Upload ColQwen2

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. -->
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+[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:**
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+[More Information Needed]
+
+**APA:**
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+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
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+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

config.json

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+{
+  "_name_or_path": "vidore/colqwen2-base",
+  "architectures": [
+    "ColQwen2"
+  ],
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoModel": "modeling_colqwen2.ColQwen2"
+  },
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "image_token_id": 151655,
+  "initializer_range": 0.02,
+  "intermediate_size": 8960,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2_vl",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": {
+    "mrope_section": [
+      16,
+      24,
+      24
+    ],
+    "rope_type": "default",
+    "type": "default"
+  },
+  "rope_theta": 1000000.0,
+  "sliding_window": 32768,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.45.2",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "video_token_id": 151656,
+  "vision_config": {
+    "hidden_size": 1536,
+    "in_chans": 3,
+    "model_type": "qwen2_vl",
+    "spatial_patch_size": 14
+  },
+  "vision_end_token_id": 151653,
+  "vision_start_token_id": 151652,
+  "vision_token_id": 151654,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.01,
+  "top_k": 1,
+  "top_p": 0.001,
+  "transformers_version": "4.45.2"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:091a37354d5fddd0abc9184402570c6dc95a4693a54dcebdb8a246252c696e88
+size 4418444496

modeling_colqwen2.py

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+from typing import ClassVar, List, Optional
+
+import torch
+from torch import nn
+from transformers.models.qwen2_vl import Qwen2VLConfig, Qwen2VLForConditionalGeneration
+
+
+class ColQwen2(Qwen2VLForConditionalGeneration):
+    """
+    ColQwen2 model implementation from the "ColPali: Efficient Document Retrieval with Vision Language Models" paper.
+    """
+
+    main_input_name: ClassVar[str] = "doc_input_ids"  # transformers-related
+
+    def __init__(self, config: Qwen2VLConfig):
+        super().__init__(config=config)
+        self.dim = 128
+        self.custom_text_proj = nn.Linear(self.model.config.hidden_size, self.dim)
+        self.padding_side = "left"
+        self.post_init()
+
+
+    def inner_forward(
+            self,
+            input_ids: torch.LongTensor = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_values: Optional[List[torch.FloatTensor]] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            pixel_values: Optional[torch.Tensor] = None,
+            pixel_values_videos: Optional[torch.FloatTensor] = None,
+            image_grid_thw: Optional[torch.LongTensor] = None,
+            video_grid_thw: Optional[torch.LongTensor] = None,
+    ) -> torch.Tensor:
+
+        if inputs_embeds is None:
+            inputs_embeds = self.model.embed_tokens(input_ids)
+            if pixel_values is not None:
+                pixel_values = pixel_values.type(self.visual.get_dtype())
+                image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw)
+                image_mask = (input_ids == self.config.image_token_id).unsqueeze(-1).expand_as(inputs_embeds)
+                image_embeds = image_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
+                inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)
+
+            if pixel_values_videos is not None:
+                pixel_values_videos = pixel_values_videos.type(self.visual.get_dtype())
+                video_embeds = self.visual(pixel_values_videos, grid_thw=video_grid_thw)
+                video_mask = (input_ids == self.config.video_token_id).unsqueeze(-1).expand_as(inputs_embeds)
+                video_embeds = video_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
+                inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds)
+
+            if attention_mask is not None:
+                attention_mask = attention_mask.to(inputs_embeds.device)
+
+        outputs = self.model(
+            input_ids=None,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        return hidden_states
+
+
+
+    def forward(self, *args, **kwargs) -> torch.Tensor:
+        # Delete output_hidden_states from kwargs
+        kwargs.pop("output_hidden_states", None)
+
+        # The following code is a hack to make sure the scatter in DDP is done correctly when training on multiple GPUs
+        if "pixel_values" in kwargs:
+            # compute pixel_values offsets
+            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=kwargs["input_ids"],
+            image_grid_thw=kwargs.get("image_grid_thw", None),
+            video_grid_thw=None,
+            attention_mask=kwargs.get("attention_mask", None),
+        )
+
+        last_hidden_states = self.inner_forward(*args,
+                                  **kwargs,
+                                  position_ids=position_ids,
+                                  use_cache=False,
+                                  output_hidden_states=True)  # (batch_size, sequence_length, hidden_size)
+
+        proj = self.custom_text_proj(last_hidden_states)  # (batch_size, sequence_length, dim)
+
+        # L2 normalization
+        proj = proj / proj.norm(dim=-1, keepdim=True)  # (batch_size, sequence_length, dim)
+        proj = proj * kwargs["attention_mask"].unsqueeze(-1)  # (batch_size, sequence_length, dim)
+        return proj