README.md

metadata

language:
  - en
task_categories:
  - text-generation
tags:
  - logistics
  - fine-tuning
  - synthetic
  - gemma
  - lora
  - mlx
  - supply-chain
  - india
pretty_name: AI Logistics Copilot Dataset
size_categories:
  - 10K<n<100K

AI Logistics Copilot Dataset

A synthetic dataset of 50,000 instruction-following examples for fine-tuning a Logistics Copilot that helps small-scale manufacturers in India prevent missed pickups, predict SLA breaches, and automate operational decisions.

Problem Statement

Small-scale manufacturers frequently miss delivery deadlines when logistics providers fail to arrive for scheduled pickups — with no backup plan, no early warning, and no automated communication to customers.

This dataset trains a model to be the intelligence layer that solves this.

Dataset Details

• Total examples: 50,000
• Train split: 47,500
• Validation split: 2,500
• Format: Instruction-following (### User: / ### Assistant:)
• Base model used for fine-tuning: Gemma 4B (MLX)
• Fine-tuning method: LoRA (rank 8)

Task Categories

Category	Count	%
Pickup Risk Assessment	~9,100	18%
SLA Breach Prediction	~6,000	12%
Root Cause Analysis	~6,000	12%
Carrier Selection	~5,000	10%
Backup Carrier Recommendation	~5,000	10%
Customer Notification Drafting	~5,000	10%
Delay Escalation Handling	~4,000	8%
Optimal Pickup Time Suggestion	~3,000	6%
Historical Pattern Analysis	~3,000	6%
Operational Shift Summary	~2,000	4%
Conversational Follow-ups	~2,000	4%

Domain Coverage

• 15 carriers with distinct reliability profiles (42% to 98%)
• 20 Indian cities as origin/destination (Chennai, Bangalore, Mumbai, etc.)
• 30 manufacturing customers (Toyota, Bosch, Tata Motors, JSW Steel, etc.)
• 20 delay reasons (driver unavailable, breakdown, weather, traffic, etc.)
• Vehicle types: Mini Truck to 40ft Container
• Cargo types: Auto components, pharmaceuticals, steel, textiles, etc.

Sample

{
  "text": "### User:\nAssess pickup risk for this shipment.\n\nCarrier: ABC Logistics\nReliability: 42%\nFactory: Chennai\nDestination: Bangalore\nPickup Time: 9:00 AM\nDay: Monday\nDriver Status: Not Assigned\nHistorical Failures (last 90 days): 6\nCargo Weight: 8.5 tons\nWeather: Heavy Rain\nCustomer: Toyota\nPriority: Critical\n\n### Assistant:\nRisk Level: Critical\n\nReason:\n- Carrier reliability is low at 42%\n- Carrier has 6 failures in the last 90 days\n- Driver status is 'Not Assigned'\n- Adverse weather: Heavy Rain\n\nRecommended Actions:\n1. Immediately assign a backup transporter.\n2. Notify warehouse to prepare for delayed dispatch.\n3. Inform Toyota about potential delay.\n4. Escalate to operations manager."
}

Fine-Tuning Usage

from datasets import load_dataset

dataset = load_dataset("Aravind2701/logistics-copilot-dataset")

Train with MLX + LoRA:

python -m mlx_lm lora \
  --model ./gemma4-e4b-mlx \
  --data ./data_logistics \
  --adapter-path ./adapters_logistics \
  --train \
  --iters 1000 \
  --batch-size 1 \
  --learning-rate 1e-5 \
  --num-layers 16

What the Fine-Tuned Model Learns

1. Risk scoring — Maps carrier reliability + driver status + weather → risk level
2. Decision logic — When to escalate (P1/P2/P3/P4) vs monitor vs assign backup
3. Domain language — SLA breach, carrier blacklisting, escalation tiers
4. Structured responses — Risk Level, Reason, Recommended Actions format
5. Pattern recognition — "Carrier A fails on Mondays", "avoid pickups after 5 PM"

License

MIT