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tripmind-ft-gguf
agurusantosh/tripmind-ft-gguf
tripmind-ft-gguf is a machine learning model from agurusantosh. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
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…
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At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Jun 10, 2026
- Updated
- Jul 9, 2026
- SHA
- e7fa3c77
Base models
Model card
From the Hugging Face model README
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
- License
- apache-2.0
- Languages
- en
- Created
- Jun 10, 2026
- Updated
- Jul 9, 2026