Instructions to use tencent/HunyuanOCR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tencent/HunyuanOCR with Transformers:

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

pipe = pipeline("image-text-to-text", model="tencent/HunyuanOCR")
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 AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("tencent/HunyuanOCR", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tencent/HunyuanOCR with vLLM:

Install from pip and serve model

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

SGLang

How to use tencent/HunyuanOCR 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 "tencent/HunyuanOCR" \
    --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": "tencent/HunyuanOCR",
		"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 "tencent/HunyuanOCR" \
        --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": "tencent/HunyuanOCR",
		"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 tencent/HunyuanOCR with Docker Model Runner:
```
docker model run hf.co/tencent/HunyuanOCR
```

Update README

#11

by EthannW - opened Nov 26, 2025

base: refs/heads/main

←

from: refs/pr/11

Discussion Files changed

-6

Files changed (1) hide show

README.md +4 -6

README.md CHANGED Viewed

@@ -70,16 +70,13 @@ processor = AutoProcessor.from_pretrained(model_name_or_path, use_fast=False)
 img_path = "path/to/your/image.jpg"
 image_inputs = Image.open(img_path)
 messages1 = [
     {
         "role": "user",
         "content": [
             {"type": "image", "image": img_path},
             {"type": "text", "text": (
-                "Extract all information from the main body of the document image "
-                "and represent it in markdown format, ignoring headers and footers. "
-                "Tables should be expressed in HTML format, formulas in the document "
-                "should be represented using LaTeX format, and the parsing should be "
-                "organized according to the reading order."
             )},
         ],
     }
@@ -183,9 +180,10 @@ sampling_params = SamplingParams(temperature=0, max_tokens=16384)
 img_path = "/path/to/image.jpg"
 img = Image.open(img_path)
 messages = [
     {"role": "user", "content": [
         {"type": "image", "image": img_path},
-        {"type": "text", "text": "Detect and recognize text in the image, and output the text coordinates in a formatted manner."}
     ]}
 ]
 prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

 img_path = "path/to/your/image.jpg"
 image_inputs = Image.open(img_path)
 messages1 = [
+    {"role": "system", "content": ""},
     {
         "role": "user",
         "content": [
             {"type": "image", "image": img_path},
             {"type": "text", "text": (
+                "检测并识别图片中的文字，将文本坐标格式化输出。"
             )},
         ],
     }
 img_path = "/path/to/image.jpg"
 img = Image.open(img_path)
 messages = [
+    {"role": "system", "content": ""},
     {"role": "user", "content": [
         {"type": "image", "image": img_path},
+        {"type": "text", "text": "检测并识别图片中的文字，将文本坐标格式化输出。"}
     ]}
 ]
 prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)