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Model: daniel3303/QwenStoryteller Source: Original Platform
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README.md
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---
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language: en
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license: apache-2.0
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tags:
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- vision-language-model
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- visual-storytelling
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- chain-of-thought
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- grounded-text-generation
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- cross-frame-consistency
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- storytelling
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- image-to-text
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datasets:
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- daniel3303/StoryReasoning
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metrics:
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- precision
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- recall
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- bleu
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- meteor
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- rouge
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base_model:
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- Qwen/Qwen2.5-VL-7B-Instruct
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pipeline_tag: image-to-text
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model-index:
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- name: QwenStoryteller
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results:
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- task:
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type: visual-storytelling
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name: Visual Storytelling
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dataset:
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name: StoryReasoning
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type: daniel3303/StoryReasoning
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split: test
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metrics:
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- name: Character Precision
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type: precision
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value: 0.83
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- name: Object Precision
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type: precision
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value: 0.46
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- name: Total Precision
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type: precision
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value: 0.57
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- name: mAP
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type: mean_average_precision
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value: 0.27
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- name: Character Recall
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type: recall
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value: 0.62
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- name: Object Recall
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type: recall
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value: 0.25
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- name: Total Recall
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type: recall
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value: 0.40
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- name: METEOR
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type: meteor
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value: 0.14
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- name: ROUGE-L
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type: rouge-l
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value: 0.16
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- name: BLEU-4
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type: bleu-4
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value: 0.054
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- name: Description Accuracy
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type: accuracy
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value: 2.76
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description: "Rating on a scale of 1-5"
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- name: Average Hallucinations
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type: error_rate
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value: 3.56
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description: "Average number of hallucinations per story"
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library_name: transformers
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---
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# QwenStoryteller
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QwenStoryteller is a fine-tuned version of Qwen2.5-VL 7B specialized for grounded visual storytelling with cross-frame consistency, capable of generating coherent narratives from multiple images while maintaining character and object identity throughout the story.
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## Model Description
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**Base Model:** Qwen2.5-VL 7B
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**Training Method:** LoRA fine-tuning (rank 2048, alpha 4096)
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**Training Dataset:** [StoryReasoning](https://huggingface.co/datasets/daniel3303/StoryReasoning)
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QwenStoryteller processes sequences of images to perform:
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- End-to-end object detection
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- Cross-frame object re-identification
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- Landmark detection
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- Chain-of-thought reasoning for scene understanding
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- Grounded story generation with explicit visual references
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The model was fine-tuned on the StoryReasoning dataset using LoRA with a rank of 2048 and alpha scaling factor of 4096, targeting self-attention layers of the language components. Training used a peak learning rate of 1×10⁻⁴ with batch size 32, warmup for the first 3% of steps for 4 epochs, AdamW optimizer with weight decay 0.01, and bfloat16 precision.
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## System Prompt
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The model was trained with the following system prompt, and we recommend using it as it is for inference.
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```
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You are an AI storyteller that can analyze sequences of images and create creative narratives.
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First think step-by-step to analyze characters, objects, settings, and narrative structure.
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Then create a grounded story that maintains consistent character identity and object references across frames.
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Use <think></think> tags to show your reasoning process before writing the final story.
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```
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## Key Features
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- **Cross-Frame Consistency:** Maintains consistent character and object identity across multiple frames through visual similarity and face recognition techniques
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- **Structured Reasoning:** Employs chain-of-thought reasoning to analyze scenes with explicit modeling of characters, objects, settings, and narrative structure
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- **Grounded Storytelling:** Uses specialized XML tags to link narrative elements directly to visual entities
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- **Reduced Hallucinations:** Achieves 12.3% fewer hallucinations compared to the non-fine-tuned base model
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## Usage
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```python
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from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
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from qwen_vl_utils import process_vision_info
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import torch
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from PIL import Image
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# Load the model
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model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
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"daniel3303/QwenStoryteller", torch_dtype="auto", device_map="auto"
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)
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# Load processor
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processor = AutoProcessor.from_pretrained("daniel3303/QwenStoryteller")
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# Load images
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images = [
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Image.open("image1.jpg"),
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Image.open("image2.jpg"),
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Image.open("image3.jpg"),
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Image.open("image4.jpg"),
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Image.open("image5.jpg")
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]
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# Create image content list
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image_content = []
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for img in images:
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image_content.append({
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"type": "image",
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"image": img,
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})
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# Add text prompt at the end
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image_content.append({"type": "text", "text": "Generate a story based on these images."})
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# Create messages with system prompt
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messages = [
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{
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"role": "system",
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"content": "You are an AI storyteller that can analyze sequences of images and create creative narratives. First think step-by-step to analyze characters, objects, settings, and narrative structure. Then create a grounded story that maintains consistent character identity and object references across frames. Use <think></think> tags to show your reasoning process before writing the final story."
