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Model: prithivMLmods/Camel-Doc-OCR-062825 Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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base_model:
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- Qwen/Qwen2.5-VL-7B-Instruct
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pipeline_tag: image-text-to-text
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library_name: transformers
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tags:
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- Document
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- KIE
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- OCR
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- VL
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- Camel
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- Openpdf
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- text-generation-inference
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- Extraction
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- Linking
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- Markdown
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- .Md
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datasets:
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- prithivMLmods/OpenDoc-Pdf-Preview
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- prithivMLmods/Opendoc1-Analysis-Recognition
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- allenai/olmOCR-mix-0225
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- prithivMLmods/Openpdf-Analysis-Recognition
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license: apache-2.0
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---
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# **Camel-Doc-OCR-062825**
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> The **Camel-Doc-OCR-062825** model is a fine-tuned version of **Qwen2.5-VL-7B-Instruct**, optimized for **Document Retrieval**, **Content Extraction**, and **Analysis Recognition**. Built on top of the Qwen2.5-VL architecture, this model enhances document comprehension capabilities with focused training on the Opendoc2-Analysis-Recognition dataset for superior document analysis and information extraction tasks.
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# Key Enhancements
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* **Context-Aware Multimodal Extraction and Linking for Documents**: Advanced capability for understanding document context and establishing connections between multimodal elements within documents.
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* **Enhanced Document Retrieval**: Designed to efficiently locate and extract relevant information from complex document structures and layouts.
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* **Superior Content Extraction**: Optimized for precise extraction of structured and unstructured content from diverse document formats.
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* **Analysis Recognition**: Specialized in recognizing and interpreting analytical content, charts, tables, and visual data representations.
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* **State-of-the-Art Performance Across Resolutions**: Achieves competitive results on OCR and visual QA benchmarks such as DocVQA, MathVista, RealWorldQA, and MTVQA.
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* **Video Understanding up to 20+ minutes**: Supports detailed comprehension of long-duration videos for content summarization, Q\&A, and multi-modal reasoning.
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* **Visually-Grounded Device Interaction**: Enables mobile/robotic device operation via visual inputs and text-based instructions using contextual understanding and decision-making logic.
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# Quick Start with Transformers
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```python
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from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
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from qwen_vl_utils import process_vision_info
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model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
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"prithivMLmods/Camel-Doc-OCR-062825", torch_dtype="auto", device_map="auto"
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)
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processor = AutoProcessor.from_pretrained("prithivMLmods/Camel-Doc-OCR-062825")
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "image",
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"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
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},
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{"type": "text", "text": "Describe this image."},
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],
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}
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]
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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("cuda")
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generated_ids = model.generate(**inputs, max_new_tokens=128)
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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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output_text = processor.batch_decode(
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
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)
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print(output_text)
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```
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# Intended Use
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This model is intended for:
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* Context-aware multimodal extraction and linking for complex document structures.
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* High-fidelity document retrieval and content extraction from various document formats.
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* Analysis recognition of charts, graphs, tables, and visual data representations.
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* Document-based question answering for educational and enterprise applications.
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* Extraction and LaTeX formatting of mathematical expressions from printed or handwritten content.
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* Retrieval and summarization from long documents, slides, and multi-modal inputs.
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* Multilingual document analysis and structured content extraction for global use cases.
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* Robotic or mobile automation with vision-guided contextual interaction.
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# Limitations
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* May show degraded performance on extremely low-quality or occluded images.
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* Not optimized for real-time applications on low-resource or edge devices due to computational demands.
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* Variable accuracy on uncommon or low-resource languages/scripts.
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* Long video processing may require substantial memory and is not optimized for streaming applications.
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* Visual token settings affect performance; suboptimal configurations can impact results.
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* In rare cases, outputs may contain hallucinated or contextually misaligned information.
