Update README.md

README.md

@@ -18,4 +18,124 @@ datasets:
 license: apache-2.0
 ---
 
-![Camel.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/IkSg6Ubp8AKrPsXXgD8cQ.png)
+![Camel.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/IkSg6Ubp8AKrPsXXgD8cQ.png)
+
+# **Camel-Doc-OCR-062825**
+
+> 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.
+
+# Key Enhancements
+
+* **Context-Aware Multimodal Extraction and Linking for Documents**: Advanced capability for understanding document context and establishing connections between multimodal elements within documents.
+
+* **Enhanced Document Retrieval**: Designed to efficiently locate and extract relevant information from complex document structures and layouts.
+
+* **Superior Content Extraction**: Optimized for precise extraction of structured and unstructured content from diverse document formats.
+
+* **Analysis Recognition**: Specialized in recognizing and interpreting analytical content, charts, tables, and visual data representations.
+
+* **State-of-the-Art Performance Across Resolutions**: Achieves competitive results on OCR and visual QA benchmarks such as DocVQA, MathVista, RealWorldQA, and MTVQA.
+
+* **Video Understanding up to 20+ minutes**: Supports detailed comprehension of long-duration videos for content summarization, Q\&A, and multi-modal reasoning.
+
+* **Visually-Grounded Device Interaction**: Enables mobile/robotic device operation via visual inputs and text-based instructions using contextual understanding and decision-making logic.
+
+# Quick Start with Transformers
+
+```python
+from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
+from qwen_vl_utils import process_vision_info
+
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    "prithivMLmods/Camel-Doc-OCR-062825", torch_dtype="auto", device_map="auto"
+)
+
+processor = AutoProcessor.from_pretrained("prithivMLmods/Camel-Doc-OCR-062825")
+
+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."},
+        ],
+    }
+]
+
+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=128)
+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)
+```
+
+## Training Details
+
+| Parameter               | Value                                               |
+|-------------------------|-----------------------------------------------------|
+| **Dataset Size**        | 108K samples (Modular Combustion of Datasets)      |
+| **Model Architecture**  | `Qwen2_5_VLForConditionalGeneration`                |
+| **Hardware**            | 3 × A40 (27 vCPUs) (144 GB VRAM) (150 GB RAM)       |
+| **Total Disk Volume**   | 300,000 MB                                          |
+| **Training Time**       | approx.	~12,897 seconds (~3.58 hours)               |
+| **Warmup Steps**        | 750                                                 |
+| **Precision**           | bfloat16                                            |
+
+# Intended Use
+
+This model is intended for:
+
+* Context-aware multimodal extraction and linking for complex document structures.
+* High-fidelity document retrieval and content extraction from various document formats.
+* Analysis recognition of charts, graphs, tables, and visual data representations.
+* Document-based question answering for educational and enterprise applications.
+* Extraction and LaTeX formatting of mathematical expressions from printed or handwritten content.
+* Retrieval and summarization from long documents, slides, and multi-modal inputs.
+* Multilingual document analysis and structured content extraction for global use cases.
+* Robotic or mobile automation with vision-guided contextual interaction.
+
+# Limitations
+
+* May show degraded performance on extremely low-quality or occluded images.
+* Not optimized for real-time applications on low-resource or edge devices due to computational demands.
+* Variable accuracy on uncommon or low-resource languages/scripts.
+* Long video processing may require substantial memory and is not optimized for streaming applications.
+* Visual token settings affect performance; suboptimal configurations can impact results.
+* In rare cases, outputs may contain hallucinated or contextually misaligned information.
+
+
+## References
+
+- **DocVLM: Make Your VLM an Efficient Reader** 
+  [https://arxiv.org/pdf/2412.08746v1](https://arxiv.org/pdf/2412.08746v1)
+
+- **YaRN: Efficient Context Window Extension of Large Language Models**  
+  [https://arxiv.org/pdf/2309.00071](https://arxiv.org/pdf/2309.00071)
+
+- **Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution**  
+  [https://arxiv.org/pdf/2409.12191](https://arxiv.org/pdf/2409.12191)
+
+- **Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond**  
+  [https://arxiv.org/pdf/2308.12966](https://arxiv.org/pdf/2308.12966)
+
+- **A Comprehensive and Challenging OCR Benchmark for Evaluating Large Multimodal Models in Literacy**
+  [https://arxiv.org/pdf/2412.02210](https://arxiv.org/pdf/2412.02210)