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},
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{
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"role": "user",
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"content": image_content,
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}
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]
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# Preparation for inference
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text = processor.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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image_inputs, video_inputs = process_vision_info(messages)
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inputs = processor(
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text=[text],
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images=image_inputs,
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videos=video_inputs,
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padding=True,
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return_tensors="pt",
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)
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inputs = inputs.to(model.device)
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# Inference: Generation of the output
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generated_ids = model.generate(
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**inputs,
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max_new_tokens=4096,
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do_sample=True,
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temperature=0.7,
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top_p=0.9
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)
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generated_ids_trimmed = [
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out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
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]
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story = processor.batch_decode(
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
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)[0]
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print(story)
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```
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### Using vLLM for faster inference
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For significantly faster inference, you can use vLLM to serve the model. Simply install vLLM and run:
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```bash
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# Install vLLM
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pip install vllm
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# Serve the model with vLLM
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vllm serve daniel3303/QwenStoryteller
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```
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## Output Format
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QwenStoryteller produces two main outputs:
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1. **Chain-of-Thought Analysis (`<think></think>`):** A structured analysis containing:
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- Character tables with consistent identity references, emotions, actions, and spatial locations
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- Object tables with functions, interactions, and spatial coordinates
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- Setting tables categorizing environmental elements
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- Narrative structure tables modeling story progression
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2. **Grounded Story:** A narrative with specialized XML tags linking text to visual elements:
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- `<gdi>`: Image tags for specific frames
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- `<gdo>`: Entity reference tags for character and object mentions
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- `<gda>`: Action tags for character actions
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- `<gdl>`: Location/landmark tags for background elements
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## Limitations
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- Re-identification relies primarily on object appearance rather than overall context, which can lead to confusion with similar-looking objects/persons
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- Movie-derived training data introduces biases from cinematic composition that may not generalize to candid visual sequences
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- Low grounding rates for first-person pronouns as they primarily appear in character dialogues
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- May still produce hallucinations, albeit at a reduced rate compared to the base model
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## Citation
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```
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@misc{oliveira2025storyreasoningdatasetusingchainofthought,
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title={StoryReasoning Dataset: Using Chain-of-Thought for Scene Understanding and Grounded Story Generation},
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author={Daniel A. P. Oliveira and David Martins de Matos},
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year={2025},
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eprint={2505.10292},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2505.10292},
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}
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```
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## Contact
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For questions or feedback regarding this model, please contact:
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- Daniel A. P. Oliveira (daniel.oliveira@inesc-id.pt)
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chat_template.json
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chat_template.json
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{
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"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
|
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}
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||||
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config.json
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config.json
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{
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"architectures": [
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"Qwen2_5_VLForConditionalGeneration"
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],
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|
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|
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"max_window_layers": 28,
|
||||
"model_type": "qwen2_5_vl",
|
||||
"num_attention_heads": 28,
|
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"num_hidden_layers": 28,
|
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"num_key_value_heads": 4,
|
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"rms_norm_eps": 1e-06,
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"rope_scaling": {
|
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"mrope_section": [
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"rope_type": "default",
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|
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|
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|
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|
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|
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|
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|
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|
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|
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"vision_config": {
|
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"depth": 32,
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|
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|
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|
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|
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|
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|
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"model_type": "qwen2_5_vl",
|
||||
"num_heads": 16,
|
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|
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"patch_size": 14,
|
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"spatial_merge_size": 2,
|
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"spatial_patch_size": 14,
|
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"temporal_patch_size": 2,
|
||||
"tokens_per_second": 2,
|
||||
"torch_dtype": "bfloat16",
|
||||
"window_size": 112
|
||||
},
|
||||
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|
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|
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|
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"vocab_size": 152064
|
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}
|
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generation_config.json
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generation_config.json
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"repetition_penalty": 1.05,
|
||||
"temperature": 1e-06,
|
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"transformers_version": "4.51.3"
|
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}
|
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31
special_tokens_map.json
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Normal file
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209
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|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"processor_class": "Qwen2_5_VLProcessor",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user