Technical Architecture

The hybrid computing system, open-source stack, and sovereign data ingestion pipeline powering Project Pak-LLM.

Hardware Topology

Sandbox (Short-Term)

Google Colab Pro+

On-demand GPU acceleration (NVIDIA A100/H100 80GB) paired with high-RAM systems to conduct swift vocabulary sweeps, initial token training, and validation loops without CapEx.

On-Premise Node (Medium-Term)

Karachi Micro-Node

Secured locally managed Dell PowerEdge servers for clean data curation, vector embeddings, high-throughput model quantization, and local database security controls.

Distributed Scale (Long-Term)

Cloud Pre-training Arrays

On-demand multi-node H100 clusters orchestrated through Ray and DeepSpeed to run final high-parameter foundational training tasks.

Open-Source Core Stack

AI & Foundational

PyTorch, Transformers, DeepSpeed, vLLM

Low-latency inference engines and memory-efficient distributed model shards.

Agents & RAG

LangGraph, LlamaIndex, Qdrant

Hierarchical multi-agent workflow systems and highly queryable regional vector stores.

Microservices & APIs

FastAPI, Django, gRPC

Sub-millisecond inter-agent communication framework and web interface controllers.

Delivery & UI

Next.js, React, Vercel Edge

Responsive right-to-left (RTL) localization for local enterprise operational consoles.

Ingestion Pipeline Data Structures & Formats

Pipeline Step	Target Technology Stack	Data Input Schema	Processed Output Format
1. Data Extraction	Crawl4AI, Scrapy, Asyncio	Raw government/public registries HTML	Clean Markdown text tables
2. QA Structuring	Llama-3-8B-Instruct (local execution)	Unstructured Markdown logs	Structured instruction-response JSON Q&A
3. Adaptor Training	Byte-Pair Encoding, PEFT/QLoRA 4-bit	Instruction JSON datasets + base weights	QLoRA adapter checkpoint weights
4. Validation Judge	LLM-as-a-Judge (Eval Scorecard)	Trained response predictions	Evaluated accuracy scores (1-10 JSON scorecard)

Sovereign Data Ingestion Pipeline

Click through the steps below to simulate the closed-loop, automated data extraction, training, and validation cycle.

Active Step Pipeline Simulator

1. Extraction (Crawl4AI)

Crawl4AI / Scrapy / Asyncio

Asynchronous crawlers scrape regional trade registries and logistics files, bypassing JavaScript overlays and converting raw elements to clean Markdown tables.

pipeline_sandbox.py

# SIMULATED INPUT DATA

GET https://example-regional-trade-registry.gov.pk/supply-chain-reports

# SYSTEM PIPELINE OUTPUT

[
  {
    "source_url": "https://example-regional-trade-registry.gov.pk/supply-chain-reports",
    "raw_markdown": "# Karachi Port Logistics Reports\n| Date | Port Clearances | Delay Factors |\n|---|---|---|\n| 2026-06-21 | 14,200 TEU | Port clearance latency due to customs backup |"
  }
]