Development and deployment of a local LLM for classification and data extraction
557 USD 
Create an isolated microservice based on Open Source LLM for automatic processing of incoming documents (scans, PDF, photos).
2. Tasks of the executor
Hosting selection: Choose the optimal GPU hosting (for example, Hostkey, Selectel, RunPod, Lambda Labs or similar) considering the budget and performance requirements.
Model selection and configuration: It is recommended to use Llama 3.1 8B, Mistral Small 3 or Qwen 2.5. The use of quantized versions (4-bit/8-bit) is acceptable for resource savings.
Classification (5 types): The model must determine the type of document (for example: CTC, PTC, Passport, Contract, Invoice).
Data extraction: For each type of document, configure the extraction of key fields (for example, VIN, Full Name, document number, date).
API Development: Create a REST API (FastAPI/Flask) for uploading the document and receiving a JSON response.
3. Technical requirements
OCR layer: If the document is submitted as an image, a quality OCR must be configured (for example, EasyOCR, Tesseract or DocTR).
Inference engine: Use vLLM or Ollama to ensure high processing speed and support for queues.
Containerization: The entire stack (API + Model + OCR) must be packaged in Docker.
4. Output data format
The API must return structured JSON:
{
"class": "vehicle_registration",
"confidence": 0.95,
"data": {
"vin": "ABC123456789",
"reg_number": "X000XX77",
"year": 2022
},
"processing_time": "1.2s"
}-
какие типы документов будут обрабатываться. В идеале пример показать
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