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---
license: apache-2.0
language:
- en
- zh
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- text-generation-inference
- trl
- ocr
- vision-language
- reasoning
- grounded-visual-reasoning
- sft
- grpo
- thinking
- code
- thinking=1
---
![16.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/6lJr_Qh_af4nETzzaS74F.png)
# **Lumian-VLR-7B-Thinking**
> The **Lumian-VLR-7B-Thinking** model is a high-fidelity vision-language reasoning (experimental model) system designed for fine-grained multimodal understanding. Built on **Qwen2.5-VL-7B-Instruct**, this model enhances image captioning, sampled video reasoning, and document comprehension through explicit grounded reasoning. It produces structured reasoning traces aligned with visual coordinates, enabling explainable multimodal reasoning. Trained via supervised fine-tuning (SFT) on visually-grounded reasoning traces and further refined using GRPO reinforcement learning, Lumian delivers superior step-by-step chain-of-thought reasoning with strong visual grounding.
> [!NOTE]
*Model Subfolder:* [Lumian-VLR-7B-Thinking(think-preview)](https://huggingface.co/prithivMLmods/Lumian-VLR-7B-Thinking/tree/main/think-preview)
>
> *Model Folder:* [Lumian-VLR-7B-Thinking(no-think-single-shot)](https://huggingface.co/prithivMLmods/Lumian-VLR-7B-Thinking/tree/main/)
## Quick Start with Transformers(think-preview)🤗
```py
pip install git+https://github.com/huggingface/transformers.git
```
```py
# Load Lumian-VLR-7B-Thinking
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
MODEL_ID = "prithivMLmods/Lumian-VLR-7B-Thinking"
SUBFOLDER = "think-preview"
processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True, subfolder=SUBFOLDER)
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
MODEL_ID,
trust_remote_code=True,
subfolder=SUBFOLDER,
torch_dtype=torch.float16
).to(device).eval()
```
## Key Enhancements
* **Visually-Grounded Reasoning and Thinking Traces**: Generates explicit reasoning traces tied to image regions and document structures for transparent and explainable outputs.
* **Advanced Image Captioning**: Produces detailed, grounded captions with reasoning steps for improved scene understanding.
* **Sampled Video Reasoning**: Handles long-duration videos with temporal reasoning for question answering and summarization.
* **Context-Aware Document Analysis**: Excels at structured and unstructured content extraction with visual grounding.
* **Fine-Grained Visual Grounding**: Accurately links reasoning steps to tables, charts, and graphical elements.
* **Reinforcement-Learned Thinking**: GRPO training incentivizes accurate, grounded reasoning with minimal hallucinations.
> [!TIP]
Colab Demo : https://huggingface.co/prithivMLmods/Lumian-VLR-7B-Thinking/blob/main/think-preview/Lumian-VLR-7B-Thinking-Demo-Notebook/Lumian-VLR-7B-Thinking.ipynb
## Thinking Traces
The model outputs reasoning and answers in a structured format:
```
<think>
Step 1: Identify the main elements in the image and their positions.
Step 2: Analyze the relationships between objects and surrounding context.
Step 3: Derive the final answer based on spatial reasoning and visual cues.
</think>
<answer>
The image depicts a person holding an open book with highlighted sections on the left page.
</answer>
```
## Quick Start with Transformers(single-shot)
```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
"prithivMLmods/Lumian-VLR-7B-Thinking", torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained("prithivMLmods/Lumian-VLR-7B-Thinking")
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
},
{"type": "text", "text": "Describe this image with thinking traces."},
],
}
]
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("cuda")
generated_ids = model.generate(**inputs, max_new_tokens=256)
generated_ids_trimmed = [
out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
```
## Intended Use
* Visual reasoning with grounded, step-by-step thinking traces.
* Explainable image captioning and sampled video reasoning.
* Multimodal document retrieval, extraction, and analytical interpretation.
* Transparent chain-of-thought reasoning for educational, research, and enterprise use.
