agurusantosh/tripmind-distill-gguf

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