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README.md

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+---
+license: mit
+tags:
+  - peft
+  - lora
+  - qwen2
+  - multilingual
+  - ocr
+  - translation
+  - safetensors
+base_model: Qwen/Qwen2-VL-7B-Instruct
+---
+
+# Babel
+
+## Model Description
+Babel is a Qwen2-VL LoRA adapter fine-tuned for multilingual OCR (Optical Character Recognition) and translation tasks. It can extract text from images across multiple languages and translate between them, making it ideal for document digitization, cross-language content processing, and international business automation.
+
+## Model Architecture
+- **Base Model**: `Qwen/Qwen2-VL-7B-Instruct`
+- **Fine-tuning Method**: LoRA (Low-Rank Adaptation) via PEFT
+- **Checkpoint**: Final checkpoint
+- **Task**: Multilingual OCR + Translation (Vision-Language)
+
+## Training Details
+- **Framework**: HuggingFace PEFT + Transformers
+- **Dataset**: Multilingual document images with text annotations and translations
+- **Languages**: Multiple languages supported including English, Hindi, and more
+- **Approach**: Vision-language fine-tuning with OCR and translation objectives
+
+## Files
+| File | Description |
+|------|-------------|
+| `adapter_model.safetensors` | LoRA adapter weights |
+| `adapter_config.json` | PEFT adapter configuration |
+| `tokenizer.json` | Tokenizer vocabulary |
+| `tokenizer_config.json` | Tokenizer configuration |
+
+## Usage
+
+```python
+from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
+from peft import PeftModel
+from PIL import Image
+from huggingface_hub import snapshot_download
+
+# Download adapter
+adapter_dir = snapshot_download(repo_id='devanshty/Babel')
+
+# Load base model
+base_model = Qwen2VLForConditionalGeneration.from_pretrained(
+    "Qwen/Qwen2-VL-7B-Instruct",
+    torch_dtype="auto",
+    device_map="auto"
+)
+processor = AutoProcessor.from_pretrained(adapter_dir)
+
+# Load LoRA adapter
+model = PeftModel.from_pretrained(base_model, adapter_dir)
+model.eval()
+
+# OCR + Translate
+image = Image.open("document.jpg")
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": image},
+            {"type": "text", "text": "Extract all text from this image and translate it to English."}
+        ]
+    }
+]
+text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)
+output = model.generate(**inputs, max_new_tokens=1024)
+print(processor.decode(output[0], skip_special_tokens=True))
+```
+
+## Download & Use
+
+```python
+from huggingface_hub import hf_hub_download
+adapter = hf_hub_download(repo_id='devanshty/Babel', filename='adapter_model.safetensors')
+```