* Multilingual reasoning and structured content extraction.
* Robotic and mobile vision-based automation with grounded decision-making.
## Limitations
* High memory requirements for long videos and large document batches.
* Degraded accuracy on extremely low-resolution or obscured visuals.
* Suboptimal for real-time inference on edge devices.
* Visual token configuration strongly influences reasoning fidelity.
* Occasional reasoning drift or partial grounding errors.
## References
* **YaRN: Efficient Context Window Extension of Large Language Models**
* **Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution**
* **Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond**
* **A Comprehensive and Challenging OCR Benchmark for Evaluating Large Multimodal Models in Literacy**
* **Ground-R1: Incentivizing Grounded Visual Reasoning via Reinforcement Learning**

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{% 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
A conversation between User and Assistant. The User asks a question, and the Assistant solves it. The Assistant systematically reasons through the problem step by step by checking and verifying possible solutions and image regions, while grounding reasoning steps to specific objects and their relationships in the image using (x,y) coordinates. There may be one image or two images concatenated together, in which case the Assistant must compare the spatial relationships between the two images.
All reasoning processes must be enclosed within a single set of '<think>' tags, and reasoning steps must include specific reference coordinates:
For example, <think>
{Reasoning text}. {Further reasoning text} {more reasoning}
</think>
The final answer should be enclosed in '<answer>' tags in the format:
<answer> {text of selected answer choice} </answer>
The Assistant must help the user identify the correct answer choice from the options provided.
-If the correct answer is unclear, select the most relevant option based on the spatial relationships and dynamics within the image.
- The Assistant should verify each step and check multiple possible solutions before selecting the final answer.<|im_end|>
{% endif %}<|im_start|>{{ message['role'] }}
{% if message['content'] is string %}{{ message['content'] }}<|im_end|>
{% 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|>
{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant
{% endif %}

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37
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{
"crop_size": null,
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"device": null,
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"temporal_patch_size": 2
}

31
special_tokens_map.json Normal file
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{
"additional_special_tokens": [
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381
think-preview/Lumian-VLR-7B-Thinking-Demo-Notebook/Lumian-VLR-7B-Thinking.ipynb Normal file
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@@ -0,0 +1,381 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "uFovmijgUV1Z"
},
"source": [
"***Multimodal Thinking ReportLab : Lumian-VLR-7B-Thinking***\n",
"\n",
"\n",
"*explicit grounded reasoning* ~*notebook by : [prithivMLmods](https://huggingface.co/prithivMLmods)🤗 x ❤️*"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RugX4SGZV-8O"
},
"source": [
"***Installing all necessary packages***"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "l-NtFtjSpuJQ"
},
"outputs": [],
"source": [
"%%capture\n",
"!pip install gradio transformers transformers-stream-generator qwen-vl-utils\n",
"!pip install torchvision torch huggingface_hub spaces accelerate ipython\n",
"!pip install pillow av python-docx requests numpy reportlab fpdf hf_xet\n",
"#Hold tight, this will take around 3-5 minutes."
