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Model: daniel3303/QwenStoryteller
Source: Original Platform

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README.md

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+---
+language: en
+license: apache-2.0
+tags:
+- vision-language-model
+- visual-storytelling
+- chain-of-thought
+- grounded-text-generation
+- cross-frame-consistency
+- storytelling
+- image-to-text
+datasets:
+- daniel3303/StoryReasoning
+metrics:
+- precision
+- recall
+- bleu
+- meteor
+- rouge
+base_model:
+- Qwen/Qwen2.5-VL-7B-Instruct
+pipeline_tag: image-to-text
+model-index:
+  - name: QwenStoryteller
+    results:
+      - task:
+          type: visual-storytelling
+          name: Visual Storytelling
+        dataset:
+          name: StoryReasoning
+          type: daniel3303/StoryReasoning
+          split: test
+        metrics:
+          - name: Character Precision
+            type: precision
+            value: 0.83
+          - name: Object Precision
+            type: precision
+            value: 0.46
+          - name: Total Precision
+            type: precision
+            value: 0.57
+          - name: mAP
+            type: mean_average_precision
+            value: 0.27
+          - name: Character Recall
+            type: recall
+            value: 0.62
+          - name: Object Recall
+            type: recall
+            value: 0.25
+          - name: Total Recall
+            type: recall
+            value: 0.40
+          - name: METEOR
+            type: meteor
+            value: 0.14
+          - name: ROUGE-L
+            type: rouge-l
+            value: 0.16
+          - name: BLEU-4
+            type: bleu-4
+            value: 0.054
+          - name: Description Accuracy
+            type: accuracy
+            value: 2.76
+            description: "Rating on a scale of 1-5"
+          - name: Average Hallucinations
+            type: error_rate
+            value: 3.56
+            description: "Average number of hallucinations per story"
+library_name: transformers
+---
+
+# QwenStoryteller
+
+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.
+
+## Model Description
+
+**Base Model:** Qwen2.5-VL 7B  
+**Training Method:** LoRA fine-tuning (rank 2048, alpha 4096)  
+**Training Dataset:** [StoryReasoning](https://huggingface.co/datasets/daniel3303/StoryReasoning)
+
+QwenStoryteller processes sequences of images to perform:
+- End-to-end object detection
+- Cross-frame object re-identification
+- Landmark detection
+- Chain-of-thought reasoning for scene understanding
+- Grounded story generation with explicit visual references
+
+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.
+
+## System Prompt
+The model was trained with the following system prompt, and we recommend using it as it is for inference.
+
+```
+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.
+```
+
+## Key Features
+
+- **Cross-Frame Consistency:** Maintains consistent character and object identity across multiple frames through visual similarity and face recognition techniques
+- **Structured Reasoning:** Employs chain-of-thought reasoning to analyze scenes with explicit modeling of characters, objects, settings, and narrative structure
+- **Grounded Storytelling:** Uses specialized XML tags to link narrative elements directly to visual entities
+- **Reduced Hallucinations:** Achieves 12.3% fewer hallucinations compared to the non-fine-tuned base model
+
+## Usage
+
+```python
+from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
+from qwen_vl_utils import process_vision_info
+import torch
+from PIL import Image
+
+# Load the model
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    "daniel3303/QwenStoryteller", torch_dtype="auto", device_map="auto"
+)
+
+# Load processor
+processor = AutoProcessor.from_pretrained("daniel3303/QwenStoryteller")
+
+# Load images
+images = [
+    Image.open("image1.jpg"),
+    Image.open("image2.jpg"),
+    Image.open("image3.jpg"),
+    Image.open("image4.jpg"),
+    Image.open("image5.jpg")
+]
+
+# Create image content list
+image_content = []
+for img in images:
+    image_content.append({
+        "type": "image",
+        "image": img,
+    })
+
+# Add text prompt at the end
+image_content.append({"type": "text", "text": "Generate a story based on these images."})
+
+# Create messages with system prompt
+messages = [
+    {
+        "role": "system", 
+        "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."
+    },
+    {
+        "role": "user",
+        "content": image_content,
+    }
+]
+
+# Preparation for inference
+text = processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+image_inputs, video_inputs = process_vision_info(messages)
+inputs = processor(
+    text=[text],
+    images=image_inputs,
+    videos=video_inputs,
+    padding=True,
+    return_tensors="pt",
+)
+inputs = inputs.to(model.device)
+
+# Inference: Generation of the output
+generated_ids = model.generate(
+    **inputs, 
+    max_new_tokens=4096,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9
+)
+generated_ids_trimmed = [
+    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+]
+story = processor.batch_decode(
+    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)[0]
+
+print(story)
+```
+
+### Using vLLM for faster inference
+
+For significantly faster inference, you can use vLLM to serve the model. Simply install vLLM and run:
+
+```bash
+# Install vLLM
+pip install vllm
+
+# Serve the model with vLLM
+vllm serve daniel3303/QwenStoryteller
+```
+
+## Output Format
+
+QwenStoryteller produces two main outputs:
+
+1. **Chain-of-Thought Analysis (`<think></think>`):** A structured analysis containing:
+   - Character tables with consistent identity references, emotions, actions, and spatial locations
+   - Object tables with functions, interactions, and spatial coordinates
+   - Setting tables categorizing environmental elements
+   - Narrative structure tables modeling story progression
+
+2. **Grounded Story:** A narrative with specialized XML tags linking text to visual elements:
+   - `<gdi>`: Image tags for specific frames
+   - `<gdo>`: Entity reference tags for character and object mentions
+   - `<gda>`: Action tags for character actions
+   - `<gdl>`: Location/landmark tags for background elements
+
+## Limitations
+
+- Re-identification relies primarily on object appearance rather than overall context, which can lead to confusion with similar-looking objects/persons
+- Movie-derived training data introduces biases from cinematic composition that may not generalize to candid visual sequences
+- Low grounding rates for first-person pronouns as they primarily appear in character dialogues
+- May still produce hallucinations, albeit at a reduced rate compared to the base model
+
+## Citation
+
+```
+@misc{oliveira2025storyreasoningdatasetusingchainofthought,
+      title={StoryReasoning Dataset: Using Chain-of-Thought for Scene Understanding and Grounded Story Generation}, 
+      author={Daniel A. P. Oliveira and David Martins de Matos},
+      year={2025},
+      eprint={2505.10292},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2505.10292}, 
+}
+```
+
+## Contact
+
+For questions or feedback regarding this model, please contact:
+- Daniel A. P. Oliveira (daniel.oliveira@inesc-id.pt)

