------:|:----------:|:-----------:|:----------:|
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| | | T→T (16) | I→I (1) | T→I (4) | T→VD (10) | I→T (4) | T→IT (2) | IT→T (5) | IT→I (2) | IT→IT (3) | (47) |
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| VISTA | 0.2B | 55.15 | **31.98** | 32.88 | 10.12 | 31.23 | 45.81 | 53.32 | 8.97 | 26.26 | 37.32 |
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| CLIP-SF | 0.4B | 39.75 | 31.42 | 59.05 | 24.09 | 62.95 | 66.41 | 53.32 | 34.9 | 55.65 | 43.66 |
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| One-Peace | 4B | 43.54 | 31.27 | 61.38 | 42.9 | 65.59 | 42.72 | 28.29 | 6.73 | 23.41 | 42.01 |
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| DSE | 4.2B | 48.94 | 27.92 | 40.75 | 78.21 | 52.54 | 49.62 | 35.44 | 8.36 | 40.18 | 50.04 |
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| E5-V | 8.4B | 52.41 | 27.36 | 46.56 | 41.22 | 47.95 | 54.13 | 32.9 | 23.17 | 7.23 | 42.52 |
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| **[GME-Qwen2-VL-2B](https://modelscope.cn/models/iic/gme-Qwen2-VL-2B-Instruct)** | 2.2B | 55.93 | 29.86 | 57.36 | 87.84 | 61.93 | 76.47 | 64.58 | 37.02 | 66.47 | 64.45 |
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| **[GME-Qwen2-VL-7B](https://modelscope.cn/models/iic/gme-Qwen2-VL-7B-Instruct)** | 8.3B | **58.19** | 31.89 | **61.35** | **89.92** | **65.83** | **80.94** | **66.18** | **42.56** | **73.62** | **67.44** |
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The [MTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard) English tab shows the text embeddings performence of our model.
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**更详细的实验结果可以在[论文](http://arxiv.org/abs/2412.16855)中找到**。
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## 限制
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- **单图像输入**：为了获得较好的训练效率，我们将视觉标记的数量限制为1024。由于相关数据的缺乏，我们的模型和评估仅保留单一图像。
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- **单语训练**：我们的模型仅在英语数据上进行训练。尽管`Qwen2-VL`模型是多语言的，但多语言多模态Embedding嵌入的性能不能完全保证
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我们将在未来的版本中扩展到多图像输入、图文交错的数据以及多语言数据。
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## 阿里云API服务
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除了开源的[GME](https://modelscope.cn/models/iic/gme-Qwen2-VL-2B-Instruct/file/edit/master?fileName=README.md)系列模型，GME模型也可以作为商业API服务在阿里云上使用。
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- [多模态向量模型](https://help.aliyun.com/zh/model-studio/developer-reference/multimodal-embedding-api-reference?spm=a2c4g.11186623.0.0.321c1d1cqmoJ5C)：提供 `multimodal-embedding-v1` 模型服务。
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请注意，商业API背后的模型与开源模型并不完全相同。
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## 引用
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如果您发现我们的论文或模型对您有帮助，请考虑引用：
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```
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@misc{zhang2024gme,
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title={GME: Improving Universal Multimodal Retrieval by Multimodal LLMs},
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author={Zhang, Xin and Zhang, Yanzhao and Xie, Wen and Li, Mingxin and Dai, Ziqi and Long, Dingkun and Xie, Pengjun and Zhang, Meishan and Li, Wenjie and Zhang, Min},
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year={2024},
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eprint={2412.16855},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={http://arxiv.org/abs/2412.16855},
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}
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```
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{
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
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"<|endoftext|>": 151643,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644,
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"<|image_pad|>": 151655,
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"<|object_ref_end|>": 151647,
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"<|object_ref_start|>": 151646,
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"<|quad_end|>": 151651,
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"<|quad_start|>": 151650,
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"<|video_pad|>": 151656,
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"<|vision_end|>": 151653,
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"<|vision_pad|>": 151654,
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"<|vision_start|>": 151652
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}
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{
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"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 %}"
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}
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{
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"_name_or_path": "gme-Qwen2-VL-2B-Instruct",
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"architectures": [
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"Qwen2VLForConditionalGeneration"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 1536,
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"image_token_id": 151655,
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"initializer_range": 0.02,
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"intermediate_size": 8960,
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"max_position_embeddings": 32768,
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"max_window_layers": 28,
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"model_type": "qwen2_vl",
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"num_attention_heads": 12,
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"num_hidden_layers": 28,
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"num_key_value_heads": 2,
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"rms_norm_eps": 1e-06,
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"rope_scaling": {
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"mrope_section": [
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16,
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24,
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24
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],
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"type": "mrope"
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},
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"rope_theta": 1000000.0,
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"sliding_window": 32768,
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"tie_word_embeddings": true,
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"torch_dtype": "float32",
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"transformers_version": "4.45.0.dev0",
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"use_cache": true,
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"use_sliding_window": false,
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"video_token_id": 151656,
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"vision_config": {
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"hidden_size": 1536,
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"in_chans": 3,
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"model_type": "qwen2_vl",
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"spatial_patch_size": 14
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},
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"vision_end_token_id": 151653,
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"vision_start_token_id": 151652,
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"vision_token_id": 151654,
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"vocab_size": 151936
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}
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{"framework":"Pytorch","task":"multi-modal-embedding"}
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{
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"bos_token_id": 151643,
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
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"pad_token_id": 151643,
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"repetition_penalty": 1.05,
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"temperature": 0.1,
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"top_k": 1,
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"top_p": 0.001,
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"transformers_version": "4.45.0.dev0"
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}
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{
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"metadata": {
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"total_size": 8835942400
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},
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"weight_map": {
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"model.embed_tokens.weight": "model-00001-of-00003.safetensors",
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"model.layers.0.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
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"model.layers.1.mlp.gate_proj.weight": "model-00001-of-00003.safetensors",
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"model.layers.1.mlp.up_proj.weight": "model-00001-of-00003.safetensors",
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"model.layers.1.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
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"model.layers.1.self_attn.k_proj.bias": "model-00001-of-00003.safetensors",
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"model.layers.1.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
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"model.layers.1.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
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||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151647": {
|
||||||
|
"content": "<|object_ref_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151648": {
|
||||||
|
"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
|
||||||
|
},
|
||||||
|
"151654": {
|
||||||
|
"content": "<|vision_pad|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"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",
|
||||||
|
"model_max_length": 32768,
|
||||||
|
"pad_token": "<|endoftext|>",
|
||||||
|
"padding_side": "left",
|
||||||
|
"split_special_tokens": false,
|
||||||
|
"tokenizer_class": "Qwen2Tokenizer",
|
||||||
|
"unk_token": null
|
||||||
|
}
|
||||||
File diff suppressed because one or more lines are too long
Loading…
Reference in New Issue