Instructions to use openbmb/MiniCPM-OCR-1.0-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Local Apps Settings

How to use openbmb/MiniCPM-OCR-1.0-AWQ with vLLM:

Install from pip and serve model

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

SGLang

How to use openbmb/MiniCPM-OCR-1.0-AWQ 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 "openbmb/MiniCPM-OCR-1.0-AWQ" \
    --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": "openbmb/MiniCPM-OCR-1.0-AWQ",
		"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 "openbmb/MiniCPM-OCR-1.0-AWQ" \
        --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": "openbmb/MiniCPM-OCR-1.0-AWQ",
		"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 openbmb/MiniCPM-OCR-1.0-AWQ with Docker Model Runner:
```
docker model run hf.co/openbmb/MiniCPM-OCR-1.0-AWQ
```

asdfasdfqrqwer commited on 5 days ago

Commit

e3cd6a1

1 Parent(s): f34cb12

Add top navigation links and remove the hero image from the model card.

Browse files

Files changed (1) hide show

README.md +5 -8

README.md CHANGED Viewed

@@ -16,19 +16,16 @@ base_model: openbmb/MiniCPM-OCR-1.0
 base_model_relation: quantized
 ---
-> **This repository hosts the AWQ W4A16 quantized version of
-> [MiniCPM-OCR 1.0](https://huggingface.co/openbmb/MiniCPM-OCR-1.0).** For the original BF16 weights and the base model
-> card, see [openbmb/MiniCPM-OCR-1.0](https://huggingface.co/openbmb/MiniCPM-OCR-1.0). Everything below describes the model
-> itself; see [Quantization](#quantization) for what changed.
 <div align="center">
-<img src="./assets/release-hero.svg" alt="MiniCPM-OCR: fast, accurate document parsing into text, tables, and formulas, with a decision model that flags regions needing review." width="100%">
-<p align="center"><strong>MiniCPM-OCR &middot; Fast, accurate parsing for real-world documents.</strong></p>
 </div>
 **MiniCPM-OCR 1.0** (1.3B) is the first OCR-specific model in the
 [MiniCPM-V/o](https://github.com/openbmb/MiniCPM-V) family. It focuses on text-centric document
 understanding — text, tables, formulas, charts, seals, and structured information extraction —

 base_model_relation: quantized
 ---
 <div align="center">
+[GitHub](https://github.com/OpenBMB/MiniCPM-OCR) · [API](https://github.com/OpenBMB/MiniCPM-OCR/blob/main/docs/minicpm_ocr_api_en.md) · [Demo](https://platform.modelbest.ai/console/playground/document-parse) · [DSpark](https://huggingface.co/openbmb/MiniCPM-OCR-1.0-DSpark)
 </div>
+> **This repository hosts the AWQ W4A16 quantized version of
+> [MiniCPM-OCR 1.0](https://huggingface.co/openbmb/MiniCPM-OCR-1.0).** For the original BF16 weights and the base model
+> card, see [openbmb/MiniCPM-OCR-1.0](https://huggingface.co/openbmb/MiniCPM-OCR-1.0). Everything below describes the model
+> itself; see [Quantization](#quantization) for what changed.
 **MiniCPM-OCR 1.0** (1.3B) is the first OCR-specific model in the
 [MiniCPM-V/o](https://github.com/openbmb/MiniCPM-V) family. It focuses on text-centric document
 understanding — text, tables, formulas, charts, seals, and structured information extraction —