]
},
{
"cell_type": "markdown",
"source": [
">\n",
"*Model Subfolder:* [Lumian-VLR-7B-Thinking(think-preview)](https://huggingface.co/prithivMLmods/Lumian-VLR-7B-Thinking/tree/main/think-preview) \n",
"\n",
"*Model Folder:* [Lumian-VLR-7B-Thinking(no-think-single-shot)](https://huggingface.co/prithivMLmods/Lumian-VLR-7B-Thinking/tree/main/)\n"
],
"metadata": {
"id": "g3TKo5gpEROZ"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "mvoSnRZcVBu4"
},
"source": [
"***Run app***"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "tElKr2Fkp1bO"
},
"outputs": [],
"source": [
"# ================================================\n",
"# Model Configuration\n",
"# ================================================\n",
"\n",
"# Model used in the app:\n",
"# https://huggingface.co/prithivMLmods/Lumian-VLR-7B-Thinking\n",
"\n",
"import gradio as gr\n",
"import spaces\n",
"from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor, TextIteratorStreamer\n",
"from qwen_vl_utils import process_vision_info\n",
"import torch\n",
"from PIL import Image\n",
"import os\n",
"import uuid\n",
"import io\n",
"from threading import Thread\n",
"from reportlab.lib.pagesizes import A4\n",
"from reportlab.lib.styles import getSampleStyleSheet\n",
"from reportlab.lib import colors\n",
"from reportlab.platypus import SimpleDocTemplate, Image as RLImage, Paragraph, Spacer\n",
"from reportlab.lib.units import inch\n",
"from reportlab.pdfbase import pdfmetrics\n",
"from reportlab.pdfbase.ttfonts import TTFont\n",
"import docx\n",
"from docx.enum.text import WD_ALIGN_PARAGRAPH\n",
"\n",
"# Define model options\n",
"MODEL_OPTIONS = {\n",
" \"Lumian-VLR-7B-Thinking\": \"prithivMLmods/Lumian-VLR-7B-Thinking\",\n",
"}\n",
"SUBFOLDER = \"think-preview\"\n",
"# Preload models and processors into CUDA\n",
"models = {}\n",
"processors = {}\n",
"for name, model_id in MODEL_OPTIONS.items():\n",
" print(f\"Loading {name}🤗. Hold tight, this will take around 4-6 minutes..\")\n",
" models[name] = Qwen2_5_VLForConditionalGeneration.from_pretrained(\n",
" model_id,\n",
" trust_remote_code=True,\n",
" subfolder=SUBFOLDER,\n",
" torch_dtype=torch.float16\n",
" ).to(\"cuda\").eval()\n",
" processors[name] = AutoProcessor.from_pretrained(model_id, trust_remote_code=True, subfolder=SUBFOLDER)\n",
"\n",
"image_extensions = Image.registered_extensions()\n",
"\n",
"def identify_and_save_blob(blob_path):\n",
" \"\"\"Identifies if the blob is an image and saves it.\"\"\"\n",
" try:\n",
" with open(blob_path, 'rb') as file:\n",
" blob_content = file.read()\n",
" try:\n",
" Image.open(io.BytesIO(blob_content)).verify() # Check if it's a valid image\n",
" extension = \".png\" # Default to PNG for saving\n",
" media_type = \"image\"\n",
" except (IOError, SyntaxError):\n",
" raise ValueError(\"Unsupported media type. Please upload a valid image.\")\n",
"\n",
" filename = f\"temp_{uuid.uuid4()}_media{extension}\"\n",
" with open(filename, \"wb\") as f:\n",
" f.write(blob_content)\n",
"\n",
" return filename, media_type\n",
"\n",
" except FileNotFoundError:\n",
" raise ValueError(f\"The file {blob_path} was not found.\")\n",
" except Exception as e:\n",
" raise ValueError(f\"An error occurred while processing the file: {e}\")\n",
"\n",
"@spaces.GPU\n",
"def qwen_inference(model_name, media_input, text_input=None):\n",
" \"\"\"Handles inference for the selected model.\"\"\"\n",
" model = models[model_name]\n",
" processor = processors[model_name]\n",
"\n",
" if isinstance(media_input, str):\n",
" media_path = media_input\n",
" if media_path.endswith(tuple([i for i in image_extensions.keys()])):\n",
" media_type = \"image\"\n",
" else:\n",
" try:\n",
" media_path, media_type = identify_and_save_blob(media_input)\n",
