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README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

adapter/adapter_config.json

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+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "jinaai/colqwen25-duo-base",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": false,
+  "init_lora_weights": "gaussian",
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_dropout": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": "(.*(model).*(down_proj|gate_proj|up_proj|k_proj|q_proj|v_proj|o_proj).*$|.*(custom_text_proj).*$)",
+  "task_type": "FEATURE_EXTRACTION",
+  "use_dora": false,
+  "use_rslora": false
+}

adapter/adapter_model.safetensors

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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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+{
+  "_name_or_path": "jinaai/jina-embeddings-v4",
+  "pretrained_peft_model_name_or_path": "adapter/",
+  "architectures": [
+    "ColQwen25Duo"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_colqwen_duo.ColQwen25DuoConfig",
+    "AutoModel": "modeling_colqwen_duo.ColQwen25Duo"
+  },
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "image_token_id": 151655,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 128000,
+  "max_window_layers": 70,
+  "multi_vector_projector_dim": 128,
+  "num_attention_heads": 16,
+  "num_hidden_layers": 36,
+  "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,
+  "single_vector_pool_strategy": "mean",
+  "single_vector_projector_dim": 1024,
+  "sliding_window": 32768,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.50.0.dev0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "video_token_id": 151656,
+  "vision_config": {
+    "hidden_size": 1280,
+    "in_chans": 3,
+    "model_type": "qwen2_5_vl",
+    "out_hidden_size": 2048,
+    "spatial_patch_size": 14,
+    "tokens_per_second": 2,
+    "torch_dtype": "bfloat16"
+  },
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+  "vision_start_token_id": 151652,
+  "vision_token_id": 151654,
+  "vocab_size": 151936
+}

configuration_colqwen_duo.py

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+from transformers.models.qwen2_5_vl import Qwen2_5_VLConfig
+
+from typing import Optional
+
+class ColQwen25DuoConfig(Qwen2_5_VLConfig):
+    """
+    Configuration for the ColQwenDuo model.
+    """
+
+    def __init__(
+        self,
+        single_vector_projector_dim: int = 1024,
+        single_vector_pool_strategy: str = "mean",
+        multi_vector_projector_dim: int = 128,
+        pretrained_peft_model_name_or_path: Optional[str] = None,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.single_vector_projector_dim = single_vector_projector_dim
+        self.single_vector_pool_strategy = single_vector_pool_strategy
+        self.multi_vector_projector_dim = multi_vector_projector_dim
+        self.pretrained_peft_model_name_or_path = pretrained_peft_model_name_or_path
+

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "transformers_version": "4.50.0.dev0"
+}

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model.safetensors.index.json

