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.gitattributes

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

README.md

@@ -0,0 +1,204 @@
+---
+license: other
+license_name: bsd-3-clause
+license_link: https://github.com/TencentARC/TimeLens/blob/main/LICENSE
+language:
+- en
+tags:
+- video-grounding
+- temporal-grounding
+- video-understanding
+- qwen2-vl
+library_name: transformers
+pipeline_tag: video-text-to-text
+---
+
+# TimeLens-7B
+
+📑 [**Paper**](TODO) | 💻 [**Code**](https://github.com/TencentARC/TimeLens) | 🏠 [**Project Page**](https://timelens-arc-lab.github.io/) | 🤗 [**Model & Data**](https://huggingface.co/collections/TencentARC/TimeLens)
+
+
+## ✨ Model Description
+
+**TimeLens-7B** is an MLLM with strong video temporal grounding (VTG) capability, fine-tuned from [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct). It is trained with a carefully crafted RLVR (reinforcement learning with verifiable rewards) recipe and improved timestamp encoding strategy proposed in our [paper](TODO), utilizing our high-quality VTG training dataset [TimeLens-100K](https://huggingface.co/datasets/TencentARC/TimeLens-100K).
+
+## Performance
+
+TimeLens-7B achieves strong video temporal grounding performance:
+
+<table>
+  <thead>
+    <tr>
+      <th rowspan="2" align="center">Model</th>
+      <th colspan="4" align="center">Charades-TimeLens</th>
+      <th colspan="4" align="center">ActivityNet-TimeLens</th>
+      <th colspan="4" align="center">QVHighlights-TimeLens</th>
+    </tr>
+    <tr>
+      <th align="center">R1<br>@0.3</th>
+      <th align="center">R1<br>@0.5</th>
+      <th align="center">R1<br>@0.7</th>
+      <th align="center">mIoU</th>
+      <th align="center">R1<br>@0.3</th>
+      <th align="center">R1<br>@0.5</th>
+      <th align="center">R1<br>@0.7</th>
+      <th align="center">mIoU</th>
+      <th align="center">R1<br>@0.3</th>
+      <th align="center">R1<br>@0.5</th>
+      <th align="center">R1<br>@0.7</th>
+      <th align="center">mIoU</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><a href="https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct">Qwen2.5-VL-7B-Instruct</a></td>
+      <td align="center">59.7</td>
+      <td align="center">37.8</td>
+      <td align="center">16.6</td>
+      <td align="center">39.3</td>
+      <td align="center">44.1</td>
+      <td align="center">31.0</td>
+      <td align="center">16.1</td>
+      <td align="center">31.4</td>
+      <td align="center">41.5</td>
+      <td align="center">27.8</td>
+      <td align="center">15.2</td>
+      <td align="center">31.6</td>
+    </tr>
+    <tr>
+      <td><a href="https://huggingface.co/TencentARC/TimeLens-7B"><b>TimeLens-7B</b>🚀</a></td>
+      <td align="center"><b>70.5</b></td>
+      <td align="center"><b>55.6</b></td>
+      <td align="center"><b>28.4</b></td>
+      <td align="center"><b>48.8</b></td>
+      <td align="center"><b>62.8</b></td>
+      <td align="center"><b>51.0</b></td>
+      <td align="center"><b>32.6</b></td>
+      <td align="center"><b>46.2</b></td>
+      <td align="center"><b>74.1</b></td>
+      <td align="center"><b>62.7</b></td>
+      <td align="center"><b>43.1</b></td>
+      <td align="center"><b>56.0</b></td>
+    </tr>
+    <tr>
+      <td><a href="https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct">Qwen3-VL-8B-Instruct</a></td>
+      <td align="center">69.2</td>
+      <td align="center">53.4</td>
+      <td align="center">27.5</td>
+      <td align="center">48.3</td>