" except Exception as e:\n",
" raise ValueError(\"Unsupported media type. Please upload a valid image.\")\n",
"\n",
" messages = [\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": [\n",
" {\n",
" \"type\": media_type,\n",
" media_type: media_path\n",
" },\n",
" {\"type\": \"text\", \"text\": text_input},\n",
" ],\n",
" }\n",
" ]\n",
"\n",
" text = processor.apply_chat_template(\n",
" messages, tokenize=False, add_generation_prompt=True\n",
" )\n",
" image_inputs, _ = process_vision_info(messages)\n",
" inputs = processor(\n",
" text=[text],\n",
" images=image_inputs,\n",
" padding=True,\n",
" return_tensors=\"pt\",\n",
" ).to(\"cuda\")\n",
"\n",
" streamer = TextIteratorStreamer(\n",
" processor.tokenizer, skip_prompt=True, skip_special_tokens=True\n",
" )\n",
" generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024)\n",
"\n",
" thread = Thread(target=model.generate, kwargs=generation_kwargs)\n",
" thread.start()\n",
"\n",
" buffer = \"\"\n",
" for new_text in streamer:\n",
" buffer += new_text\n",
" # Remove <|im_end|> or similar tokens from the output\n",
" buffer = buffer.replace(\"<|im_end|>\", \"\")\n",
" yield buffer\n",
"\n",
"def format_plain_text(output_text):\n",
" \"\"\"Formats the output text as plain text without LaTeX delimiters.\"\"\"\n",
" # Remove LaTeX delimiters and convert to plain text\n",
" plain_text = output_text.replace(\"\\\\(\", \"\").replace(\"\\\\)\", \"\").replace(\"\\\\[\", \"\").replace(\"\\\\]\", \"\")\n",
" return plain_text\n",
"\n",
"def generate_document(media_path, output_text, file_format, font_size, line_spacing, alignment, image_size):\n",
" \"\"\"Generates a document with the input image and plain text output.\"\"\"\n",
" plain_text = format_plain_text(output_text)\n",
" if file_format == \"pdf\":\n",
" return generate_pdf(media_path, plain_text, font_size, line_spacing, alignment, image_size)\n",
" elif file_format == \"docx\":\n",
" return generate_docx(media_path, plain_text, font_size, line_spacing, alignment, image_size)\n",
"\n",
"def generate_pdf(media_path, plain_text, font_size, line_spacing, alignment, image_size):\n",
" \"\"\"Generates a PDF document.\"\"\"\n",
" filename = f\"output_{uuid.uuid4()}.pdf\"\n",
" doc = SimpleDocTemplate(\n",
" filename,\n",
" pagesize=A4,\n",
" rightMargin=inch,\n",
" leftMargin=inch,\n",
" topMargin=inch,\n",
" bottomMargin=inch\n",
" )\n",
" styles = getSampleStyleSheet()\n",
" styles[\"Normal\"].fontSize = int(font_size)\n",
" styles[\"Normal\"].leading = int(font_size) * line_spacing\n",
" styles[\"Normal\"].alignment = {\n",
" \"Left\": 0,\n",
" \"Center\": 1,\n",
" \"Right\": 2,\n",
" \"Justified\": 4\n",
" }[alignment]\n",
"\n",
" story = []\n",
"\n",
" # Add image with size adjustment\n",
" image_sizes = {\n",
" \"Small\": (200, 200),\n",
" \"Medium\": (400, 400),\n",
" \"Large\": (600, 600)\n",
" }\n",
" img = RLImage(media_path, width=image_sizes[image_size][0], height=image_sizes[image_size][1])\n",
" story.append(img)\n",
" story.append(Spacer(1, 12))\n",
"\n",
" # Add plain text output\n",
" text = Paragraph(plain_text, styles[\"Normal\"])\n",
" story.append(text)\n",
"\n",
" doc.build(story)\n",
" return filename\n",
"\n",
"def generate_docx(media_path, plain_text, font_size, line_spacing, alignment, image_size):\n",
" \"\"\"Generates a DOCX document.\"\"\"\n",
" filename = f\"output_{uuid.uuid4()}.docx\"\n",
" doc = docx.Document()\n",
"\n",
" # Add image with size adjustment\n",
" image_sizes = {\n",
" \"Small\": docx.shared.Inches(2),\n",
" \"Medium\": docx.shared.Inches(4),\n",
" \"Large\": docx.shared.Inches(6)\n",
" }\n",
" doc.add_picture(media_path, width=image_sizes[image_size])\n",
