PrinceRansom7/gemma4-e2b-it-regulatory-obligation-v1

VISIONConcurrency Cost:1Model Size:5.1BQuant:BF16Ctx Length:32kTool Calling:SupportedPublished:Jun 4, 2026License:apache-2.0Architecture:Transformer Open Weights Cold

The PrinceRansom7/gemma4-e2b-it-regulatory-obligation-v1 is a 5.1 billion parameter Gemma 4 E2B IT-based Small Language Model (SLM) fine-tuned by PrinceRansom7. It is specifically optimized for extracting regulatory obligations from legal and compliance documents, converting unstructured text into structured JSON for Governance, Risk, and Compliance (GRC) applications. This model excels at identifying obligations, non-obligations, and neutral statements, and extracting detailed information like subject, action, and deadlines.

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Overview

This model, Gemma 4 E2B IT – Regulatory Obligation Extraction v1, is a specialized 5.1 billion parameter Small Language Model (SLM) developed by PrinceRansom7. It is built upon the Google Gemma 4 E2B IT architecture and fine-tuned using QLoRA (4-bit NF4) for domain-specific tasks. Unlike general-purpose LLMs, this model is engineered to process legal and compliance documents, transforming unstructured regulatory text into structured JSON output.

Key Capabilities

  • Regulatory Obligation Extraction: Identifies and extracts specific regulatory obligations from text.
  • Structured JSON Generation: Converts extracted information into a structured JSON format, including subject, action, modality, conditions, and deadlines.
  • Text Classification: Categorizes regulatory text into 'Obligation', 'Non-Obligation', or 'Neutral Statement'.
  • Low-Memory Inference: Optimized for efficient inference, making it suitable for environments with limited resources.
  • Legal NLP Focus: Instruction-tuned specifically for legal Natural Language Processing tasks within compliance automation workflows.

Intended Applications

This model is designed for use in:

  • Compliance monitoring and automation pipelines.
  • Legal document parsing and regulatory change management.
  • Governance, Risk, and Compliance (GRC) systems.
  • Knowledge Graph construction and Retrieval-Augmented Generation (RAG) for regulatory intelligence.

Limitations

It is important to note that the model may miss implicit obligations, misclassify ambiguous language, or produce incomplete JSON for very long documents. It is not a substitute for legal professionals and its outputs require human validation for high-risk compliance decisions.

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