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Model: longvideotool/LongVT-RL Source: Original Platform
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---
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base_model:
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- Qwen/Qwen2.5-VL-7B-Instruct
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datasets:
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- longvideotool/LongVT-Parquet
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license: apache-2.0
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library_name: transformers
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pipeline_tag: video-text-to-text
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---
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# LongVT: Incentivizing “Thinking with Long Videos” via Native Tool Calling
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<div align="center">
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[](https://huggingface.co/collections/lmms-lab/longvt)
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[](https://arxiv.org/abs/2511.20785)
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[](https://evolvinglmms-lab.github.io/LongVT/)
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[](https://github.com/EvolvingLMMs-Lab/LongVT)
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</div>
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## Overview
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Large multimodal models (LMMs) have shown great potential for video reasoning with textual Chain-of-Thought.
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However, they remain vulnerable to hallucination, especially when processing long-form videos where evidence is sparse and temporally dispersed.
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Inspired by how humans comprehend long videos-by first skimming globally and then examining relevant clips for details-we introduce **LongVT**, an end-to-end agentic framework that enables ``Thinking with **Long** **V**ideos'' via interleaved Multimodal Chain-of-**T**ool-Thought.
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Specifically, we exploit LMMs' inherent temporal grounding ability as a native video cropping tool to zoom in on a specific video clip and resample finer-grained video frames.
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This global-to-local reasoning loop continues until answers are grounded in retrieved visual evidence.
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Given the scarcity of fine-grained question-answering (QA) data for the long video reasoning task, we curate and will release a data suite named **VideoSIAH** to facilitate both training and evaluation.
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Specifically, our training dataset consists of 247.9K samples for tool-integrated cold-start supervised fine-tuning, 1.6K samples for agentic reinforcement learning, and 15.4K samples for agentic reinforcement fine-tuning, respectively.
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Our evaluation benchmark consists of 1,280 QA pairs that are carefully curated through a semi-automatic data pipeline with human-in-the-loop validation.
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With a meticulously designed three-stage training strategy and extensive empirical validation, LongVT consistently outperforms existing strong baselines across four challenging long-video understanding and reasoning benchmarks.
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## Model Card
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The model is the RL version of the LongVT and was trained on https://huggingface.co/datasets/longvideotool/LongVT-Parquet.
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## Usage & Evaluation
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For detailed instructions on inference and evaluation, please refer to our [GitHub repository](https://github.com/EvolvingLMMs-Lab/LongVT). We recommend using the scripts and environment provided there to reproduce our results.
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## Evaluation Results
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| Model | Reasoning Prompt | Tool Calling | VideoMME<br>(≈1018s) | VideoMMMU<br>(subtitle) | VideoMMMU<br>(adaptation) | VideoMMMU<br>(comprehension) | LVBench<br>(≈4101s) | VideoSIAH-Eval<br>(≈1688s) | Average Score |
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| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
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| **Proprietary LMMs** | | | | | | | | | |
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| GPT-4o | ✗ | ✗ | 77.2<sup>†</sup> | 66.0<sup>†</sup> | 62.0<sup>†</sup> | 55.7<sup>†</sup> | 30.8<sup>†</sup> | 17.4 | 51.5 |
