---

language:

- en

tags:

- travel

- india

- distillation

- llama

- qlora

- itinerary-optimization

- chain-of-thought

license: apache-2.0

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

datasets:

- agurusantosh/tripmind-agent-traces

metrics:

- bertscore

- rouge

model-index:

- name: tripmind-distill

results:

- task:

type: text-generation

name: Travel Itinerary Optimization

metrics:

- type: json_valid

value: 0.924

name: JSON Validity Rate

- type: savings_valid

value: 0.981

name: Savings Found Rate

- type: bertscore_f1

value: 0.738

name: BERTScore F1

- type: reasoning_coherence

value: 0.674

name: Reasoning Coherence

---

tripmind-distill

Knowledge-distilled Llama 3.1 8B for Indian domestic travel optimization. Distilled from 500 multi-agent DeepSeek reasoning traces (Phase 2 of the TripMind pipeline), where a Supervisor + Analyst + Concierge + Optimizer chain used real MCP tool calls to build itineraries.

Part of the TripMind project. Unlike tripmind-ft (trained on clean synthetic pairs), this model was trained on agent reasoning chains — the hypothesis being that richer teacher signal improves generalization. Results were mixed: reasoning coherence improved, but structural output compliance dropped.

Model Details

| Property | Value |

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

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

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

| Training data | 449 Alpaca-format distillation pairs (Phase 2 agent traces) |

| Epochs | 5 |

| Final train loss | 0.429 |

| Hardware | Lightning.ai A100 (bf16, seq_len=16384) |

| Format | GGUF Q4_K_M (4.6 GB) |

The higher loss (0.429 vs 0.266 for ft) correlates with noisier training signal — agent traces include tool-call artifacts and variable output lengths that add training noise.

Evaluation Results (92 test cases)

| Metric | Score | Target | ✓/✗ |

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

| JSON valid | 92.4% | 85% | ✓ |

| Savings found | 98.1% | 70% | ✓ |

| Budget compliance | — | 80% | — |

| Schema compliance | 0.0% | 80% | ✗ |

| BERTScore F1 | 0.738 | 0.70 | ✓ |

| ROUGE-L | 0.090 | 0.25 | ✗ |

| Reasoning coherence | 0.674 | 0.65 | ✓ |

| Grounding accuracy | 0.442 | 0.60 | ✗ |

| Red-team pass | 46.7% | 80% | ✗ |

Schema compliance of 0% indicates the model produces valid JSON but with a different structure than the expected schema — a consequence of the diverse output formats in the distillation training data.

Usage with Ollama

ollama create tripmind-distill -f Modelfile.distill
ollama run tripmind-distill

Prompt format (Alpaca with reasoning chain instruction):

### Instruction:
Act as TripMind Supervisor for an Indian domestic trip. Coordinate the Analyst, Concierge, and Optimizer agents to find Price-Pivot Points and produce an optimized itinerary. Show the reasoning chain for each agent handoff, then provide the final pivot analysis and optimized itinerary.

### Input:
{"starting_city": "Mumbai", ...}

### Response:

Limitations

• Schema compliance is 0% — produces valid JSON but in a non-standard structure.
• Not recommended for production use without post-processing to extract the itinerary.
• Trained on only 449 examples (vs 4,749 for ft) — limited coverage of edge cases.

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