+      <td align="center">62.1</td>
+      <td align="center">51.2</td>
+      <td align="center">34.4</td>
+      <td align="center">46.8</td>
+      <td align="center">74.2</td>
+      <td align="center">64.6</td>
+      <td align="center">49.3</td>
+      <td align="center">59.4</td>
+    </tr>
+    <tr>
+      <td><a href="https://huggingface.co/TencentARC/TimeLens-8B"><b>TimeLens-8B</b>🚀</a></td>
+      <td align="center"><b>72.0</b></td>
+      <td align="center"><b>56.3</b></td>
+      <td align="center"><b>29.2</b></td>
+      <td align="center"><b>50.3</b></td>
+      <td align="center"><b>64.5</b></td>
+      <td align="center"><b>53.7</b></td>
+      <td align="center"><b>35.2</b></td>
+      <td align="center"><b>48.7</b></td>
+      <td align="center"><b>75.6</b></td>
+      <td align="center"><b>65.3</b></td>
+      <td align="center"><b>51.3</b></td>
+      <td align="center"><b>61.5</b></td>
+    </tr>
+  </tbody>
+</table>
+
+> For detailed comparison with other models, please refer to the [Leaderboard](https://timelens-arc-lab.github.io/#leaderboard).
+
+
+## 🚀 Usage
+
+Install the following packages:
+```bash
+pip install transformers==4.57.1 accelerate==1.6.0 torch==2.6.0 torchvision==0.21.0
+pip install qwen-vl-utils[decord]==0.0.14
+pip install flash-attn==2.7.4.post1 --no-build-isolation --no-cache-dir  # Flash-Attention 2 to speed up generation
+```
+
+Using 🤗Transformers for Inference:
+```python
+import torch
+from transformers import AutoModelForImageTextToText, AutoProcessor
+from qwen_vl_utils import process_vision_info
+
+# Load model and processor
+model = AutoModelForImageTextToText.from_pretrained(
+    "TencentARC/TimeLens-7B",
+    dtype=torch.bfloat16,
+    attn_implementation="flash_attention_2",
+    device_map="auto",
+)
+
+processor = AutoProcessor.from_pretrained(
+    "TencentARC/TimeLens-7B",
+    padding_side="left",
+    do_resize=False,
+    trust_remote_code=True,
+)
+
+# Prepare input
+query = "A man is sitting on a chair"
+video_path = "https://huggingface.co/datasets/JungleGym/TimeLens-Assets/blob/main/2Y8XQ.mp4"
+
+GROUNDER_PROMPT = "You are given a video with multiple frames. The numbers before each video frame indicate its sampling timestamp (in seconds). Please find the visual event described by the sentence '{}', determining its starting and ending times. The format should be: 'The event happens in <start time> - <end time> seconds'."
+
+messages = [{
+    'role': 'user',
+    'content': [
+        {
+            'type': 'video',
+            'video': video_path,
+            'min_pixels': 64 * 28 * 28,
+            'total_pixels': 14336 * 28 * 28,
+            'fps': 2,
+        },
+        {
+            'type': 'text',
+            'text': GROUNDER_PROMPT.format(query)
+        }
+    ]
+}]
+
+text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+images, videos = process_vision_info(messages, return_video_metadata=True)
+
+inputs = processor(
+    text=[text],
+    images=images,
+    videos=videos,
+    padding=True,
+    return_tensors='pt'
+).to("cuda")
+
+output_ids = model.generate(
+    **inputs,
+    do_sample=False,
+    max_new_tokens=512,
+)
+
+generated_ids_trimmed = [
+    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, output_ids)
+]
+answer = processor.batch_decode(
+    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)[0]
+print(f"Answer: {answer}")
+```
+
+## Citation
+
+If you find our work helpful for your research and applications, please cite our paper:
+
+```bibtex
+TODO
+```

