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

Libraries

How to use PleIAs/OCRonos with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PleIAs/OCRonos")
model = AutoModelForCausalLM.from_pretrained("PleIAs/OCRonos")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use PleIAs/OCRonos with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/PleIAs/OCRonos

SGLang

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

Docker Model Runner
How to use PleIAs/OCRonos with Docker Model Runner:
```
docker model run hf.co/PleIAs/OCRonos
```

Pclanglais commited on Jul 18, 2024

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@@ -12,7 +12,7 @@ language:
 OCROnos models are versatile tools supporting the correction of OCR errors, wrong word cut/merge and overall broken text structures. The training data includes a highly diverse set of ocrized texts in multiple languages from PleIAs open pre-training corpus, drawn from cultural heritage sources (Common Corpus) and financial and administrative documents in open data (Finance Commons).
-This release currently features a model based on llama-3-8b that has been the most tested to date. Future release will focus on smaller internal models that provides a better ratio of generation cost/quality.
 OCRonos is generally faithful to what the original material, provides sensible restitution of deteriorated text and will rarely rewrite correct words. On highly deteriorated content, OCRonos can act as a synthetic rewriting tool rather than a strict correction tool.

 OCROnos models are versatile tools supporting the correction of OCR errors, wrong word cut/merge and overall broken text structures. The training data includes a highly diverse set of ocrized texts in multiple languages from PleIAs open pre-training corpus, drawn from cultural heritage sources (Common Corpus) and financial and administrative documents in open data (Finance Commons).
+This release currently features a model based on llama-3-8b that has been the most tested to date. The model was trained using HPC resources from GENCI–IDRIS (Grant 2023-AD011014736) on Jean-Zay. Future release will focus on smaller internal models that provides a better ratio of generation cost/quality.
 OCRonos is generally faithful to what the original material, provides sensible restitution of deteriorated text and will rarely rewrite correct words. On highly deteriorated content, OCRonos can act as a synthetic rewriting tool rather than a strict correction tool.