Instructions to use Chhagan005/Chhagan_ML-VL-OCR-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Chhagan005/Chhagan_ML-VL-OCR-v1 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct")
model = PeftModel.from_pretrained(base_model, "Chhagan005/Chhagan_ML-VL-OCR-v1")

Transformers

How to use Chhagan005/Chhagan_ML-VL-OCR-v1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="Chhagan005/Chhagan_ML-VL-OCR-v1")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Chhagan005/Chhagan_ML-VL-OCR-v1")
model = AutoModelForImageTextToText.from_pretrained("Chhagan005/Chhagan_ML-VL-OCR-v1")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Chhagan005/Chhagan_ML-VL-OCR-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Chhagan005/Chhagan_ML-VL-OCR-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Chhagan005/Chhagan_ML-VL-OCR-v1",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Chhagan005/Chhagan_ML-VL-OCR-v1

SGLang

How to use Chhagan005/Chhagan_ML-VL-OCR-v1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Chhagan005/Chhagan_ML-VL-OCR-v1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Chhagan005/Chhagan_ML-VL-OCR-v1",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Chhagan005/Chhagan_ML-VL-OCR-v1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Chhagan005/Chhagan_ML-VL-OCR-v1",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Chhagan005/Chhagan_ML-VL-OCR-v1 with Docker Model Runner:
```
docker model run hf.co/Chhagan005/Chhagan_ML-VL-OCR-v1
```

Chhagan_ML-VL-OCR-v1

Multilingual OCR model for government ID cards, fine-tuned with LoRA on Qwen2.5-VL-3B.

Supported Documents

🇴🇲 Oman Resident Card
🇦🇪 UAE Identity Card
🇸🇦 Saudi National ID
🇮🇳 Aadhaar Card, PAN Card, Passport
🇿🇦 South Africa ID

Languages

English, Arabic (عربي), Hindi (हिन्दी), Urdu (اردو)

Usage

Option 1: Load as LoRA Adapter (~74MB download)

from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from peft import PeftModel

base = Qwen2_5_VLForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct")
model = PeftModel.from_pretrained(base, "Chhagan005/Chhagan_ML-VL-OCR-v1")
processor = AutoProcessor.from_pretrained("Chhagan005/Chhagan_ML-VL-OCR-v1")

Option 2: Load as Standalone Model (~4.4GB download)

from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "Chhagan005/Chhagan_ML-VL-OCR-v1",
    torch_dtype=torch.float16, device_map="auto"
)
processor = AutoProcessor.from_pretrained("Chhagan005/Chhagan_ML-VL-OCR-v1")

Training

Base: Qwen/Qwen2.5-VL-3B-Instruct
Method: LoRA (r=32, α=64)
Data: Synthetic IDs + ANETAC Arabic names + Ara-Eng Parallel Corpus
Focus: OCR extraction from government identity documents

Downloads last month: -

Safetensors

Model size

4B params

Tensor type

F16

Chhagan005
/

Chhagan_ML-VL-OCR-v1