added_tokens.json

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chat_template.json

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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 %}"
+}

config.json

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+  "rms_norm_eps": 1e-06,
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+      16,
+      24,
+      24
+    ],
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+    "type": "default"
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+    "temporal_patch_size": 2,
+    "tokens_per_second": 2,
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+  "vision_start_token_id": 151652,
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+  "vocab_size": 152064
+}

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+  "top_p": 0.001,
+  "transformers_version": "4.51.3"
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+  }
+}

preprocessor_config.json

@@ -0,0 +1,32 @@
+{
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": false,
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_processor_type": "Qwen2VLImageProcessor",
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "max_pixels": 12845056,
+  "merge_size": 2,
+  "min_pixels": 3136,
+  "patch_size": 14,
+  "processor_class": "TimeLensProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "longest_edge": 12845056,
+    "shortest_edge": 3136
+  },
+  "temporal_patch_size": 2,
+  "auto_map": {
+    "AutoProcessor": "processing_timelens.TimeLensProcessor"
+  }
+}

processing_timelens.py

@@ -0,0 +1,227 @@
+# Modified from https://github.com/huggingface/transformers/blob/v4.57.1/src/transformers/models/qwen2_5_vl/processing_qwen2_5_vl.py
+# Copyright 2025 The Qwen Team and The HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import torch
+from transformers import Qwen2_5_VLProcessor
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.models.qwen2_5_vl.processing_qwen2_5_vl import (
+    Qwen2_5_VLProcessorKwargs,
+)
+
+
+class TimeLensProcessor(Qwen2_5_VLProcessor):
+    r"""
+    Constructs a Qwen2.5-VL processor which wraps a Qwen2.5-VL image processor and a Qwen2 tokenizer into a single processor.
+    [`Qwen2_5_VLProcessor`] offers all the functionalities of [`Qwen2VLImageProcessor`] and [`Qwen2TokenizerFast`]. See the
+    [`~Qwen2_5_VLProcessor.__call__`] and [`~Qwen2_5_VLProcessor.decode`] for more information.
+    Args:
+        image_processor ([`Qwen2VLImageProcessor`], *optional*):
+            The image processor is a required input.
+        tokenizer ([`Qwen2TokenizerFast`], *optional*):
+            The tokenizer is a required input.
+        video_processor ([`Qwen2_5_VLVideoProcessor`], *optional*):
+            The video processor is a required input.
+        chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
+            in a chat into a tokenizable string.
+    """
+
+    def __init__(
+        self,
+        image_processor=None,
+        tokenizer=None,
+        video_processor=None,
+        chat_template=None,
+        **kwargs,
+    ):
+        super().__init__(
+            image_processor, tokenizer, video_processor, chat_template, **kwargs
+        )
+        # ============ [TimeLens] Modification BEGIN ============
+        self.vision_start = (
+            "<|vision_start|>"
+            if not hasattr(tokenizer, "vision_start")
+            else tokenizer.vision_start
+        )
+        self.vision_end = (
+            "<|vision_end|>"
+            if not hasattr(tokenizer, "vision_end")
+            else tokenizer.vision_end
+        )
+        # ============ [TimeLens] Modification END ==============
+
+    def __call__(
+        self,
+        images=None,
+        text=None,
+        videos=None,
+        **kwargs,
+    ) -> BatchFeature:
+        """
+        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
+        and `kwargs` arguments to Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`] if `text` is not `None` to encode
+        the text. To prepare the vision inputs, this method forwards the `vision_infos` and `kwargs` arguments to
+        Qwen2VLImageProcessor's [`~Qwen2VLImageProcessor.__call__`] if `vision_infos` is not `None`.
+
+        Args:
+            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`):
+                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+                tensor. Both channels-first and channels-last formats are supported.
+            text (`str`, `list[str]`, `list[list[str]]`):
+                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+            videos (`np.ndarray`, `torch.Tensor`, `list[np.ndarray]`, `list[torch.Tensor]`):
+                The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch
+                tensor, or a nested list of 3D frames. Both channels-first and channels-last formats are supported.
+            return_tensors (`str` or [`~utils.TensorType`], *optional*):
+                If set, will return tensors of a particular framework. Acceptable values are:
+                - `'tf'`: Return TensorFlow `tf.constant` objects.
+                - `'pt'`: Return PyTorch `torch.Tensor` objects.
+                - `'np'`: Return NumPy `np.ndarray` objects.
+                - `'jax'`: Return JAX `jnp.ndarray` objects.
+
+        Returns:
+            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+              `None`).
+            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+            - **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`.
+            - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`.
+            - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`.
