abrarali113/d360_vlm_unified

D360-VLM v4.0.0

D360-VLM v4.0.0 is the V4 unified enterprise document intelligence model for extraction, classification, and structured JSON generation from business documents, including noisy and partially degraded scans.

What's New In V4

• Migrates to one canonical d360_vlm runtime with legacy V3 API/inference wrappers for backward compatibility.
• Adds release gates for field extraction F1, document classification accuracy, confidence calibration, and JSON schema validity.
• Introduces a normalized business document schema (business-doc-v1) and a LLaVA-style image+instruction+JSON supervision manifest.
• Ships budget-guarded Modal training + packaging workflow for repeatable enterprise releases.

Models Used

• Primary V4 base model: HuggingFaceTB/SmolVLM-500M-Instruct
• Training objective: Vision-to-JSON instruction tuning (image + prompt -> structured response)
• Runtime surface: Unified d360_vlm engine + V3 compatibility endpoints

V4 Architecture

• Backbone: AutoModelForVision2Seq initialized from HuggingFaceTB/SmolVLM-500M-Instruct
• Processor: AutoProcessor multimodal tokenization for image + text sequences
• Supervision format: LLaVA-style examples (<image>, business prompt, target JSON response)
• Serving layer: Canonical FastAPI app in d360_vlm.api.app with V3-compatible routes and wrappers
• Quality gates: Extraction/classification metrics, confidence buckets (ECE), schema checks, and release readiness flags

Training Summary (Current V4 Run)

• Run name: d360_vlm_v4
• Train rows: unknown
• Eval rows: unknown
• Elapsed minutes: unknown
• Projected spend (USD): unknown (budget: unknown)
• Hardware profile: Modal L4 GPU with runtime budget guard

Evaluation Signals

• Release ready: None
• Field F1: None
• Document type accuracy: None
• Schema valid rate: None

Included In This HF Repo

• model/ trained model artifacts
• model.py runtime wrapper
• d360_vlm_v3_inference.py compatibility inference API
• d360_vlm_v3_api.py compatibility FastAPI entrypoint
• eval_gate_results.json gate report
• model_card.json structured release metadata

Intended Use

• Enterprise document workflows: forms, invoices, receipts, IDs, and mixed-layout business documents.
• Structured extraction and classification into downstream JSON contracts.

Limitations

• Performance is dataset-dependent; always validate on your own documents.
• Degraded handwriting and extreme low-resolution scans may reduce quality.
• Human review is recommended for high-risk compliance and financial workflows.

Repository

abrarali113/d360_vlm_unified