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## Training Details
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| Parameter | Value |
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|-------------------------|-----------------------------------------------------|
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| **Dataset Size** | 108K samples (Modular Combustion of Datasets) |
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| **Model Architecture** | `Qwen2_5_VLForConditionalGeneration` |
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| **Total Disk Volume** | 300,000 MB |
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| **Training Time** | approx. 12,897 seconds (~3.58 hours) |
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| **Warmup Steps** | 750 |
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| **Precision** | bfloat16 |
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## References
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- **DocVLM: Make Your VLM an Efficient Reader**
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[https://arxiv.org/pdf/2412.08746v1](https://arxiv.org/pdf/2412.08746v1)
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- **YaRN: Efficient Context Window Extension of Large Language Models**
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[https://arxiv.org/pdf/2309.00071](https://arxiv.org/pdf/2309.00071)
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|
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- **Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution**
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[https://arxiv.org/pdf/2409.12191](https://arxiv.org/pdf/2409.12191)
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- **Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond**
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[https://arxiv.org/pdf/2308.12966](https://arxiv.org/pdf/2308.12966)
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|
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- **A Comprehensive and Challenging OCR Benchmark for Evaluating Large Multimodal Models in Literacy**
|
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[https://arxiv.org/pdf/2412.02210](https://arxiv.org/pdf/2412.02210)
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added_tokens.json
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chat_template.jinja
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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
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You are a helpful assistant.<|im_end|>
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{% endif %}<|im_start|>{{ message['role'] }}
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{% if message['content'] is string %}{{ message['content'] }}<|im_end|>
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{% 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|>
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{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant
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{% endif %}
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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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"num_key_value_heads": 4,
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"rope_type": "default",
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"type": "default"
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"rope_theta": 1000000.0,
|
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"sliding_window": 32768,
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"text_config": {
|
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"architectures": [
|
||||
"Qwen2_5_VLForConditionalGeneration"
|
||||
],
|
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"attention_dropout": 0.0,
|
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"hidden_act": "silu",
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|
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|
||||
"model_type": "qwen2_5_vl_text",
|
||||
"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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16,
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24,
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],
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"rope_type": "default",
|
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"type": "default"
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|
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|
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"torch_dtype": "bfloat16",
|
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"use_cache": false,
|
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"use_sliding_window": false,
|
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"vocab_size": 152064
|
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},
|
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"tie_word_embeddings": false,
|
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"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.52.4",
|
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"use_cache": false,
|
||||
"use_sliding_window": false,
|
||||
"video_token_id": 151656,
|
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"vision_config": {
|
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"depth": 32,
|
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"fullatt_block_indexes": [
|
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|
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],
|
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"hidden_act": "silu",
|
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"hidden_size": 1280,
|
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"in_channels": 3,
|
||||
"in_chans": 3,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 3420,
|
||||
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|
||||
"num_heads": 16,
|
||||
"out_hidden_size": 3584,
|
||||
"patch_size": 14,
|
||||
"spatial_merge_size": 2,
|
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"spatial_patch_size": 14,
|
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"temporal_patch_size": 2,
|
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"tokens_per_second": 2,
|
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"torch_dtype": "bfloat16",
|
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},
|
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|
||||
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|
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|
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}
|
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configuration.json
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configuration.json
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{"framework": "pytorch", "task": "image-text-to-text", "allow_remote": true}
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generation_config.json
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generation_config.json
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{
|
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"bos_token_id": 151643,
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|
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"repetition_penalty": 1.05,
|
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"temperature": 1e-06,
|
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"transformers_version": "4.52.4"
|
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}
|
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31
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209
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|
||||
"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,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"padding_side": "right",
|
||||
"processor_class": "Qwen2_5_VLProcessor",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
86
video_preprocessor_config.json
Normal file
86
video_preprocessor_config.json
Normal file
@@ -0,0 +1,86 @@
|
||||
{
|
||||
"_valid_kwargs_names": [
|
||||
"do_convert_rgb",
|
||||
"do_resize",
|
||||
"size",
|
||||
"size_divisor",
|
||||
"default_to_square",
|
||||
"resample",
|
||||
"do_rescale",
|
||||
"rescale_factor",
|
||||
"do_normalize",
|
||||
"image_mean",
|
||||
"image_std",
|
||||
"do_pad",
|
||||
"do_center_crop",
|
||||
"crop_size",
|
||||
"data_format",
|
||||
"input_data_format",
|
||||
"device",
|
||||
"min_pixels",
|
||||
"max_pixels",
|
||||
"patch_size",
|
||||
"temporal_patch_size",
|
||||
"merge_size"
|
||||
],
|
||||
"crop_size": null,
|
||||
"data_format": "channels_first",
|
||||
"default_to_square": true,
|
||||
"device": null,
|
||||
"do_center_crop": null,
|
||||
"do_convert_rgb": true,
|
||||
"do_normalize": true,
|
||||
"do_pad": null,
|
||||
"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
|
||||
],
|
||||
"input_data_format": null,
|
||||
"max_pixels": 12845056,
|
||||
"merge_size": 2,
|
||||
"min_pixels": 3136,
|
||||
"model_valid_processing_keys": [
|
||||
"do_convert_rgb",
|
||||
"do_resize",
|
||||
"size",
|
||||
"size_divisor",
|
||||
"default_to_square",
|
||||
"resample",
|
||||
"do_rescale",
|
||||
"rescale_factor",
|
||||
"do_normalize",
|
||||
"image_mean",
|
||||
"image_std",
|
||||
"do_pad",
|
||||
"do_center_crop",
|
||||
"crop_size",
|
||||
"data_format",
|
||||
"input_data_format",
|
||||
"device",
|
||||
"min_pixels",
|
||||
"max_pixels",
|
||||
"patch_size",
|
||||
"temporal_patch_size",
|
||||
"merge_size"
|
||||
],
|
||||
"patch_size": 14,
|
||||
"processor_class": "Qwen2_5_VLProcessor",
|
||||
"resample": 3,
|
||||
"rescale_factor": 0.00392156862745098,
|
||||
"size": {
|
||||
"longest_edge": 12845056,
|
||||
"shortest_edge": 3136
|
||||
},
|
||||
"size_divisor": null,
|
||||
"temporal_patch_size": 2,
|
||||
"video_processor_type": "Qwen2VLVideoProcessor"
|
||||
}
|
||||
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