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README.md
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# Model Description
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Fine-tuned Florence-2 model on DocumentVQA dataset to perform question answering on document images
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#
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# Get Started with the Model
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#### 1. Installation
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```python
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!pip install torch transformers datasets flash_attn
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```
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#### 2. Loading model and processor
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```python
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import torch
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model.to(device)
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```
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#### 3. Running inference
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```python
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def run_inference(task_prompt, question, image):
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generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
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return generated_text
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```
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#### 4. Example
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```python
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from PIL import Image
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```
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---
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# BibTeX:
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```bibtex
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@misc{sahilnishad_florence_2_ft_docvqa,
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# Model Description
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Fine-tuned Florence-2 model on DocumentVQA dataset to perform question answering on document images
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- **[Github](https://github.com/sahilnishad/Fine-Tuning-Florence-2-DocumentVQA)**
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# Get Started with the Model
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#### 1. Installation
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```python
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!pip install torch transformers datasets flash_attn
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```
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#### 2. Loading model and processor
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```python
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import torch
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model.to(device)
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```
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#### 3. Running inference
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```python
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def run_inference(task_prompt, question, image):
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generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
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return generated_text
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```
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#### 4. Example
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```python
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from PIL import Image
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```
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---
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# BibTeX:
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```bibtex
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@misc{sahilnishad_florence_2_ft_docvqa,
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