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+  }
+}

modeling_colqwen_duo.py

@@ -0,0 +1,683 @@
+
+import os
+import math
+import numpy as np
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import Any, Callable, ClassVar, Dict, List, Optional, Union, cast
+from typing_extensions import Unpack
+ 
+import torch
+from torch import nn
+from torch.utils.data import DataLoader
+
+from functools import partial
+from PIL import Image
+from tqdm import tqdm
+from enum import Enum
+
+from transformers import BatchEncoding, BatchFeature
+from transformers.modeling_utils import PreTrainedModel
+
+from transformers.models.qwen2_vl.modeling_qwen2_vl import Qwen2VLCausalLMOutputWithPast
+
+from transformers.models.qwen2_5_vl import Qwen2_5_VLProcessor, Qwen2_5_VLForConditionalGeneration
+
+from transformers.processing_utils import (
+    AllKwargsForChatTemplate,
+    ImageInput,
+    PreTokenizedInput,
+    TextInput,
+    VideoInput,
+)
+
+from huggingface_hub import snapshot_download
+
+from .configuration_colqwen_duo import ColQwen25DuoConfig
+
+
+def get_torch_device(device: str = "auto") -> str:
+    """
+    Returns the device (string) to be used by PyTorch.
+
+    `device` arg defaults to "auto" which will use:
+    - "cuda:0" if available
+    - else "mps" if available
+    - else "cpu".
+    """
+
+    if device == "auto":
+        if torch.cuda.is_available():
+            device = "cuda:0"
+        elif torch.backends.mps.is_available():  # for Apple Silicon
+            device = "mps"
+        else:
+            device = "cpu"
+        logger.info(f"Using device: {device}")
+
+    return device
+
+
+class PromptType(str, Enum):
+    query = "query"
+    passage = "passage"
+
+
+
+
+class BaseVisualRetrieverProcessor(ABC):
+    """
+    Base class for visual retriever processors.
+    """
+
+    @abstractmethod
+    def process_images(
+        self,
+        images: List[Image.Image],
+    ) -> Union[BatchFeature, BatchEncoding]:
+        pass
+
+    @abstractmethod
+    def process_texts(
+        self,
+        texts: List[str],
+        max_length: int = 50,
+        suffix: Optional[str] = None,
+        prefix: Optional[str] = None,
+    ) -> Union[BatchFeature, BatchEncoding]:
+        pass
+
+    @abstractmethod
+    def score(
+        self,
+        qs: List[torch.Tensor],
+        ps: List[torch.Tensor],
+        device: Optional[Union[str, torch.device]] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        pass
+
+    @staticmethod
+    def score_single_vector(
+        qs: List[torch.Tensor],
+        ps: List[torch.Tensor],
+        device: Optional[Union[str, torch.device]] = None,
+    ) -> torch.Tensor:
+        """
+        Compute the dot product score for the given single-vector query and passage embeddings.
+        """
+        device = device or get_torch_device("auto")
+
+        if len(qs) == 0:
+            raise ValueError("No queries provided")
+        if len(ps) == 0:
+            raise ValueError("No passages provided")
+
+        qs_stacked = torch.stack(qs).to(device)
+        ps_stacked = torch.stack(ps).to(device)
+
+        scores = torch.einsum("bd,cd->bc", qs_stacked, ps_stacked)
+        assert scores.shape[0] == len(qs), f"Expected {len(qs)} scores, got {scores.shape[0]}"
+
+        scores = scores.to(torch.float32)
+        return scores
+
+    @staticmethod
+    def score_multi_vector(
+        qs: List[torch.Tensor],
+        ps: List[torch.Tensor],
+        batch_size: int = 128,
+        device: Optional[Union[str, torch.device]] = None,
+    ) -> torch.Tensor:
+        """
+        Compute the MaxSim score (ColBERT-like) for the given multi-vector query and passage embeddings.
+        """
+        device = device or get_torch_device("auto")
+
+        if len(qs) == 0:
+            raise ValueError("No queries provided")
+        if len(ps) == 0:
+            raise ValueError("No passages provided")
+
+        scores_list: List[torch.Tensor] = []
+
+        for i in range(0, len(qs), batch_size):
+            scores_batch = []
+            qs_batch = torch.nn.utils.rnn.pad_sequence(qs[i : i + batch_size], batch_first=True, padding_value=0).to(
+                device
+            )
+            for j in range(0, len(ps), batch_size):
+                ps_batch = torch.nn.utils.rnn.pad_sequence(
+                    ps[j : j + batch_size], batch_first=True, padding_value=0
+                ).to(device)
+                scores_batch.append(torch.einsum("bnd,csd->bcns", qs_batch, ps_batch).max(dim=3)[0].sum(dim=2))
+            scores_batch = torch.cat(scores_batch, dim=1).cpu()
+            scores_list.append(scores_batch)
+
+        scores = torch.cat(scores_list, dim=0)
+        assert scores.shape[0] == len(qs), f"Expected {len(qs)} scores, got {scores.shape[0]}"
+
+        scores = scores.to(torch.float32)
+        return scores
+
+
+class QwenVLProcessor(ABC):
+
+    def __call__(
+        self,
