Instructions to use titou4ng/smolified-ocr-data-extract-and-compare with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use titou4ng/smolified-ocr-data-extract-and-compare with Transformers:

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

pipe = pipeline("text-generation", model="titou4ng/smolified-ocr-data-extract-and-compare")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("titou4ng/smolified-ocr-data-extract-and-compare", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use titou4ng/smolified-ocr-data-extract-and-compare with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "titou4ng/smolified-ocr-data-extract-and-compare"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "titou4ng/smolified-ocr-data-extract-and-compare",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/titou4ng/smolified-ocr-data-extract-and-compare

SGLang

How to use titou4ng/smolified-ocr-data-extract-and-compare 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 "titou4ng/smolified-ocr-data-extract-and-compare" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "titou4ng/smolified-ocr-data-extract-and-compare",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "titou4ng/smolified-ocr-data-extract-and-compare" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "titou4ng/smolified-ocr-data-extract-and-compare",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use titou4ng/smolified-ocr-data-extract-and-compare with Docker Model Runner:
```
docker model run hf.co/titou4ng/smolified-ocr-data-extract-and-compare
```

🤏 smolified-ocr-data-extract-and-compare

Intelligence, Distilled.

This is a Domain Specific Language Model (DSLM) generated by the Smolify Foundry.

It has been synthetically distilled from SOTA reasoning engines into a high-efficiency architecture, optimized for deployment on edge hardware (CPU/NPU) or low-VRAM environments.

📦 Asset Details

Origin: Smolify Foundry (Job ID: 790dd5fa)
Architecture: DSLM-Micro (270M Parameter Class)
Training Method: Proprietary Neural Distillation
Optimization: 4-bit Quantized / FP16 Mixed
Dataset: Link to Dataset

🚀 Usage (Inference)

This model is compatible with standard inference backends like vLLM.

# Example: Running your Sovereign Model
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "titou4ng/smolified-ocr-data-extract-and-compare"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

messages = [
    {'role': 'system', 'content': '''You are an OCR data extraction and comparison engine. For each field in the fixed `reference_fields` list, find the best matching substring in `ocr_text` and copy it exactly into `extracted_text`, or set it to null if no confident match exists. Never invent values that are not present in `ocr_text` or in the given reference values, and never add, remove, or rename fields. Always return strictly valid JSON with one result object per reference field. Always return exactly 12 fields.'''},
    {'role': 'user', 'content': '''ocr_text: "Date: 14/11/2023\nRef. Ticket: 90021\nSite D'origine: CARRIERES DE L'OUEST SARL Siret 19876543210000\nAdresse: 25 RUE DE LA ROCHE, 49000 ANGERS\nSite De Destination: CENTRALE BETON DU VAL DE LOIRE SAS 10293847560000\nAdresse: 12 CHEMIN DU MOULIN, 37000 TOURS\nType De Matériau: GRAVIERS\nPoids NET: 45.0 T"'''}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize = False,
    add_generation_prompt = True,
).removeprefix('<bos>')

from transformers import TextStreamer
_ = model.generate(
    **tokenizer(text, return_tensors = "pt").to("cuda"),
    max_new_tokens = 1000,
    temperature = 1, top_p = 0.95, top_k = 64,
    streamer = TextStreamer(tokenizer, skip_prompt = True),
)

⚖️ License & Ownership

This model weights are a sovereign asset owned by titou4ng. Generated via Smolify.ai.

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Safetensors

Model size

0.3B params

Tensor type

BF16