Instructions to use Phu-Hien/LightOnOCR-2-ft-01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Phu-Hien/LightOnOCR-2-ft-01 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("lightonai/LightOnOCR-2-1B-base")
model = PeftModel.from_pretrained(base_model, "Phu-Hien/LightOnOCR-2-ft-01")

Transformers

How to use Phu-Hien/LightOnOCR-2-ft-01 with Transformers:

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

pipe = pipeline("text-generation", model="Phu-Hien/LightOnOCR-2-ft-01")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Phu-Hien/LightOnOCR-2-ft-01", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Phu-Hien/LightOnOCR-2-ft-01 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Phu-Hien/LightOnOCR-2-ft-01"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Phu-Hien/LightOnOCR-2-ft-01",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Phu-Hien/LightOnOCR-2-ft-01

SGLang

How to use Phu-Hien/LightOnOCR-2-ft-01 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 "Phu-Hien/LightOnOCR-2-ft-01" \
    --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": "Phu-Hien/LightOnOCR-2-ft-01",
		"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 "Phu-Hien/LightOnOCR-2-ft-01" \
        --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": "Phu-Hien/LightOnOCR-2-ft-01",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Phu-Hien/LightOnOCR-2-ft-01 with Docker Model Runner:
```
docker model run hf.co/Phu-Hien/LightOnOCR-2-ft-01
```

LightOnOCR-2-ft-01

This model is a fine-tuned version of lightonai/LightOnOCR-2-1B-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0613

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 6e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 4
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 10
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
0.1561	0.1074	50	0.1297
0.1339	0.2148	100	0.0887
0.0739	0.3222	150	0.0774
0.0615	0.4296	200	0.0714
0.0698	0.5371	250	0.0678
0.0716	0.6445	300	0.0646
0.0589	0.7519	350	0.0628
0.0563	0.8593	400	0.0616
0.0737	0.9667	450	0.0613

Framework versions

PEFT 0.19.1
Transformers 5.0.0
Pytorch 2.11.0+cu128
Datasets 4.8.5
Tokenizers 0.22.2

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