" doc.add_paragraph()\n",
"\n",
" # Add plain text output\n",
" paragraph = doc.add_paragraph()\n",
" paragraph.paragraph_format.line_spacing = line_spacing\n",
" paragraph.paragraph_format.alignment = {\n",
" \"Left\": WD_ALIGN_PARAGRAPH.LEFT,\n",
" \"Center\": WD_ALIGN_PARAGRAPH.CENTER,\n",
" \"Right\": WD_ALIGN_PARAGRAPH.RIGHT,\n",
" \"Justified\": WD_ALIGN_PARAGRAPH.JUSTIFY\n",
" }[alignment]\n",
" run = paragraph.add_run(plain_text)\n",
" run.font.size = docx.shared.Pt(int(font_size))\n",
"\n",
" doc.save(filename)\n",
" return filename\n",
"\n",
"# CSS for output styling\n",
"css = \"\"\"\n",
" #output {\n",
" height: 500px;\n",
" overflow: auto;\n",
" border: 1px solid #ccc;\n",
" }\n",
".submit-btn {\n",
" background-color: #cf3434 !important;\n",
" color: white !important;\n",
"}\n",
".submit-btn:hover {\n",
" background-color: #ff2323 !important;\n",
"}\n",
".download-btn {\n",
" background-color: #35a6d6 !important;\n",
" color: white !important;\n",
"}\n",
".download-btn:hover {\n",
" background-color: #22bcff !important;\n",
"}\n",
"\"\"\"\n",
"\n",
"# Gradio app setup\n",
"with gr.Blocks(css=css, theme=\"bethecloud/storj_theme\") as demo:\n",
" gr.Markdown(\"# **Multimodal-Thinking : Lumian-VLR-7B-Thinking**\")\n",
"\n",
" with gr.Tab(label=\"Image Input\"):\n",
"\n",
" with gr.Row():\n",
" with gr.Column():\n",
" model_choice = gr.Dropdown(\n",
" label=\"Model Selection\",\n",
" choices=list(MODEL_OPTIONS.keys()),\n",
" value=\"Lumian-VLR-7B-Thinking\"\n",
" )\n",
" input_media = gr.File(\n",
" label=\"Upload Image\", type=\"filepath\"\n",
" )\n",
" text_input = gr.Textbox(label=\"Question\", value=\"OCR the image precisely.\")\n",
" submit_btn = gr.Button(value=\"Submit\", elem_classes=\"submit-btn\")\n",
"\n",
" with gr.Column():\n",
" output_text = gr.Textbox(label=\"Output Text\", lines=7)\n",
"\n",
" with gr.Accordion(\"Plain Text\", open=False):\n",
" plain_text_output = gr.Textbox(label=\"Standardized Plain Text\", lines=10)\n",
"\n",
" submit_btn.click(\n",
" qwen_inference, [model_choice, input_media, text_input], [output_text]\n",
" ).then(\n",
" lambda output_text: format_plain_text(output_text), [output_text], [plain_text_output]\n",
" )\n",
"\n",
" with gr.Accordion(\"Docx/PDF Settings\", open=False):\n",
" with gr.Row():\n",
" with gr.Column():\n",
" line_spacing = gr.Dropdown(\n",
" choices=[0.5, 1.0, 1.15, 1.5, 2.0, 2.5, 3.0],\n",
" value=1.5,\n",
" label=\"Line Spacing\"\n",
" )\n",
" font_size = gr.Dropdown(\n",
" choices=[\"8\", \"10\", \"12\", \"14\", \"16\", \"18\", \"20\", \"22\", \"24\"],\n",
" value=\"16\",\n",
" label=\"Font Size\"\n",
" )\n",
" alignment = gr.Dropdown(\n",
" choices=[\"Left\", \"Center\", \"Right\", \"Justified\"],\n",
" value=\"Justified\",\n",
" label=\"Text Alignment\"\n",
" )\n",
" image_size = gr.Dropdown(\n",
" choices=[\"Small\", \"Medium\", \"Large\"],\n",
" value=\"Medium\",\n",
" label=\"Image Size\"\n",
" )\n",
" file_format = gr.Radio([\"pdf\", \"docx\"], label=\"File Format\", value=\"pdf\")\n",
"\n",
" get_document_btn = gr.Button(value=\"Get Document\", elem_classes=\"download-btn\")\n",
"\n",
" get_document_btn.click(\n",
" generate_document, [input_media, output_text, file_format, font_size, line_spacing, alignment, image_size], gr.File(label=\"Download Document\")\n",
" )\n",
"\n",
"demo.launch(debug=True)"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"gpuType": "T4",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

24
think-preview/added_tokens.json Normal file
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{
"</tool_call>": 151658,
"<tool_call>": 151657,
"<|box_end|>": 151649,
"<|box_start|>": 151648,
"<|endoftext|>": 151643,
"<|file_sep|>": 151664,