+            - **second_per_grid_ts** -- List of video seconds per time grid. Returned when `videos` is not `None`.
+        """
+        output_kwargs = self._merge_kwargs(
+            Qwen2_5_VLProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+
+        image_inputs = videos_inputs = {}
+        if images is not None:
+            image_inputs = self.image_processor(
+                images=images, **output_kwargs["images_kwargs"]
+            )
+            image_grid_thw = image_inputs["image_grid_thw"]
+
+        if videos is not None:
+            # ============ [TimeLens] Modification BEGIN ============
+            # videos is a list of (video_tensor, metadata) tuples
+            videos, metadata = [v[0] for v in videos], [v[1] for v in videos]
+            # Duplicate frames at even indices
+            for cur_video_tensor in videos:
+                cur_video_tensor[1::2] = cur_video_tensor[::2]
+            # Calculate sampled timestamps for each video
+            frames_timestamps = [
+                [
+                    idx / cur_metadata["fps"]
+                    for idx in cur_metadata["frames_indices"][::2]
+                ]
+                for cur_metadata in metadata
+            ]
+
+            videos_inputs = self.video_processor(
+                videos=videos, **output_kwargs["videos_kwargs"]
+            )
+            video_grid_thw = videos_inputs["video_grid_thw"]
+            # ============ [TimeLens] Modification END ==============
+
+        if not isinstance(text, list):
+            text = [text]
+
+        text = text.copy()  # below lines change text in-place
+        if images is not None:
+            merge_length = self.image_processor.merge_size**2
+            index = 0
+            for i in range(len(text)):
+                while self.image_token in text[i]:
+                    num_image_tokens = image_grid_thw[index].prod() // merge_length
+                    text[i] = text[i].replace(
+                        self.image_token, "<|placeholder|>" * num_image_tokens, 1
+                    )
+                    index += 1
+                text[i] = text[i].replace("<|placeholder|>", self.image_token)
+
+        if videos is not None:
+            merge_length = self.video_processor.merge_size**2
+            index = 0
+            # ============ [TimeLens] Modification BEGIN ============
+            for i in range(len(text)):
+                while self.video_token in text[i]:
+                    cur_video_tokens = ""
+                    num_tokens_per_frame = (
+                        video_grid_thw[index][1:].prod() // merge_length
+                    )
+                    per_frame_tokens = (
+                        self.vision_start
+                        + "<|placeholder|>" * num_tokens_per_frame
+                        + self.vision_end
+                    )
+                    for cur_frames_timestamp in frames_timestamps[index]:
+                        cur_video_tokens += (
+                            f"{cur_frames_timestamp:.1f}s: " + per_frame_tokens
+                        )
+
+                    text[i] = text[i].replace(
+                        self.vision_start + self.video_token + self.vision_end,
+                        cur_video_tokens,
+                        1,
+                    )
+                    index += 1
+                text[i] = text[i].replace("<|placeholder|>", self.image_token)
+            # modeling_qwen2_5_vl.py calls `.item()` on image_grid_thw to convert t, h, w from tensor to int, so we create image_grid_thw as Tensor to be compatible with `.item()` call
+            image_grid_thw = torch.tensor(
+                [
+                    [1, grid_h, grid_w]
+                    for grid_t, grid_h, grid_w in video_grid_thw
+                    for _ in range(grid_t)
+                ],
+                dtype=torch.long,
+            )
+
+            image_inputs = {
+                "pixel_values": videos_inputs[
+                    "pixel_values_videos"
+                ],  # [grid_t * grid_h * grid_w, channel * temporal_patch_size * patch_size * patch_size] = [num_patches, dim]
+                "image_grid_thw": image_grid_thw,
+            }
+            videos_inputs = {}
+            # ============ [TimeLens] Modification END ==============
+
+        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
+        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop(
+            "return_mm_token_type_ids", None
+        )
+        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
+        self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video"])
+
+        if return_mm_token_type_ids:
+            array_ids = np.array(text_inputs["input_ids"])
+            mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
+            mm_token_type_ids[array_ids == self.image_token_id] = 1
+            text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()
+
+        return BatchFeature(
+            data={**text_inputs, **image_inputs, **videos_inputs},
+            tensor_type=return_tensors,
+        )

special_tokens_map.json

@@ -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
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
+size 11421896

tokenizer_config.json

@@ -0,0 +1,211 @@
+{
+  "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": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": false,
+  "do_resize": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "padding_side": "left",
+  "processor_class": "Qwen2_5_VLProcessor",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}