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| Gemini 1.5 Pro | ✗ | ✗ | 81.3<sup>†</sup> | 59.0<sup>†</sup> | 53.3<sup>†</sup> | 49.3<sup>†</sup> | 33.1<sup>†</sup> | - | 55.2 |
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| **Open-Source (Sparse)** | | | | | | | | | |
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| Qwen2.5-VL-7B | ✗ | ✗ | <u>62.6</u> | <u>37.3</u> | 28.0 | 36.7 | 30.7 | <u>28.1</u> | 37.2 |
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| Video-R1-7B | ✓ | ✗ | 61.0 | 36.3 | 40.7 | 52.3 | 37.2 | 27.9 | <u>42.6</u> |
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| VideoRFT-7B | ✓ | ✗ | 60.9 | 36.7 | 42.0 | <u>53.0</u> | 34.7 | 26.5 | 42.3 |
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| Video-Thinker-7B | ✓ | ✗ | 61.0 | 34.3 | <u>44.7</u> | <u>53.0</u> | **52.2** | 10.4 | <u>42.6</u> |
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| LongVT-7B-SFT (Ours) | ✓ | ✓ | 12.5 | **37.7** | **46.0** | **58.3** | 36.0 | 26.8 | 36.2 |
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| **LongVT-7B-RL (Ours)** | ✓ | ✓ | **66.1** | 32.7 | <u>44.7</u> | 50.0 | <u>37.8</u> | **31.0** | **43.7** |
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| **Open-Source (Dense)** | | | | | | | | | |
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| Qwen2.5-VL-7B | ✗ | ✗ | 64.3 | 35.7 | **44.3** | **56.7** | 40.9 | 33.8 | 46.0 |
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| Video-R1-7B | ✓ | ✗ | 60.5 | <u>37.3</u> | 38.7 | 46.3 | 40.1 | 33.1 | 42.7 |
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| VideoRFT-7B | ✓ | ✗ | 49.2 | **37.7** | 40.7 | 48.7 | 18.7 | 26.9 | 37.0 |
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| Video-Thinker-7B | ✓ | ✗ | 60.8 | **37.7** | 42.7 | 55.3 | **54.3** | 6.6 | 42.9 |
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| LongVT-7B-SFT (Ours) | ✓ | ✓ | 64.9 | 32.3 | 42.0 | 49.7 | 41.1 | 34.8 | 44.1 |
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| LongVT-7B-RL (Ours) | ✓ | ✓ | <u>66.1</u> | **37.7** | 42.3 | <u>56.3</u> | <u>41.4</u> | <u>35.9</u> | <u>46.6</u> |
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| **LongVT-7B-RFT (Ours)** | ✓ | ✓ | **67.0** | 35.7 | <u>43.7</u> | **56.7** | 41.3 | **42.0** | **47.7** |
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> **Performance Comparison with Existing Video-Centric LMMs across Various Long Video Understanding and Reasoning Benchmarks.** The best and second-best result among open-source models in each column is marked in **bold** and <u>underlined</u>, respectively. The numbers with "≈" denote the average video duration of each benchmark. <sup>†</sup> indicates results sourced from official reports. **Reasoning Prompt** indicates whether a standard reasoning-style prompt (✓) or a direct question-answering prompt (✗) is applied; **Tool Calling** denotes whether native tool calling is enabled (✓) or disabled (✗) in the prompt.
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## Citation
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If you find LongVT useful for your research and applications, please cite using this BibTeX:
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```bibtex
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@misc{yang2025longvtincentivizingthinkinglong,
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title={LongVT: Incentivizing "Thinking with Long Videos" via Native Tool Calling},
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author={Zuhao Yang and Sudong Wang and Kaichen Zhang and Keming Wu and Sicong Leng and Yifan Zhang and Bo Li and Chengwei Qin and Shijian Lu and Xingxuan Li and Lidong Bing},
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year={2025},
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eprint={2511.20785},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2511.20785},
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}
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```
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Check out this paper: https://arxiv.org/abs/2511.20785
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## Acknowledgements
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We gratefully acknowledge the following open-source projects that made this work possible:
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- [**lmms-eval**](https://github.com/EvolvingLMMs-Lab/lmms-eval) for providing the comprehensive evaluation framework for large multimodal models.
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- [**lmms-engine**](https://github.com/EvolvingLMMs-Lab/lmms-engine) for the SFT training infrastructure and tools.
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- [**verl**](https://github.com/volcengine/verl) for the reinforcement learning training framework.
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We thank the developers and contributors of these projects for their excellent work and for making their code publicly available.
