| --- |
| 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 |
|
|
| ```json |
| { |
| "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 |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("Aravind2701/logistics-copilot-dataset") |
| ``` |
|
|
| Train with MLX + LoRA: |
|
|
| ```bash |
| 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 |
|
|