+        images: Optional[ImageInput] = None,
+        text: Optional[Union[TextInput, PreTokenizedInput, List[PreTokenizedInput]]] = None,
+        videos: Optional[VideoInput] = None,
+        **kwargs,
+    ) -> BatchFeature:
+        return super().__call__(images=images, text=text, videos=videos, **kwargs)  # type: ignore
+
+    def apply_chat_template(
+        self,
+        conversation: Union[List[Dict[str, str]], List[List[Dict[str, str]]]],
+        chat_template: Optional[str] = None,
+        **kwargs: Unpack[AllKwargsForChatTemplate],
+    ) -> str:
+        return super().apply_chat_template(conversation=conversation, chat_template=chat_template, **kwargs)  # type: ignore
+
+
+class QwenVLEmbeddingProcessorBase(BaseVisualRetrieverProcessor, QwenVLProcessor):
+
+    assistant_prefix_len: int = 58  # length of prefix created by
+    # super().apply_chat_template(conversation=conversation, chat_template=chat_template, **kwargs)
+
+    @staticmethod
+    def round_by_factor(number: float, factor: int) -> int:
+        """Returns the closest integer to 'number' that is divisible by 'factor'."""
+        return round(number / factor) * factor
+
+    @staticmethod
+    def ceil_by_factor(number: float, factor: int) -> int:
+        """Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'."""
+        return math.ceil(number / factor) * factor
+
+    @staticmethod
+    def floor_by_factor(number: float, factor: int) -> int:
+        """Returns the largest integer less than or equal to 'number' that is divisible by 'factor'."""
+        return math.floor(number / factor) * factor
+
+    def process_images(
+        self,
+        images: Union[List[Image.Image], List[List[Image.Image]]],
+    ) -> BatchFeature:
+
+        if isinstance(images[0], list):
+            images = cast(List[List[Image.Image]], images)
+            text_doc = []
+            for i in range(len(images)):
+                conversation = [{"role": "user", "content": [{"type": "image"}] * len(images[i])}]
+                template = self.apply_chat_template(conversation, add_generation_prompt=False)
+                text_doc.append(template[self.assistant_prefix_len :])
+
+        else:
+            images = cast(List[Image.Image], images)
+            text_doc = [
+                "<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>Describe the image.<|im_end|>\n"
+            ] * len(images)
+
+        # The following code is a hack to make sure the scatter in DDP is done correctly when training on multiple GPUs
+        batch_doc = self(text=text_doc, images=images, padding="longest", return_tensors="pt")  # type: ignore
+        # Separate pixel_values for each image
+        offsets = batch_doc["image_grid_thw"][:, 1] * batch_doc["image_grid_thw"][:, 2]
+        # Pad pixel_values to the same length to be able to make it into a tensor
+        pixel_values = torch.split(batch_doc["pixel_values"], offsets.tolist())
+
+        max_length = max([len(pv) for pv in pixel_values])
+
+        pixel_values = [
+            torch.cat([pv, torch.zeros((max_length - len(pv), pv.shape[1]), dtype=pv.dtype, device=pv.device)])
+            for pv in pixel_values
+        ]
+
+        batch_doc["pixel_values"] = torch.stack(pixel_values)
+        return batch_doc
+
+    def process_texts(
+        self,
+        texts: List[str],
+        max_length: int = 8192,
+        suffix: Optional[str] = None,
+        prefix: Optional[str] = None,
+        padding: Optional[str] = None,
+    ) -> BatchFeature:
+
+        if suffix is None:
+            suffix = "<pad>" * 10
+
+        padded_texts: List[str] = []
+
+        for text in texts:
+            if prefix:
+                text = f"{prefix}: {text}"
+            text += suffix
+            padded_texts.append(text)
+
+        text_batch = self(
+            text=padded_texts,
+            return_tensors="pt",
+            padding=padding or "longest",
+            max_length=max_length,
+            truncation=True,
+        )
+
+        return text_batch
+
+
+class ColQwenDuoProcessorBase(QwenVLEmbeddingProcessorBase):
+    """
+    Processor for ColQwenDuo. Mirrors the `ColQwen2Processor` class.
+    """
+
+    def score(
+        self,
+        qs: List[torch.Tensor],
+        ps: List[torch.Tensor],
+        vector_type: str,
+        device: Optional[Union[str, torch.device]] = None,
+        truncate: Optional[int] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        """
+        Compute the MaxSim score (ColBERT-like) for the given multi-vector query and passage embeddings.
+        """
+        if truncate:
+            qs = [q[..., :truncate] for q in qs]
+            ps = [p[..., :truncate] for p in ps]
+
+        if vector_type == "single_vector":
+            return self.score_single_vector(qs, ps, device=device)
+        elif vector_type == "multi_vector":
+            return self.score_multi_vector(qs, ps, device=device, **kwargs)
+        else:
