Instructions to use tiny-random/minicpm-ocr-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiny-random/minicpm-ocr-1.0 with Transformers:

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

pipe = pipeline("image-text-to-text", model="tiny-random/minicpm-ocr-1.0")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("tiny-random/minicpm-ocr-1.0")
model = AutoModelForMultimodalLM.from_pretrained("tiny-random/minicpm-ocr-1.0", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tiny-random/minicpm-ocr-1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tiny-random/minicpm-ocr-1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiny-random/minicpm-ocr-1.0",
		"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/tiny-random/minicpm-ocr-1.0

SGLang

How to use tiny-random/minicpm-ocr-1.0 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 "tiny-random/minicpm-ocr-1.0" \
    --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": "tiny-random/minicpm-ocr-1.0",
		"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 "tiny-random/minicpm-ocr-1.0" \
        --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": "tiny-random/minicpm-ocr-1.0",
		"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 tiny-random/minicpm-ocr-1.0 with Docker Model Runner:
```
docker model run hf.co/tiny-random/minicpm-ocr-1.0
```

minicpm-ocr-1.0 / README.md

yujiepan

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c5752e3 verified 5 days ago

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---
library_name: transformers
pipeline_tag: image-text-to-text
base_model:
- openbmb/MiniCPM-OCR-1.0
---
This is a randomly initialized tiny model based on [openbmb/MiniCPM-OCR-1.0](https://huggingface.co/openbmb/MiniCPM-OCR-1.0) for testing and debugging.

| Feature | Source | This repo |
|---|---|---|
| Checkpoint tensor elements | 1.30B BF16 | 7.58M BF16 |
| Safetensors weight files | 2.60 GB (model.safetensors) | 15.18 MB (model.safetensors) |
| Architecture / input | MiniCPM-OCR; text and document image | Same |
| Vocabulary rows / tied embeddings | 248,100 / tied | Same |
| Backbone layers / layer cadence | 24 / six 3-linear + 1-full cycles | 8 / two complete cycles |
| Hidden width / MLP width | 1,024 / 3,584 | 16 / 32 |
| Q heads / KV heads / head dimension | 8 / 2 / 256 | Same |
| Linear-attention key/value heads and dimensions | 16/16, 128/128 | Same |
| Vision layers / width / insert merger | 27 / 1,152 / after layer 6 | 4 / 128 / after layer 2 |
| Visual downsampling | 4x | Same |
| M-RoPE | sections 11/11/10; partial 0.25 | sections 11/11/10; partial 0.25 |
| MTP | disabled | Same |
| Checkpoint precision | BF16 | Same |

The tokenizer embedding and output head stay tied, so the checkpoint contains one BF16 token table. Two complete linear/linear/linear/full cycles are retained. The Qwen3.5 linear-attention path requires CUDA and Triton. The exact public snippet was verified on an NVIDIA A800-SXM4-80GB.

| File path | Size |
|------|------|
| model.safetensors | 15.2MB |

### Example usage

#### Transformers (✅ verified)

The Qwen3.5 linear-attention path requires CUDA and Triton. Install `transformers[torch]>=5.21.0` and `Pillow`.

```python
def run_transformers(model_id: str):
    import torch
    from PIL import Image, ImageDraw
    from transformers import AutoModelForImageTextToText, AutoProcessor
    model_id = str(model_id)
    processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
    model = AutoModelForImageTextToText.from_pretrained(
        model_id,
        dtype=torch.bfloat16,
        device_map="cuda",
        attn_implementation="sdpa",
    )
    image = Image.new("RGB", (448, 448), "white")
    ImageDraw.Draw(image).text((20, 20), "Hello OCR", fill="black")
    messages = [
        {
            "role": "user",
            "content": [
                {"type": "image", "image": image},
                {"type": "text", "text": "Read this document."},
            ],
        }
    ]
    inputs = processor.apply_chat_template(
        messages,
        tokenize=True,
        add_generation_prompt=True,
        return_dict=True,
        return_tensors="pt",
        downsample_mode="4x",
        max_slice_nums=1,
        use_image_id=False,
    ).to(model.device)
    with torch.no_grad():
        logits = model(**inputs).logits
        assert torch.isfinite(logits).all()
        output = model.generate(**inputs, downsample_mode="4x", do_sample=False, max_new_tokens=10)
    print(processor.decode(output[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True))
if __name__ == "__main__":
    run_transformers(model_id="tiny-random/minicpm-ocr-1.0")
```

### Creating this repo

- Code: [.meta/create_model.py](.meta/create_model.py)
- Model structure: [.meta/model.log](.meta/model.log)
- Environment: [.meta/env.json](.meta/env.json)

### Change log

- 2026-11-01: Initial tiny MiniCPM-OCR 1.0 checkpoint.