"<|fim_middle|>": 151660,
"<|fim_pad|>": 151662,
"<|fim_prefix|>": 151659,
"<|fim_suffix|>": 151661,
"<|im_end|>": 151645,
"<|im_start|>": 151644,
"<|image_pad|>": 151655,
"<|object_ref_end|>": 151647,
"<|object_ref_start|>": 151646,
"<|quad_end|>": 151651,
"<|quad_start|>": 151650,
"<|repo_name|>": 151663,
"<|video_pad|>": 151656,
"<|vision_end|>": 151653,
"<|vision_pad|>": 151654,
"<|vision_start|>": 151652
}

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think-preview/chat_template.jinja Normal file
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{% 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
A conversation between User and Assistant. The User asks a question, and the Assistant solves it. The Assistant systematically reasons through the problem step by step by checking and verifying possible solutions and image regions, while grounding reasoning steps to specific objects and their relationships in the image using (x,y) coordinates. There may be one image or two images concatenated together, in which case the Assistant must compare the spatial relationships between the two images.
All reasoning processes must be enclosed within a single set of '<think>' tags, and reasoning steps must include specific reference coordinates:
For example, <think>
{Reasoning text}. {Further reasoning text} {more reasoning}
</think>
The final answer should be enclosed in '<answer>' tags in the format:
<answer> {text of selected answer choice} </answer>
The Assistant must help the user identify the correct answer choice from the options provided.
-If the correct answer is unclear, select the most relevant option based on the spatial relationships and dynamics within the image.
- The Assistant should verify each step and check multiple possible solutions before selecting the final answer.<|im_end|>
{% endif %}<|im_start|>{{ message['role'] }}
{% if message['content'] is string %}{{ message['content'] }}<|im_end|>
{% 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|>
{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant
{% endif %}

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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,
"pad_token_id": 151643,
"rms_norm_eps": 1e-06,
"rope_scaling": {
"mrope_section": [
16,
24,
24
],
"rope_type": "default",
"type": "default"
},
"rope_theta": 1000000.0,
"sliding_window": 32768,
"text_config": {
"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": null,
"initializer_range": 0.02,
"intermediate_size": 18944,
"layer_types": [
"full_attention",
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"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
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"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention"
],
"max_position_embeddings": 128000,
"max_window_layers": 28,
"model_type": "qwen2_5_vl_text",
"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": null,
"torch_dtype": "bfloat16",
"use_cache": false,
"use_sliding_window": false,
"video_token_id": null,
"vision_end_token_id": 151653,
"vision_start_token_id": 151652,
"vision_token_id": 151654,
"vocab_size": 152064
},
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.54.1",
"use_cache": false,
"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,
"initializer_range": 0.02,
"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
}

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{
"bos_token_id": 151643,
"do_sample": true,
"eos_token_id": [
151645,
151643
],
"max_length": 128000,
"pad_token_id": 151643,
"repetition_penalty": 1.05,
"temperature": 1e-06,
"transformers_version": "4.54.1"
}

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