---
pipeline_tag: image-text-to-text
tags:
- visual-document-understanding
- visual-question-answering
- indian-documents
license: apache-2.0
language:
- en
library_name: transformers
---

# Patram-7B-Instruct

Patram-7B-Instruct by BharatGen is a 7B parameter vision-language model trained from scratch for visual document understanding. As India’s first document foundation model, it is built to tackle complex document analysis.
The model was trained on a carefully curated instruction-tuned dataset, combining diverse public and custom synthetic data designed to support a broad spectrum of document understanding tasks.

## Model Overview

* **Architecture:** Vision Transformer (ViT) + MLP projector + OLMo-7B LLM
* **Training Data:** BharatDocs-v1, a dataset of diverse Indian documents + Other Open Source Document Datasets
* **Supported I/O Formats:** The model currently accepts English-language instructions and image files (e.g., PNG, JPEG) as input. The output is provided in text format.
* **Language:** English (Indian language support upcoming)
* **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)

## Usage Examples

Use the `transformers` library.

```python
import torch
from transformers import AutoProcessor, AutoModelForCausalLM, GenerationConfig
from PIL import Image
import requests

# Model ID and device setup
model_id = "bharatgenai/patram-7b-instruct"
device = "cuda" if torch.cuda.is_available() else "cpu"

# Load processor and model
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True
).to(device)

def get_patram_response(image_path_or_url, question):
    try:
        # Load image
        if image_path_or_url.startswith("http"):
            image = Image.open(requests.get(image_path_or_url, stream=True).raw).convert("RGB")
        else:
            image = Image.open(image_path_or_url).convert("RGB")
    except Exception as e:
        print(f"Error loading image: {e}")
        return None

    # Format the prompt as expected
    prompt = f"Question: {question} Answer based on the image."

    try:
        # Preprocess image and text using the processor
        inputs = processor.process(images=[image], text=prompt)
        inputs = {k: v.to(device).unsqueeze(0) for k, v in inputs.items()}

        # Generate output using model's generate_from_batch method (Patram-specific)
        output = model.generate_from_batch(
            inputs,
            GenerationConfig(max_new_tokens=200, stop_strings="<|endoftext|>"),
            tokenizer=processor.tokenizer
        )

        # Extract generated tokens (excluding input tokens) and decode
        generated_tokens = output[0, inputs['input_ids'].size(1):]
        response = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True).strip()
        return response
    except Exception as e:
        print(f"Error during inference: {e}")
        return None

# Example usage:
# image_input = "https://knowscope.in/wp-content/uploads/2025/05/cghd-nag.png"
# question = "Who issued this notice?"
# answer = get_patram_response(image_input, question)
# if answer:
#     print("Answer:", answer)
```

**Note**: If you're trying this on an Apple Silicon (M1/M2/M3/M4/...) chip, please follow the official documentation by PyTorch and Hugging Face for installing dependencies:

- [PyTorch's official guide on installation (macOS)](https://pytorch.org/get-started/locally/#:~:text=torch%20torchvision%20torchaudio-,Installing%20on%20macOS,-PyTorch%20can%20be)
- [Hugging Face Transformers performance tips](https://huggingface.co/docs/transformers/main/en/perf_train_special)
     
  
## Evaluations

We evaluated Patram-7B-Instruct alongside other vision-language models (VLMs) in the 7B–9B parameter range across multiple public document benchmarks.

**Benchmarks**: DocVQA, VisualMRC, Patram-Bench

Patram-Bench is an in-house benchmark designed for Indic Document VQA.

**Metric**: G-Eval (LLM-as-a-judge)

| Model                  | Overall | DocVQA | Patram-Bench | VisualMRC |
| ---------------------- | ------- | ------ | ------------ | --------- |
| claude-3.7-sonnet      | 0.8830  | 0.8480 | 0.8857       | 0.8830    |
| Qwen2.5-VL-7B-Instruct | 0.8759  | 0.8722 | 0.6816       | 0.9169    |
| gemma-3-12b-it         | 0.8556  | 0.8451 | 0.6349       | 0.9069    |
| **patram-7b-instruct** | 0.8331  | 0.8550 | 0.6515       | 0.8510    |
| InternVL3-9B           | 0.7865  | 0.8681 | 0.6888       | 0.7405    |
| deepseek-vl2           | 0.7581  | 0.8739 | 0.5089       | 0.7144    |

*Note: The benchmarked results reflect the API variant.

## Citation

```bibtex
@online{BharatGenPatramLaunch2025,
  author    = {{BharatGen Team}},
  title     = {BharatGen Unveils Patram: India's Pioneering Vision-Language Foundation Model for Document Intelligence},
  year      = {2025},
  url       = {https://bharatgen.com/blog/patram-launch},
  urldate   = {2025-06-02}
}
```

## Resources

* **Model**: [huggingface.co/bharatgenai/patram-7b-instruct](https://huggingface.co/bharatgenai/patram-7b-instruct)
* **Project Page**: [bharatgen.com/patram](https://bharatgen.com/patram)
* **Blog**: [bharatgen.com/blog/patram-launch](https://bharatgen.com/blog/patram-launch)

## Authors

* **Principal Investigators**: Prof. Ravi Kiran Sarvadevabhatla, Prof. Ganesh Ramakrishnan
* **Contributors**: BharatGen Team

## Contact

* [Contact Form](https://bharatgen.com/contact)
* Hugging Face Community Tab