Update README.md

README.md

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 ---
 library_name: transformers
-tags: []
+tags:
+- vision-language
+- image-to-text
+- question-answering
+- fine-tuned
+- blip
+- vqa
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
 
+# BLIP VQA Fine-tuned Model - Draft 1
 
+This model generates structured question-answer pairs from input images, fine-tuned for visual question answering tasks with structured output format.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+This is a fine-tuned BLIP model specifically designed for Visual Question Answering (VQA) tasks. The model takes images as input and generates structured question-answer pairs in XML-like format with `<question>` and `<answer>` tags.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Developed by:** eagle0504
+- **Model type:** Vision-Language Model (BLIP-based VQA)
+- **Language(s):** English
+- **License:** Apache 2.0
+- **Finetuned from model:** BLIP (Bootstrapping Language-Image Pre-training)
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** https://huggingface.co/eagle0504/blip-vqa-finetuned-draft-1
+- **Base Model:** BLIP (Salesforce)
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+The model can be used for:
+- Automated question-answer generation from images
+- Visual content analysis and understanding
+- Educational content creation from visual materials
+- Image captioning with structured Q&A format
 
-[More Information Needed]
+### Usage Example
 
-### Downstream Use [optional]
+```python
+from transformers import AutoProcessor, AutoModelForVision2Seq
+from PIL import Image
+import torch
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
+# Load model and processor
+model = AutoModelForVision2Seq.from_pretrained("eagle0504/blip-vqa-finetuned-draft-1")
+processor = AutoProcessor.from_pretrained("eagle0504/blip-vqa-finetuned-draft-1")
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+# Load and process image
+image = Image.open("path/to/your/image.jpg")
+inputs = processor(images=image, return_tensors="pt")
 
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
+# Generate question-answer pair
+with torch.no_grad():
+    generated_ids = model.generate(pixel_values=inputs.pixel_values, max_length=50)
+    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    
+print(generated_text)
+# Output: <question>what is there in the figure?</question><answer>the entire thickness of the epithelium</answer>
+```
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
+### Out-of-Scope Use
 
-[More Information Needed]
+- The model is not designed for real-time applications requiring immediate responses
+- Not suitable for generating factual information that requires external knowledge beyond visual content
+- May not perform well on images significantly different from the training distribution
+- Should not be used for critical decision-making without human oversight
 
 ## Training Details
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
+The model was fine-tuned on a dataset containing:
+- **Dataset size:** 19,654 image-question-answer triplets
+- **Features:** Images paired with corresponding questions and answers
+- **Format:** Structured data with XML-like tags for questions and answers
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+- **Training regime:** Mixed precision training (fp16/bf16)
+- **Max sequence length:** 50 tokens
+- **Batch size:** [Your batch size]
+- **Learning rate:** [Your learning rate]
+- **Epochs:** [Number of epochs]
 
-## Evaluation
+#### Preprocessing
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+Images are processed using the model's associated processor, which handles:
+- Image resizing and normalization
+- Tensor conversion for model input
+- Text tokenization for question-answer pairs
 
-### Testing Data, Factors & Metrics
+## Performance
 
-#### Testing Data
+The model generates structured question-answer pairs with the following format:
+```
+<question>[Generated question about the image]</question><answer>[Corresponding answer]</answer>
+```
 
-<!-- This should link to a Dataset Card if possible. -->
+### Evaluation Metrics
 
-[More Information Needed]
+- Text generation quality
+- Semantic relevance of questions to image content
+- Accuracy of answers relative to visual content
+- Format consistency (proper XML tag structure)
 
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+## Bias, Risks, and Limitations
 
-## Environmental Impact
+### Known Limitations
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+- Performance may vary based on image quality and complexity
+- Generated questions and answers reflect patterns in training data
+- May produce repetitive or generic questions for certain image types
+- Limited to the vocabulary and concepts present in training data
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+### Recommendations
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+- Validate outputs for critical applications
+- Consider domain-specific fine-tuning for specialized use cases
+- Review generated content for appropriateness in your specific context
 
-## Technical Specifications [optional]
+## Technical Specifications
 
-### Model Architecture and Objective
+### Model Architecture
 
-[More Information Needed]
+- Base architecture: BLIP (Bootstrapping Language-Image Pre-training)
+- Vision Encoder: ViT (Vision Transformer)
+- Text Decoder: BERT-based decoder
+- Input: RGB images (224x224 default resolution)
+- Output: Text sequences with structured Q&A format (`<question>` and `<answer>` tags)
+- Fine-tuned for Visual Question Answering with structured output
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
+#### Hardware Requirements
 
-[More Information Needed]
+- GPU memory: Minimum 4GB for inference
+- CPU: Compatible with modern processors
+- RAM: 8GB+ recommended for optimal performance
 
-## Glossary [optional]
+#### Software Dependencies
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+```
+transformers>=4.35.0
+torch>=2.0.0
+Pillow>=8.0.0
+```
 
-[More Information Needed]
+## Citation
 
-## More Information [optional]
+If you use this model in your research, please cite:
 
-[More Information Needed]
+```bibtex
+@misc{eagle0504-blip-vqa-2025,
+  title={BLIP VQA Fine-tuned Model - Draft 1},
+  author={eagle0504},
+  year={2025},
+  url={https://huggingface.co/eagle0504/blip-vqa-finetuned-draft-1}
+}
+```
 
-## Model Card Authors [optional]
+## Model Card Authors
 
-[More Information Needed]
+eagle0504 - Principal AI Engineer at FICO
 
 ## Model Card Contact
 
-[More Information Needed]
+https://huggingface.co/eagle0504