+            raise ValueError('vector_type must be one of the following: [`single_vector`, `multi_vector`]')
+
+
+class ColQwen25DuoProcessor(ColQwenDuoProcessorBase, Qwen2_5_VLProcessor):
+    def __init__(self, *args, **kwargs) -> None:
+        Qwen2_5_VLProcessor.__init__(self, *args, **kwargs)
+
+
+@dataclass
+class HybridModelOutput:
+    """
+    Base class for the Hybrid Model outputs.
+    Args:
+        vlm_last_hidden_states (torch.Tensor, optional): Last hidden states of the VLM.
+        single_vec_emb (torch.Tensor, optional): Single-vector embeddings.
+        multi_vec_emb (torch.Tensor, optional): Multi-vector embeddings.
+    """
+
+    vlm_last_hidden_states: Optional[torch.Tensor] = None
+    single_vec_emb: Optional[torch.Tensor] = None
+    multi_vec_emb: Optional[torch.Tensor] = None
+
+class EncodeMixin:
+    """
+    Interface to encode data for MTEB and ViDoRe evaluations.
+    """
+
+    def _process_batches(
+        self,
+        data: List[Union[str, Image.Image]],
+        processor_fn: Callable,
+        desc: str,
+        vector_type: Optional[str] = None,
+        return_numpy: bool = False,
+        **kwargs,
+    ) -> Union[np.ndarray, List[torch.Tensor]]:
+        dataloader = DataLoader(
+            dataset=data,
+            batch_size=kwargs.get("batch_size", 32),
+            shuffle=False,
+            collate_fn=processor_fn,
+        )
+        results = []
+        self.eval()
+        for batch in tqdm(dataloader, desc=desc):
+            with torch.no_grad():
+                batch = {k: v.to(self.device) for k, v in batch.items()}
+                with torch.autocast(device_type=torch.device(self.device).type):
+                    embeddings = self(**batch)
+                    if isinstance(embeddings, HybridModelOutput) and (vector_type == "single_vector"):
+                        embeddings = embeddings.single_vec_emb
+                    elif isinstance(embeddings, HybridModelOutput) and (vector_type == "multi_vector"):
+                        embeddings = embeddings.multi_vec_emb
+                    elif not vector_type and isinstance(embeddings, HybridModelOutput):
+                        embeddings = embeddings.single_vec_emb  # get single-vectors for text2text tasks by default
+                    results.append(embeddings.cpu() if return_numpy else list(torch.unbind(embeddings)))
+        if return_numpy:
+            return np.concatenate([result.numpy() for result in results], axis=0)
+        return [item for sublist in results for item in sublist]
+
+    def encode(
+        self,
+        sentences: List[str],
+        max_length: int = 8192,
+        batch_size: int = 8,
+        prefixes: Optional[List[str]] = None,
+        desc: Optional[str] = None,
+        vector_type: Optional[str] = None,
+        padding: Optional[str] = None,
+        prompt_type: Optional[PromptType] = None,
+        **kwargs,
+    ) -> np.ndarray:
+        prefix = None
+        if isinstance(prefixes, list) and len(prefixes) > 0:
+            if prompt_type:
+                desc = f"MTEB: Encode {prompt_type.value}..."
+                prefix = prefixes[0] if prompt_type.value == "query" else prefixes[1]
+            else:
+                prefix = prefixes[0]
+        processor_fn = partial(self.processor.process_texts, max_length=max_length, prefix=prefix, padding=padding)
+        desc = desc or "MTEB: Encode texts..."
+        return self._process_batches(
+            data=sentences,
+            processor_fn=processor_fn,
+            desc=desc,
+            vector_type=vector_type,
+            batch_size=batch_size,
+            **kwargs,
+        )
+
+    def encode_texts(
+        self,
+        queries: List[str],
+        max_length: int = 8192,
+        batch_size: int = 8,
+        vector_type: Optional[str] = None,
+        desc: Optional[str] = None,
+        **kwargs,
+    ) -> List[torch.Tensor]:
+        processor_fn = partial(self.processor.process_texts, max_length=max_length, prefix="Query")
+        return self._process_batches(
+            data=queries,
+            processor_fn=processor_fn,
+            desc=desc or "Encode queries...",
+            vector_type=vector_type,
+            batch_size=batch_size,
+            **kwargs,
+        )
+
+    def encode_images(
+        self,
+        documents: List[Image.Image],
+        batch_size: int = 8,
+        vector_type: Optional[str] = None,
+        desc: Optional[str] = None,
+        **kwargs,
+    ) -> List[torch.Tensor]:
+        return self._process_batches(
+            data=documents,
+            processor_fn=self.processor.process_images,
+            desc=desc or "Encode documents...",
+            vector_type=vector_type,
+            batch_size=batch_size,
+            **kwargs,
+        )
+
+class QwenVLModel(ABC):
+
+    def get_rope_index(
+        self,
+        input_ids: torch.LongTensor,
+        image_grid_thw: Union[torch.LongTensor, None],
