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
bash
1ollama create tripmind-curriculum -f Modelfile.curriculum
2ollama 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
