Instructions to use IrieDinamik/ocr-mirror-dots-ocr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IrieDinamik/ocr-mirror-dots-ocr with Transformers:

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

pipe = pipeline("text-generation", model="IrieDinamik/ocr-mirror-dots-ocr", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("IrieDinamik/ocr-mirror-dots-ocr", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use IrieDinamik/ocr-mirror-dots-ocr with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IrieDinamik/ocr-mirror-dots-ocr"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IrieDinamik/ocr-mirror-dots-ocr",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/IrieDinamik/ocr-mirror-dots-ocr

SGLang

How to use IrieDinamik/ocr-mirror-dots-ocr 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 "IrieDinamik/ocr-mirror-dots-ocr" \
    --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": "IrieDinamik/ocr-mirror-dots-ocr",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "IrieDinamik/ocr-mirror-dots-ocr" \
        --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": "IrieDinamik/ocr-mirror-dots-ocr",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use IrieDinamik/ocr-mirror-dots-ocr with Docker Model Runner:
```
docker model run hf.co/IrieDinamik/ocr-mirror-dots-ocr
```

Vox Jot OCR Mirror: dots-ocr

This repository is an app-managed mirror for Vox Jot. It preserves upstream lineage so the desktop app can download OCR model files anonymously and reproducibly.

Upstream Sources

rednote-hilab/dots.ocr

License And Attribution

Upstream license: Custom/gated upstream terms
Attribution: original model authors and upstream repositories listed above.
Vox Jot changes: mirrored and repackaged for app-managed download; no model training changes are implied by this mirror.
Notices: upstream README, LICENSE, NOTICE, and COPYING files found during staging are preserved under upstream-notices/.

Notes

May require accepting license on HF in browser before download works.

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Safetensors

Model size

3B params

Tensor type

BF16

Collection including IrieDinamik/ocr-mirror-dots-ocr

Vox Jot – OCR Verified

Collection

Curated on-device OCR models verified for Vox Jot. Image-to-text and document scanning models optimized for local inference on-device. • 11 items • Updated Apr 29