*This repo is generated by DeepSeek-V4.1-Flash.*

	---
	library_name: transformers
	pipeline_tag: image-text-to-text
	base_model:
	- openbmb/MiniCPM-OCR-1.0
	---

	This is a randomly initialized tiny model based on [openbmb/MiniCPM-OCR-1.0](https://huggingface.co/openbmb/MiniCPM-OCR-1.0) for testing and debugging.

	\| Feature \| Source \| This repo \|
	\|---\|---\|---\|
	\| Checkpoint tensor elements \| 1.30B BF16 \| 7.58M BF16 \|
	\| Safetensors weight files \| 2.60 GB (model.safetensors) \| 15.18 MB (model.safetensors) \|
	\| Architecture / input \| MiniCPM-OCR; text and document image \| Same \|
	\| Vocabulary rows / tied embeddings \| 248,100 / tied \| Same \|
	\| Backbone layers / layer cadence \| 24 / six 3-linear + 1-full cycles \| 8 / two complete cycles \|
	\| Hidden width / MLP width \| 1,024 / 3,584 \| 16 / 32 \|
	\| Q heads / KV heads / head dimension \| 8 / 2 / 256 \| Same \|
	\| Linear-attention key/value heads and dimensions \| 16/16, 128/128 \| Same \|
	\| Vision layers / width / insert merger \| 27 / 1,152 / after layer 6 \| 4 / 128 / after layer 2 \|
	\| Visual downsampling \| 4x \| Same \|
	\| M-RoPE \| sections 11/11/10; partial 0.25 \| sections 11/11/10; partial 0.25 \|
	\| MTP \| disabled \| Same \|
	\| Checkpoint precision \| BF16 \| Same \|

	The tokenizer embedding and output head stay tied, so the checkpoint contains one BF16 token table. Two complete linear/linear/linear/full cycles are retained. The Qwen3.5 linear-attention path requires CUDA and Triton. The exact public snippet was verified on an NVIDIA A800-SXM4-80GB.

	\| File path \| Size \|
	\|------\|------\|
	\| model.safetensors \| 15.2MB \|

	### Example usage

	#### Transformers (✅ verified)

	The Qwen3.5 linear-attention path requires CUDA and Triton. Install `transformers[torch]>=5.21.0` and `Pillow`.

	```python
	def run_transformers(model_id: str):
	import torch
	from PIL import Image, ImageDraw
	from transformers import AutoModelForImageTextToText, AutoProcessor

	model_id = str(model_id)
	processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
	model = AutoModelForImageTextToText.from_pretrained(
	model_id,
	dtype=torch.bfloat16,
	device_map="cuda",
	attn_implementation="sdpa",
	)
	image = Image.new("RGB", (448, 448), "white")
	ImageDraw.Draw(image).text((20, 20), "Hello OCR", fill="black")
	messages = [
	{
	"role": "user",
	"content": [
	{"type": "image", "image": image},
	{"type": "text", "text": "Read this document."},
	],
	}
	]
	inputs = processor.apply_chat_template(
	messages,
	tokenize=True,
	add_generation_prompt=True,
	return_dict=True,
	return_tensors="pt",
	downsample_mode="4x",
	max_slice_nums=1,
	use_image_id=False,
	).to(model.device)
	with torch.no_grad():
	logits = model(**inputs).logits
	assert torch.isfinite(logits).all()
	output = model.generate(**inputs, downsample_mode="4x", do_sample=False, max_new_tokens=10)
	print(processor.decode(output[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True))


	if __name__ == "__main__":
	run_transformers(model_id="tiny-random/minicpm-ocr-1.0")
	```

	### Creating this repo

	- Code: [.meta/create_model.py](.meta/create_model.py)
	- Model structure: [.meta/model.log](.meta/model.log)
	- Environment: [.meta/env.json](.meta/env.json)

	### Change log

	- 2026-11-01: Initial tiny MiniCPM-OCR 1.0 checkpoint.

	This repo is generated by DeepSeek-V4.1-Flash.