+        attention_mask: torch.Tensor,
+    ) -> tuple[torch.LongTensor, torch.Tensor]:
+        return super().get_rope_index(  # type: ignore
+            input_ids=input_ids,
+            image_grid_thw=image_grid_thw,
+            attention_mask=attention_mask,
+        )
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor,
+        attention_mask: torch.Tensor,
+        position_ids: torch.LongTensor,
+        rope_deltas: torch.Tensor,
+        output_hidden_states: bool,
+        use_cache: bool,
+        **kwargs,
+    ) -> Qwen2VLCausalLMOutputWithPast:
+        return super().forward(  # type: ignore
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            rope_deltas=rope_deltas,
+            output_hidden_states=output_hidden_states,
+            use_cache=use_cache,
+            **kwargs,
+        )
+
+
+class QwenVLEmbeddingBase(EncodeMixin, QwenVLModel):
+    main_input_name: ClassVar[str] = "doc_input_ids"
+
+    def get_vlm_last_hidden_states(
+        self,
+        input_ids: torch.LongTensor,
+        attention_mask: torch.Tensor,
+        **kwargs,
+    ) -> torch.Tensor:
+        if "pixel_values" in kwargs:
+            offsets = kwargs["image_grid_thw"][:, 1] * kwargs["image_grid_thw"][:, 2]
+            kwargs["pixel_values"] = torch.cat([pv[:o] for pv, o in zip(kwargs["pixel_values"], offsets)], dim=0)
+
+        position_ids, rope_deltas = self.get_rope_index(
+            input_ids=input_ids,
+            image_grid_thw=kwargs.get("image_grid_thw", None),
+            attention_mask=attention_mask,
+        )
+
+        outputs = super().forward(
+            input_ids,
+            attention_mask,
+            **kwargs,
+            position_ids=position_ids,
+            rope_deltas=rope_deltas,
+            output_hidden_states=True,
+            use_cache=False,
+        )
+
+        hidden_states = outputs.hidden_states
+        if not hidden_states:
+            raise ValueError("Hidden states not found in model output")
+
+        return hidden_states[-1]
+
+
+class AbstractHybridModel(ABC):
+    """
+    Abstract class for a hybrid model (single-vector and multi-vector embeddings).
+    """
+
+    @property
+    def single_vector_projector_dim(self) -> int:
+        return self.config.single_vector_projector_dim
+
+    @property
+    def multi_vector_projector_dim(self) -> int:
+        return self.config.multi_vector_projector_dim
+
+    @abstractmethod
+    def forward(
+        self,
+        input_ids: torch.LongTensor,
+        attention_mask: torch.Tensor,
+        output_vlm_last_hidden_states: bool = False,
+        *args,
+        **kwargs,
+    ) -> HybridModelOutput:
+        """
+        Forward pass through the model. Returns both single-vector and multi-vector embeddings.
+        Must be implemented by subclasses.
+        """
+        pass
+
+    def _init_projection_layers(self, config) -> None:
+        """
+        Initializes projection layers.
+        """
+        self.config.single_vector_projector_dim = config.single_vector_projector_dim
+        self.config.multi_vector_projector_dim = config.multi_vector_projector_dim
+
+        self.single_vector_projector = nn.Linear(
+            in_features=self.config.hidden_size,
+            out_features=self.config.single_vector_projector_dim,
+        )
+
+        self.multi_vector_projector = nn.Linear(
+            in_features=self.config.hidden_size,
+            out_features=self.config.multi_vector_projector_dim,
+        )
+
+    @staticmethod
+    def _delete_redundant_forward_kwargs(kwargs: Dict[str, Any]) -> None:
+        """
+        Delete redundant kwargs before passing them to the forward method. In-place operation.
+        """
+        for key in ["input_ids", "attention_mask", "output_hidden_states"]:
+            kwargs.pop(key, None)
+
+    def project_to_single_vector_embeddings(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor,
+        input_ids: Optional[torch.LongTensor] = None,
+    ) -> torch.Tensor:
+        """
+        Project the hidden states to single-vector embeddings.
+        """
+
+        pooling_method = self.config.single_vector_pool_strategy
+
+        if pooling_method == "mean" and input_ids is None:
+            print("Warning: `input_ids` is None. Using `legacy-mean` pooling strategy instead.")
+            pooling_method = "legacy-mean"
+
+        if pooling_method == "last-token":
+            pooled_output = hidden_states[:, -1, :]
+        elif pooling_method == "mean":
+            if self._input_has_image(input_ids[0]):  # got document image(s)
+                # getting start and end positions of image tokens; torch.where returns
+                #   (1) a list of indices of input sequences
+                #       (shape corresponds to the total number of images in the batch)
+                #   (2) a list of positions of image tokens in the input sequence