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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+{
+  "architectures": [
+    "Qwen2_5_VLForConditionalGeneration"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "image_token_id": 151655,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "max_position_embeddings": 128000,
+  "max_window_layers": 28,
+  "model_type": "qwen2_5_vl",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "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": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.3",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "video_token_id": 151656,
+  "vision_config": {
+    "depth": 32,
+    "fullatt_block_indexes": [
+      7,
+      15,
+      23,
+      31
+    ],
+    "hidden_act": "silu",
+    "hidden_size": 1280,
+    "in_channels": 3,
+    "in_chans": 3,
+    "intermediate_size": 3420,
+    "model_type": "qwen2_5_vl",
+    "num_heads": 16,
+    "out_hidden_size": 3584,
+    "patch_size": 14,
+    "spatial_merge_size": 2,
+    "spatial_patch_size": 14,
+    "temporal_patch_size": 2,
+    "tokens_per_second": 2,
+    "torch_dtype": "bfloat16",
+    "window_size": 112
+  },
+  "vision_end_token_id": 151653,
+  "vision_start_token_id": 151652,
+  "vision_token_id": 151654,
+  "vocab_size": 152064
+}

generation_config.json

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+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 1e-06,
+  "transformers_version": "4.51.3"
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+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "processor_class": "Qwen2_5_VLProcessor",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}