Instructions to use prithivMLmods/Megalodon-OCR-Sync-0713 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Megalodon-OCR-Sync-0713 with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/Megalodon-OCR-Sync-0713")
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("prithivMLmods/Megalodon-OCR-Sync-0713")
model = AutoModelForMultimodalLM.from_pretrained("prithivMLmods/Megalodon-OCR-Sync-0713", 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 prithivMLmods/Megalodon-OCR-Sync-0713 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Megalodon-OCR-Sync-0713"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Megalodon-OCR-Sync-0713",
		"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/prithivMLmods/Megalodon-OCR-Sync-0713

SGLang

How to use prithivMLmods/Megalodon-OCR-Sync-0713 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 "prithivMLmods/Megalodon-OCR-Sync-0713" \
    --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": "prithivMLmods/Megalodon-OCR-Sync-0713",
		"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 "prithivMLmods/Megalodon-OCR-Sync-0713" \
        --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": "prithivMLmods/Megalodon-OCR-Sync-0713",
		"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 prithivMLmods/Megalodon-OCR-Sync-0713 with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Megalodon-OCR-Sync-0713
```

update meta-data

by prithivMLmods - opened Nov 22, 2025

base: refs/heads/main

←

from: refs/pr/3

Discussion Files changed

-5

Files changed (1) hide show

README.md +2 -5

README.md CHANGED Viewed

@@ -1,6 +1,5 @@
 ---
 tags:
-- trl
 - text-generation-inference
 - image-captioning
 - optical-character-recognition
@@ -9,11 +8,9 @@ tags:
 - ocr
 - visual-understanding
 - art
-- icr
 - image-to-text
 - vlm
 - table
-- document
 language:
 - en
 - zh
@@ -23,7 +20,7 @@ pipeline_tag: image-text-to-text
 library_name: transformers
 ---
-![1](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/5sJ8OdTFjjI3gj8PfWKKm.png)
 # **Megalodon-OCR-Sync-0713**
@@ -116,4 +113,4 @@ This model is intended for:
 * Variable accuracy on uncommon or low-resource languages/scripts.
 * Long video processing may require substantial memory and is not optimized for streaming applications.
 * Visual token settings affect performance; suboptimal configurations can impact results.
-* In rare cases, outputs may contain hallucinated or contextually misaligned information.

 ---
 tags:
 - text-generation-inference
 - image-captioning
 - optical-character-recognition
 - ocr
 - visual-understanding
 - art
 - image-to-text
 - vlm
 - table
 language:
 - en
 - zh
 library_name: transformers
 ---
+![Meg](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/bqYlNVjbNy1xAElT9_NcU.png)
 # **Megalodon-OCR-Sync-0713**
 * Variable accuracy on uncommon or low-resource languages/scripts.
 * Long video processing may require substantial memory and is not optimized for streaming applications.
 * Visual token settings affect performance; suboptimal configurations can impact results.
+* In rare cases, outputs may contain hallucinated or contextually misaligned information.