+                #       (shape corresponds to the total number of images in the batch)
+                input_seq_idx, img_start_pos = torch.where(
+                    input_ids == self.config.vision_start_token_id
+                )  # (total number of images), (total number of images)
+                _, img_end_pos = torch.where(
+                    input_ids == self.config.vision_end_token_id
+                )  # (total number of images), (total number of images)
+                means = []
+                for i in range(input_seq_idx.shape[0]):
+                    vector_pos = input_seq_idx[i]
+                    start = img_start_pos[i]
+                    end = img_end_pos[i]
+                    mean_value = hidden_states[vector_pos][start : end + 1].mean(dim=0)
+                    means.append(mean_value)
+                pooled_output = torch.stack(means)
+
+            else:  # got query text
+                pooled_output = torch.sum(hidden_states * attention_mask.unsqueeze(-1), dim=1) / torch.sum(
+                    attention_mask, dim=1, keepdim=True
+                )
+
+        elif pooling_method == "legacy-mean":
+            pooled_output = torch.sum(hidden_states * attention_mask.unsqueeze(-1), dim=1) / torch.sum(
+                attention_mask, dim=1, keepdim=True
+            )
+        else:
+            raise ValueError(f"Invalid pooling strategy: {pooling_method}")
+        single_vec_emb = self.single_vector_projector(pooled_output)
+        return torch.nn.functional.normalize(single_vec_emb, dim=-1)
+
+    def project_to_multi_vector_embeddings(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Project the hidden states to multi-vector embeddings.
+        """
+        multi_vec_emb = self.multi_vector_projector(hidden_states)
+        multi_vec_emb = torch.nn.functional.normalize(multi_vec_emb, dim=-1)
+        return multi_vec_emb * attention_mask.unsqueeze(-1)
+
+    def _input_has_image(self, input_ids):
+        return self.config.vision_start_token_id in input_ids
+
+class ColQwenDuoBase(AbstractHybridModel, QwenVLEmbeddingBase):
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor,
+        attention_mask: torch.Tensor,
+        output_vlm_last_hidden_states: bool = False,
+        **kwargs,
+    ) -> HybridModelOutput:
+        """
+        Forward pass through ColQwenDuo. Returns both single-vector and multi-vector embeddings.
+        Args:
+            input_ids (torch.LongTensor): The input tokens tensor.
+            attention_mask (torch.LongTensor): The attention mask tensor.
+        Returns:
+            HybridModelOutput:
+                single_vector (torch.Tensor): Single-vector embeddings of shape (batch_size, dim).
+                multi_vector (torch.Tensor): Multi-vector embeddings of shape (batch_size, num_tokens, dim).
+        """
+        # Delete redundant kwargs
+        self._delete_redundant_forward_kwargs(kwargs)
+
+        # Forward pass through the VLM
+        hidden_states = self.get_vlm_last_hidden_states(
+            input_ids=input_ids, attention_mask=attention_mask, **kwargs
+        )  # (batch_size, seq_length, hidden_size)
+
+        # Compute the embeddings
+        single_vec_emb = self.project_to_single_vector_embeddings(hidden_states, attention_mask, input_ids=input_ids)
+        multi_vec_emb = self.project_to_multi_vector_embeddings(hidden_states, attention_mask)
+
+        return HybridModelOutput(
+            vlm_last_hidden_states=hidden_states if output_vlm_last_hidden_states else None,
+            single_vec_emb=single_vec_emb,
+            multi_vec_emb=multi_vec_emb,
+        )
+
+
+class ColQwen25Duo(ColQwenDuoBase, Qwen2_5_VLForConditionalGeneration):
+    config_class = ColQwen25DuoConfig
+    def __init__(self, config: ColQwen25DuoConfig):
+        Qwen2_5_VLForConditionalGeneration.__init__(self, config)
+        self._init_projection_layers(config)
+        self.post_init()
+        self.processor = ColQwen25DuoProcessor.from_pretrained(self.name_or_path, trust_remote_code=True)
+    
+    @classmethod
+    def from_pretrained(
+        cls,
+        *args,
+        **kwargs,
+    ):
+        if not "torch_dtype" in kwargs:
+            kwargs["torch_dtype"] = "auto"
+        model = super().from_pretrained(*args, **kwargs)
+        if model.config.pretrained_peft_model_name_or_path:
+            if os.path.isdir(model.name_or_path):
+                model.load_adapter(f'{model.name_or_path}/{model.config.pretrained_peft_model_name_or_path}')
+            else:
+                adapter_cache_path = snapshot_download(
+                    repo_id=model.name_or_path,
+                    allow_patterns=[os.path.join(model.config.pretrained_peft_model_name_or_path, '*')]  # Only download files in adapter/
+                )
+                adapter_path = os.path.join(adapter_cache_path, model.config.pretrained_peft_model_name_or_path)
+                model.load_adapter(adapter_path)
+        return model
+

