Instructions to use Reza2kn/Bina-0.1-Koochik with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Reza2kn/Bina-0.1-Koochik with Transformers:

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

pipe = pipeline("image-text-to-text", model="Reza2kn/Bina-0.1-Koochik")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Reza2kn/Bina-0.1-Koochik")
model = AutoModelForMultimodalLM.from_pretrained("Reza2kn/Bina-0.1-Koochik", device_map="auto")
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 Settings

vLLM

How to use Reza2kn/Bina-0.1-Koochik with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Reza2kn/Bina-0.1-Koochik"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Reza2kn/Bina-0.1-Koochik",
		"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/Reza2kn/Bina-0.1-Koochik

SGLang

How to use Reza2kn/Bina-0.1-Koochik 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 "Reza2kn/Bina-0.1-Koochik" \
    --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": "Reza2kn/Bina-0.1-Koochik",
		"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 "Reza2kn/Bina-0.1-Koochik" \
        --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": "Reza2kn/Bina-0.1-Koochik",
		"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 Reza2kn/Bina-0.1-Koochik with Docker Model Runner:
```
docker model run hf.co/Reza2kn/Bina-0.1-Koochik
```

Bina 0.1 Koochik — بینا ۰.۱ کوچک

The merged BF16 release of the Persian OCR LoRA checkpoint at step 8,000 from Reza2kn/surya-ocr-2-persian-lora-7m, based on datalab-to/surya-ocr-2.

This repository is the immutable BF16 transcription baseline for Bina 0.1 Koochik. Quantized/runtime-specific derivatives should be compared against this model before being described as parity-preserving.

Quick start (English)

Bina 0.1 Koochik is compatible with the Surya OCR 2 inference package. The easiest supported setup is Linux (or WSL2) with an NVIDIA GPU.

1. Install the prerequisites

Python 3.10 or newer
Docker
NVIDIA Container Toolkit

Then install or upgrade Surya:

python -m pip install -U "surya-ocr>=0.20.0"

2. Run OCR

Replace document.pdf with an image, a PDF, or a folder containing images/PDFs:

export SURYA_MODEL_CHECKPOINT=Reza2kn/Bina-0.1-Koochik
export SURYA_INFERENCE_BACKEND=vllm

surya_ocr ./document.pdf --output_dir ./bina-output

On the first run, Surya downloads the model and starts a vLLM server in Docker, so startup can take a few minutes. For the example above, the OCR result is written to ./bina-output/document/results.json. Each page contains ordered blocks with recognized HTML/text, labels, confidence scores, and bounding boxes.

Useful options:

# Process only pages 0 through 2
surya_ocr ./document.pdf --page_range 0-2 --output_dir ./bina-output

# Also save annotated page images
surya_ocr ./document.pdf --images --output_dir ./bina-output

Python example

Set the environment variables before importing surya:

import os

os.environ["SURYA_MODEL_CHECKPOINT"] = "Reza2kn/Bina-0.1-Koochik"
os.environ["SURYA_INFERENCE_BACKEND"] = "vllm"

from PIL import Image
from surya.inference import SuryaInferenceManager
from surya.recognition import RecognitionPredictor

image = Image.open("page.jpg").convert("RGB")
manager = SuryaInferenceManager()
predictor = RecognitionPredictor(manager)
result = predictor([image])[0]

for block in result.blocks:
    print(block.html)

CPU and Apple Silicon: this repository contains BF16 weights, not a GGUF build. The automatic llama.cpp path therefore cannot run Bina 0.1 Koochik directly; use the NVIDIA/vLLM setup above or a Bina-specific GGUF conversion.

راه‌اندازی سریع (فارسی)

بینا ۰.۱ کوچک با بستهٔ استنتاج Surya OCR 2 سازگار است. ساده‌ترین روش پشتیبانی‌شده، استفاده از لینوکس (یا WSL2) و کارت گرافیک NVIDIA است.

۱. نصب پیش‌نیازها

پایتون ۳.۱۰ یا جدیدتر
Docker
NVIDIA Container Toolkit

سپس Surya را نصب یا به‌روز کنید:

python -m pip install -U "surya-ocr>=0.20.0"

۲. اجرای OCR

به‌جای document.pdf می‌توانید مسیر یک تصویر، فایل PDF یا پوشه‌ای از تصاویر/PDFها را قرار دهید:

export SURYA_MODEL_CHECKPOINT=Reza2kn/Bina-0.1-Koochik
export SURYA_INFERENCE_BACKEND=vllm

surya_ocr ./document.pdf --output_dir ./bina-output

در اجرای اول، Surya مدل را دانلود و سرور vLLM را در Docker راه‌اندازی می‌کند؛ بنابراین شروع کار ممکن است چند دقیقه طول بکشد. در مثال بالا، نتیجه در مسیر ./bina-output/document/results.json ذخیره می‌شود. خروجی هر صفحه شامل بلوک‌های مرتب‌شده، متن/HTML تشخیص‌داده‌شده، نوع بلوک، میزان اطمینان و مختصات کادرها است.

چند گزینهٔ کاربردی:

# فقط پردازش صفحه‌های ۰ تا ۲
surya_ocr ./document.pdf --page_range 0-2 --output_dir ./bina-output

# ذخیرهٔ تصویر صفحه‌ها همراه با کادرهای تشخیص‌داده‌شده
surya_ocr ./document.pdf --images --output_dir ./bina-output

نمونهٔ پایتون

متغیرهای محیطی را پیش از import کردن surya تنظیم کنید:

import os

os.environ["SURYA_MODEL_CHECKPOINT"] = "Reza2kn/Bina-0.1-Koochik"
os.environ["SURYA_INFERENCE_BACKEND"] = "vllm"

from PIL import Image
from surya.inference import SuryaInferenceManager
from surya.recognition import RecognitionPredictor

image = Image.open("page.jpg").convert("RGB")
manager = SuryaInferenceManager()
predictor = RecognitionPredictor(manager)
result = predictor([image])[0]

for block in result.blocks:
    print(block.html)

CPU و Apple Silicon: این مخزن شامل وزن‌های BF16 است و فایل GGUF ندارد؛ بنابراین مسیر خودکار llama.cpp نمی‌تواند بینا ۰.۱ کوچک را مستقیماً اجرا کند. از روش NVIDIA/vLLM بالا یا یک تبدیل GGUF مخصوص بینا استفاده کنید.

Provenance

LoRA checkpoint: checkpoints/checkpoint-step-0008000
Merged artifact path on the release host: /home/rezo/triple-threat/hf-cache/surya-step8000-merged
Architecture: Qwen3_5ForConditionalGeneration
Weight dtype: BF16
model.safetensors SHA-256: 2193be4ef3d2366438121a15b7a1dea2bb85b24f83145e5a39bfa1f387891ada
config.json SHA-256: e0de22be177070f206106c184d062176fcda591d9114068c42489ffc550488de

Intended use

Persian document OCR. The model is currently served as بینا ۰.۱ کوچک on PersianVLM.com. Use deterministic decoding for baseline comparisons.

License

This release follows the upstream Surya OCR 2 OpenRAIL license. Review the upstream license before redistribution or deployment.

Downloads last month: 937

Safetensors

Model size

0.7B params

Tensor type

BF16

Model tree for Reza2kn/Bina-0.1-Koochik

Base model

datalab-to/surya-ocr-2

Finetuned

(4)

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Finetunes

1 model

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Bina-0.1-Koochik