Engineering MVP

Dolphin SSLM

A corporate-focused Small Language Model system designed to demonstrate disciplined AI behavior, strict identity control, and enterprise-grade safety boundaries.

View Repository Status: Research Completed

Purpose

This project represents an architectural proof, not marketing hype. It exists to demonstrate:

Controlled Identity

No hallucinated founders, brands, or origins.

Business Domain Only

Strict adherence to professional topics.

LoRA Architecture

Parameter-efficient fine-tuning methodology.

Runtime Guards

Intent enforcement during inference.

System Overview

Inference Flow

User Input
Intent Guards
Identity Injection
Base + LoRA
Sanitization

Runtime controls are intentionally strict to prevent identity leakage, hallucinations, and unsafe outputs.

Model Scope (MVP)

Technical Specs

  • Base Model: Phi-3 (MVP Only)
  • Method: LoRA Fine-Tuning
  • Future: Proprietary Base Models

Safety & Identity

  • Canonical Identity Lock: Single source of truth for origin and purpose.
  • Keyword Filters: Prevents external brand and personality leakage.
In Scope
  • • Business fundamentals
  • • Finance concepts
  • • Enterprise automation
Out of Scope
  • • Entertainment & Politics
  • • Personal advice
  • • Explicit content

Repository Structure

dolphin_sslm/ ├── cleaned_data/ # Training datasets (identity, refusal, business) ├── interface/ # Runtime inference (run.py) ├── model/ # MVP base model files ├── training/ # LoRA training pipeline ├── prompts/ # Prompt experiments ├── rag_docs/ # Optional reference material ├── .gitignore └── README.md

Running the MVP

Requirements: Python 3.11, PyTorch, Transformers, PEFT

Inference

cd interface python run.py # Type 'exit' to quit

Training (Experimental)

cd training python train_lora.py

Project Status

  • MVP completed
  • Safety hardening in progress
  • Custom base model roadmap planned

Founder

Soham Das

Soham Das

Founder, Dol Tech Labs