preprocessor_config.json

@@ -0,0 +1,29 @@
+{
+  "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": 12845056,
+  "merge_size": 2,
+  "min_pixels": 3136,
+  "patch_size": 14,
+  "processor_class": "ColQwen25DuoProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "longest_edge": 12845056,
+    "shortest_edge": 3136
+  },
+  "temporal_patch_size": 2
+}

processing_colqwen_duo.py

@@ -0,0 +1,277 @@
+import torch
+import math
+
+from abc import ABC, abstractmethod
+from PIL import Image
+
+from typing import Dict, List, Optional, Union, cast
+from typing_extensions import Unpack
+
+from transformers import BatchEncoding, BatchFeature
+from transformers.processing_utils import (
+    AllKwargsForChatTemplate,
+    ImageInput,
+    PreTokenizedInput,
+    TextInput,
+    VideoInput,
+)
+from transformers.models.qwen2_vl import Qwen2VLProcessor
+
+
+def get_torch_device(device: str = "auto") -> str:
+    """
+    Returns the device (string) to be used by PyTorch.
+
+    `device` arg defaults to "auto" which will use:
+    - "cuda:0" if available
+    - else "mps" if available
+    - else "cpu".
+    """
+    if device == "auto":
+        if torch.cuda.is_available():
+            device = "cuda:0"
+        elif torch.backends.mps.is_available():  # for Apple Silicon
+            device = "mps"
+        else:
+            device = "cpu"
+    return device
+
+class BaseVisualRetrieverProcessor(ABC):
+    """
+    Base class for visual retriever processors.
+    """
+
+    @abstractmethod
+    def process_images(
+        self,
+        images: List[Image.Image],
+    ) -> Union[BatchFeature, BatchEncoding]:
+        pass
+
+    @abstractmethod
+    def process_texts(
+        self,
+        texts: List[str],
+        max_length: int = 50,
+        suffix: Optional[str] = None,
+        prefix: Optional[str] = None,
+    ) -> Union[BatchFeature, BatchEncoding]:
+        pass
+
+    @abstractmethod
+    def score(
+        self,
+        qs: List[torch.Tensor],
+        ps: List[torch.Tensor],
+        device: Optional[Union[str, torch.device]] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        pass
+
+    @staticmethod
+    def score_single_vector(
+        qs: List[torch.Tensor],
+        ps: List[torch.Tensor],
+        device: Optional[Union[str, torch.device]] = None,
+    ) -> torch.Tensor:
+        """
+        Compute the dot product score for the given single-vector query and passage embeddings.
+        """
+        device = device or get_torch_device("auto")
+
+        if len(qs) == 0:
+            raise ValueError("No queries provided")
+        if len(ps) == 0:
+            raise ValueError("No passages provided")
+
+        qs_stacked = torch.stack(qs).to(device)
+        ps_stacked = torch.stack(ps).to(device)
+
+        scores = torch.einsum("bd,cd->bc", qs_stacked, ps_stacked)
+        assert scores.shape[0] == len(qs), f"Expected {len(qs)} scores, got {scores.shape[0]}"
+
+        scores = scores.to(torch.float32)
+        return scores
+
+    @staticmethod
+    def score_multi_vector(
+        qs: List[torch.Tensor],
+        ps: List[torch.Tensor],
+        batch_size: int = 128,
+        device: Optional[Union[str, torch.device]] = None,
+    ) -> torch.Tensor:
+        """
+        Compute the MaxSim score (ColBERT-like) for the given multi-vector query and passage embeddings.
+        """
+        device = device or get_torch_device("auto")
+
+        if len(qs) == 0:
+            raise ValueError("No queries provided")
+        if len(ps) == 0:
+            raise ValueError("No passages provided")
+
+        scores_list: List[torch.Tensor] = []
+
+        for i in range(0, len(qs), batch_size):
+            scores_batch = []
+            qs_batch = torch.nn.utils.rnn.pad_sequence(qs[i : i + batch_size], batch_first=True, padding_value=0).to(
+                device
+            )
+            for j in range(0, len(ps), batch_size):
+                ps_batch = torch.nn.utils.rnn.pad_sequence(
+                    ps[j : j + batch_size], batch_first=True, padding_value=0
+                ).to(device)
+                scores_batch.append(torch.einsum("bnd,csd->bcns", qs_batch, ps_batch).max(dim=3)[0].sum(dim=2))
+            scores_batch = torch.cat(scores_batch, dim=1).cpu()
+            scores_list.append(scores_batch)
+
+        scores = torch.cat(scores_list, dim=0)
+        assert scores.shape[0] == len(qs), f"Expected {len(qs)} scores, got {scores.shape[0]}"
+
+        scores = scores.to(torch.float32)
+        return scores
+
+
+
+class QwenVLProcessor(ABC):
+
+    def __call__(
+        self,
+        images: Optional[ImageInput] = None,
+        text: Optional[Union[TextInput, PreTokenizedInput, List[PreTokenizedInput]]] = None,
+        videos: Optional[VideoInput] = None,
+        **kwargs,
+    ) -> BatchFeature:
+        return super().__call__(images=images, text=text, videos=videos, **kwargs)  # type: ignore
+
+    def apply_chat_template(
+        self,
+        conversation: Union[List[Dict[str, str]], List[List[Dict[str, str]]]],
+        chat_template: Optional[str] = None,
