---

language:

- en

tags:

- travel

- india

- curriculum-learning

- llama

- qlora

- itinerary-optimization

- grounding

license: apache-2.0

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

datasets:

- agurusantosh/tripmind-synthetic-v2

- agurusantosh/tripmind-agent-traces

metrics:

- bertscore

model-index:

- name: tripmind-curriculum

results:

- task:

type: text-generation

name: Travel Itinerary Optimization

metrics:

- type: grounding_accuracy

value: 0.88

name: Grounding Accuracy

- type: bertscore_f1

value: 0.734

name: BERTScore F1

- type: red_team_pass

value: 0.60

name: Red-Team Robustness

---

tripmind-curriculum

Curriculum-trained Llama 3.1 8B for Indian domestic travel optimization. Uses two-stage sequential training: first on 4,749 Phase 1 synthetic pairs (domain knowledge), then on 449 Phase 2 agent reasoning traces (complex reasoning patterns).

Part of the TripMind project. The curriculum hypothesis was that domain knowledge should precede complex reasoning patterns — similar to how students learn fundamentals before advanced topics. Results revealed an interesting trade-off: the model achieved the highest grounding accuracy (88%) and best red-team robustness (60%) of the three variants, but the Phase 2 training stage catastrophically disrupted structured JSON output (10.9% validity).

Model Details

| Property | Value |

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

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

| Training method | QLoRA r=8, α=16, dropout=0.05 (2-stage) |

| Stage 1 data | 4,749 pairs (Phase 1 synthetic) — 424 steps |

| Stage 2 data | 449 pairs (Phase 2 agent traces) — 171 steps |

| Final train loss | 0.313 (Stage 2) |

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

| Format | GGUF Q4_K_M (4.6 GB) |

Evaluation Results (92 test cases)

| Metric | Score | Target | ✓/✗ |

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

| JSON valid | 10.9% | 85% | ✗ |

| Savings found | — | 70% | — |

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

| BERTScore F1 | 0.734 | 0.70 | ✓ |

| Intent alignment | 0.418 | 0.55 | ✗ |

| Grounding accuracy | 0.880 | 0.60 | ✓ |

| Reasoning coherence | 0.470 | 0.65 | ✗ |

| Red-team pass | 60.0% | 80% | ✗ |

Notable: Despite near-zero JSON validity, grounding accuracy (0.88) nearly matches tripmind-ft (0.895). The model has absorbed real-world knowledge about Indian cities and travel patterns — it simply cannot format the output as valid JSON after Phase 2 training overwrote structured-output behavior.

Recommendation: Use with JSON-constrained decoding (llama.cpp --grammar, Outlines, or similar) to recover structured output. The underlying knowledge is strong.

Usage with Ollama

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

Note: Due to low JSON validity in standard inference, consider using grammar-constrained decoding for reliable structured output.

Limitations

• JSON validity is 10.9% — standard inference rarely produces valid JSON. Use grammar-constrained decoding.
• The Phase 2 curriculum stage appears to have overwritten Phase 1 structured-output training — a known curriculum learning failure mode.
• Despite strong semantic knowledge, the model cannot be used without output post-processing.

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