Instructions to use davanstrien/lightonocr-books-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use davanstrien/lightonocr-books-test with Transformers:

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

pipe = pipeline("text-generation", model="davanstrien/lightonocr-books-test")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("davanstrien/lightonocr-books-test", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use davanstrien/lightonocr-books-test with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/davanstrien/lightonocr-books-test

SGLang

How to use davanstrien/lightonocr-books-test 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 "davanstrien/lightonocr-books-test" \
    --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": "davanstrien/lightonocr-books-test",
		"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 "davanstrien/lightonocr-books-test" \
        --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": "davanstrien/lightonocr-books-test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use davanstrien/lightonocr-books-test with Docker Model Runner:
```
docker model run hf.co/davanstrien/lightonocr-books-test
```

Update README.md

by librarian-bot - opened Nov 4, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

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README.md CHANGED Viewed

@@ -1,7 +1,8 @@
 ---
 library_name: transformers
 license: apache-2.0
-base_model: lightonai/LightOnOCR-1B-1025
 tags:
 - generated_from_trainer
 model-index:
@@ -73,4 +74,4 @@ The following hyperparameters were used during training:
 - Transformers 5.0.0.dev0
 - Pytorch 2.9.0+cu128
 - Datasets 4.4.0
-- Tokenizers 0.22.1

 ---
 library_name: transformers
 license: apache-2.0
+base_model:
+- lightonai/LightOnOCR-1B-1025
 tags:
 - generated_from_trainer
 model-index:
 - Transformers 5.0.0.dev0
 - Pytorch 2.9.0+cu128
 - Datasets 4.4.0
+- Tokenizers 0.22.1