+        **kwargs: Unpack[AllKwargsForChatTemplate],
+    ) -> str:
+        return super().apply_chat_template(conversation=conversation, chat_template=chat_template, **kwargs)  # type: ignore
+
+
+class QwenVLEmbeddingProcessorBase(BaseVisualRetrieverProcessor, QwenVLProcessor):
+
+    assistant_prefix_len: int = 58  # length of prefix created by
+    # super().apply_chat_template(conversation=conversation, chat_template=chat_template, **kwargs)
+
+    @staticmethod
+    def round_by_factor(number: float, factor: int) -> int:
+        """Returns the closest integer to 'number' that is divisible by 'factor'."""
+        return round(number / factor) * factor
+
+    @staticmethod
+    def ceil_by_factor(number: float, factor: int) -> int:
+        """Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'."""
+        return math.ceil(number / factor) * factor
+
+    @staticmethod
+    def floor_by_factor(number: float, factor: int) -> int:
+        """Returns the largest integer less than or equal to 'number' that is divisible by 'factor'."""
+        return math.floor(number / factor) * factor
+
+    def process_images(
+        self,
+        images: Union[List[Image.Image], List[List[Image.Image]]],
+    ) -> BatchFeature:
+
+        if isinstance(images[0], list):
+            images = cast(List[List[Image.Image]], images)
+            text_doc = []
+            for i in range(len(images)):
+                conversation = [{"role": "user", "content": [{"type": "image"}] * len(images[i])}]
+                template = self.apply_chat_template(conversation, add_generation_prompt=False)
+                text_doc.append(template[self.assistant_prefix_len :])
+
+        else:
+            images = cast(List[Image.Image], images)
+            text_doc = [
+                "<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>Describe the image.<|im_end|>\n"
+            ] * len(images)
+
+        # The following code is a hack to make sure the scatter in DDP is done correctly when training on multiple GPUs
+        batch_doc = self(text=text_doc, images=images, padding="longest", return_tensors="pt")  # type: ignore
+        # Separate pixel_values for each image
+        offsets = batch_doc["image_grid_thw"][:, 1] * batch_doc["image_grid_thw"][:, 2]
+        # Pad pixel_values to the same length to be able to make it into a tensor
+        pixel_values = torch.split(batch_doc["pixel_values"], offsets.tolist())
+
+        max_length = max([len(pv) for pv in pixel_values])
+
+        pixel_values = [
+            torch.cat([pv, torch.zeros((max_length - len(pv), pv.shape[1]), dtype=pv.dtype, device=pv.device)])
+            for pv in pixel_values
+        ]
+
+        batch_doc["pixel_values"] = torch.stack(pixel_values)
+        return batch_doc
+
+    def process_texts(
+        self,
+        texts: List[str],
+        max_length: int,
+        suffix: Optional[str] = None,
+        prefix: Optional[str] = None,
+        padding: Optional[str] = None,
+    ) -> BatchFeature:
+
+        if suffix is None:
+            suffix = "<pad>" * 10
+
+        padded_texts: List[str] = []
+
+        for text in texts:
+            if prefix:
+                text = f"{prefix}: {text}"
+            text += suffix
+            padded_texts.append(text)
+
+        text_batch = self(
+            text=padded_texts,
+            return_tensors="pt",
+            padding=padding or "longest",
+            max_length=max_length,
+            truncation=True,
+        )
+
+        return text_batch
+
+
+
+
+class ColQwenDuoProcessorBase(QwenVLEmbeddingProcessorBase):
+    """
+    Processor for ColQwenDuo. Mirrors the `ColQwen2Processor` class.
+    """
+
+    def score(
+        self,
+        qs: List[torch.Tensor],
+        ps: List[torch.Tensor],
+        vector_type: str,
+        device: Optional[Union[str, torch.device]] = None,
+        truncate: Optional[int] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        """
+        Compute the MaxSim score (ColBERT-like) for the given multi-vector query and passage embeddings.
+        """
+        if truncate:
+            qs = [q[..., :truncate] for q in qs]
+            ps = [p[..., :truncate] for p in ps]
+
+        if vector_type == "single_vector":
+            return self.score_single_vector(qs, ps, device=device)
+        elif vector_type == "multi_vector":
+            return self.score_multi_vector(qs, ps, device=device, **kwargs)
+        else:
+            raise ValueError('vector_type must be one of the following: [`single_vector`, `multi_vector`]')
+
+
+class ColQwen2DuoProcessor(ColQwenDuoProcessorBase, Qwen2VLProcessor):
+    def __init__(self, *args, **kwargs) -> None:
+        Qwen2VLProcessor.__init__(self, *args, **kwargs)
+

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 +1,209 @@
+{
+  "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": "{%- 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,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "processor_class": "ColQwen25DuoProcessor",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}