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"</tool_call>": 151658,
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"<|endoftext|>": 151643,
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{
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"chat_template": "{# Track number of images and videos #}{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{# If tools are present, add system tool declaration block #}{% if tools %}<|im_start|>system\n{% if messages[0]['role'] == 'system' %}{{ messages[0]['content'] if messages[0]['content'] is string else (messages[0]['content'] | selectattr('type','equalto','text') | map(attribute='text') | join('')) }}{% else %}You are a helpful assistant.\n{% endif %}\n# Tools\nYou may call one or more functions to assist with the user query.\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>{% for tool in tools %}{{ tool | tojson }}{% endfor %}</tools>\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>{\"name\": <function-name>, \"arguments\": <args-json-object>}</tool_call><|im_end|>{% endif %}{# Process message list #}{% for message in messages %}{% set is_tool_call = message.role == 'assistant' and message.tool_calls %}{% set is_media_message = message['content'] is not string %}{% if loop.first and message['role'] != 'system' and not tools %}<|im_start|>system\nYou are a helpful assistant.\n<|im_end|>{% elif loop.first and message['role'] == 'system' and not tools %}<|im_start|>system\n{{ message['content'] if message['content'] is string else (message['content'] | selectattr('type','equalto','text') | map(attribute='text') | join('')) }}\n<|im_end|>{% endif %}{% if message.role in ['user','system'] and (message.role != 'system' or not loop.first) or (message.role == 'assistant' and not is_tool_call) %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>{% 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|>{% endif %}{% elif message.role == 'assistant' and is_tool_call %}<|im_start|>assistant\n{% if message.content %}{{ message.content }}{% endif %}{% for tool_call in message.tool_calls %}{% set tool_call = tool_call.function if tool_call.function is defined else tool_call %}<tool_call>{\"name\":\"{{ tool_call.name }}\",\"arguments\":{{ tool_call.arguments | tojson }}}</tool_call>{% endfor %}<|im_end|>{% elif message.role == 'tool' %}{% if loop.index0 == 0 or messages[loop.index0 - 1].role != 'tool' %}<|im_start|>user{% endif %}<tool_response>\n{% if message['content'] is string %}{{ message.content }}{% 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 content['type'] == 'text' or 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}{% endif %}</tool_response>{% if loop.last or messages[loop.index0 + 1].role != 'tool' %}<|im_end|>{% endif %}{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
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}
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config.json
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{
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"architectures": [
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"Qwen2_5_VLForConditionalGeneration"
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],
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"attention_dropout": 0.0,
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 3584,
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"image_token_id": 151655,
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"initializer_range": 0.02,
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"intermediate_size": 18944,
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"max_position_embeddings": 128000,
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"max_window_layers": 28,
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"model_type": "qwen2_5_vl",
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"num_attention_heads": 28,
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"num_hidden_layers": 28,
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"num_key_value_heads": 4,
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"pad_token_id": 151643,
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"rms_norm_eps": 1e-06,
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"rope_scaling": {
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"rope_type": "default",
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"type": "default"
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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": false,
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"torch_dtype": "float32",
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"transformers_version": "4.51.1",
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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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"depth": 32,
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"fullatt_block_indexes": [
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],
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"hidden_act": "silu",
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"hidden_size": 1280,
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"in_channels": 3,
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"in_chans": 3,
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"initializer_range": 0.02,
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"intermediate_size": 3420,
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"model_type": "qwen2_5_vl",
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"num_heads": 16,
