---

language:

- en

tags:

- travel

- india

- fine-tuned

- llama

- qlora

- itinerary-optimization

- price-pivot

license: apache-2.0

base_model: unsloth/Meta-Llama-3.1-8B

datasets:

- agurusantosh/tripmind-synthetic-v2

metrics:

- bertscore

- rouge

model-index:

- name: tripmind-ft

results:

- task:

type: text-generation

name: Travel Itinerary Optimization

metrics:

- type: json_valid

value: 1.00

name: JSON Validity Rate

- type: savings_valid

value: 1.00

name: Savings Found Rate

- type: budget_compliance

value: 0.987

name: Budget Compliance

- type: bertscore_f1

value: 0.932

name: BERTScore F1

- type: grounding_accuracy

value: 0.895

name: Grounding Accuracy

- type: red_team_pass

value: 0.533

name: Red-Team Robustness

---

tripmind-ft

Fine-tuned Llama 3.1 8B for Indian domestic travel optimization. Given a traveler persona, generates an optimized day-by-day itinerary identifying Price-Pivot Points — transit, accommodation, or activity substitutions that save ≥5% without degrading trip quality.

Part of the TripMind project: a multi-agent AI travel optimizer trained via three distinct approaches (SFT, distillation, curriculum). tripmind-ft is the best-performing variant, trained via standard supervised fine-tuning on 5,000 synthetic pairs generated by GPT-4o-mini.

Model Details

| Property | Value |

|----------|-------|

| Base model | unsloth/Meta-Llama-3.1-8B |

| Training method | QLoRA r=8, α=16, dropout=0.05 |

| Training data | 4,749 Alpaca-format pairs (Phase 1 synthetic) |

| Epochs | 3 |

| Final train loss | 0.266 |

| Hardware | Colab T4 (fp16, seq_len=512) |

| Format | GGUF Q4_K_M (4.6 GB) |

Evaluation Results (92 test cases)

| Metric | Score | Target | ✓/✗ |

|--------|:-----:|:------:|:---:|

| JSON valid | 100% | 85% | ✓ |

| Savings found | 100% | 70% | ✓ |

| Budget compliance | 98.7% | 80% | ✓ |

| Schema compliance | 83.7% | 80% | ✓ |

| BERTScore F1 | 0.932 | 0.70 | ✓ |

| ROUGE-L | 0.436 | 0.25 | ✓ |

| Reasoning coherence | 0.723 | 0.65 | ✓ |

| Grounding accuracy | 0.895 | 0.60 | ✓ |

| Intent alignment | 0.322 | 0.55 | ✗ |

| Red-team pass | 53.3% | 80% | ✗ |

Head-to-head: beats tripmind-distill 78% of the time, tripmind-curriculum 57%.

Usage with Ollama

# Download GGUF from this repo
ollama create tripmind-ft -f Modelfile.ft

# Run
ollama run tripmind-ft

Prompt format (Alpaca):

### Instruction:
Act as TripMind Optimizer. Given a traveler persona for an Indian domestic trip, produce an optimized day-by-day itinerary that minimizes total cost while respecting the budget tier, trip type, and traveler intents. Identify the primary Price-Pivot Point (transit, accommodation, or activity substitution that saves ≥5%) and explain it clearly.

### Input:
{"starting_city": "Mumbai", "destination_city": "Delhi", "type": "Solo", "size": {"adults": 1, "children": 0}, "intents": ["Adventure"], "budget": "Shoestring", "duration_days": 5, "duration_nights": 4}

### Response:

Limitations

• Trained on Indian domestic travel only (20 cities). Not designed for international travel.
• Red-team robustness is below target (53.3% vs 80% goal) — the model can be prompted to bypass budget constraints.
• Intent alignment is below target (32.2% vs 55%) — cost optimization is prioritized over activity personalization.
• Inference on CPU takes 30–120 seconds per query (use GPU for production).

Citation

If you use this model, please cite:

Patnaik, A. V. S. (2026). Cost-Matched Data Generation for LLM Fine-Tuning: Comparing
Supervised Fine-Tuning, Knowledge Distillation, and Curriculum Learning for an Agentic
Travel-Planning System. Zenodo. https://doi.org/10.5281/zenodo.21198884