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"out_hidden_size": 3584,
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"patch_size": 14,
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"spatial_merge_size": 2,
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"spatial_patch_size": 14,
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"temporal_patch_size": 2,
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"tokens_per_second": 2,
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"torch_dtype": "float32",
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"window_size": 112
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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": 152064
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}
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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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],
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"pad_token_id": 151643,
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"repetition_penalty": 1.05,
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"temperature": 1e-06,
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"transformers_version": "4.51.1"
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}
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|
||||||
|
"visual.blocks.9.attn.qkv.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"visual.blocks.9.attn.qkv.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"visual.blocks.9.mlp.down_proj.bias": "model-00002-of-00004.safetensors",
|
||||||
|
"visual.blocks.9.mlp.down_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"visual.blocks.9.mlp.gate_proj.bias": "model-00003-of-00004.safetensors",
|
||||||
|
"visual.blocks.9.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"visual.blocks.9.mlp.up_proj.bias": "model-00002-of-00004.safetensors",
|
||||||
|
"visual.blocks.9.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"visual.blocks.9.norm1.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"visual.blocks.9.norm2.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"visual.merger.ln_q.weight": "model-00003-of-00004.safetensors",
|
||||||
|
"visual.merger.mlp.0.bias": "model-00003-of-00004.safetensors",
|
||||||
|
"visual.merger.mlp.0.weight": "model-00003-of-00004.safetensors",
|
||||||
|
"visual.merger.mlp.2.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"visual.merger.mlp.2.weight": "model-00003-of-00004.safetensors",
|
||||||
|
"visual.patch_embed.proj.weight": "model-00003-of-00004.safetensors"
|
||||||
|
}
|
||||||
|
}
|
||||||
37
preprocessor_config.json
Normal file
37
preprocessor_config.json
Normal file
@@ -0,0 +1,37 @@
|
|||||||
|
{
|
||||||
|
"crop_size": null,
|
||||||
|
"data_format": "channels_first",
|
||||||
|
"default_to_square": true,
|
||||||
|
"device": null,
|
||||||
|
"disable_grouping": null,
|
||||||
|
"do_center_crop": null,
|
||||||
|
"do_convert_rgb": true,
|
||||||
|
"do_normalize": true,
|
||||||
|
"do_rescale": true,
|
||||||
|
"do_resize": true,
|
||||||
|
"image_mean": [
|
||||||
|
0.48145466,
|
||||||
|
0.4578275,
|
||||||
|
0.40821073
|
||||||
|
],
|
||||||
|
"image_processor_type": "Qwen2VLImageProcessorFast",
|
||||||
|
"image_std": [
|
||||||
|
0.26862954,
|
||||||
|
0.26130258,
|
||||||
|
0.27577711
|
||||||
|
],
|
||||||
|
"input_data_format": null,
|
||||||
|
"max_pixels": 12845056,
|
||||||
|
"merge_size": 2,
|
||||||
|
"min_pixels": 3136,
|
||||||
|
"patch_size": 14,
|
||||||
|
"processor_class": "Qwen2_5_VLProcessor",
|
||||||
|
"resample": 3,
|
||||||
|
"rescale_factor": 0.00392156862745098,
|
||||||
|
"return_tensors": null,
|
||||||
|
"size": {
|
||||||
|
"longest_edge": 12845056,
|
||||||
|
"shortest_edge": 3136
|
||||||
|
},
|
||||||
|
"temporal_patch_size": 2
|
||||||
|
}
|
||||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
|||||||
|
{
|
||||||
|
"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
|
||||||
|
}
|
||||||
|
}
|
||||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
|
||||||
|
size 11421896
|
||||||
209
tokenizer_config.json
Normal file
209
tokenizer_config.json
Normal file
@@ -0,0 +1,209 @@
|
|||||||
|
{
|
||||||
|
"add_bos_token": false,
|
||||||
|
"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
|
||||||
|
},
|
||||||
|
"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
|
||||||
|
},
|
||||||
|
"151657": {
|
||||||
|
"content": "<tool_call>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151658": {
|
||||||
|
"content": "</tool_call>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151659": {
|
||||||
|
"content": "<|fim_prefix|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151660": {
|
||||||
|
"content": "<|fim_middle|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151661": {
|
||||||
|
"content": "<|fim_suffix|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151662": {
|
||||||
|
"content": "<|fim_pad|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151663": {
|
||||||
|
"content": "<|repo_name|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151664": {
|
||||||
|
"content": "<|file_sep|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"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": {},
|
||||||
|
"model_max_length": 131072,
|
||||||
|
"pad_token": "<|endoftext|>",
|
||||||
|
"processor_class": "Qwen2_5_VLProcessor",
|
||||||
|
"split_special_tokens": false,
|
||||||
|
"tokenizer_class": "Qwen2Tokenizer",
|
||||||
|
"unk_token": null
|
||||||
|
}
|
||||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user