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tripmind-distill-gguf
agurusantosh/tripmind-distill-gguf
tripmind-distill-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.
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 + Optimiz…
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At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Jun 10, 2026
- Updated
- Jul 9, 2026
- SHA
- 1f8ae200
Base models
Model card
From the Hugging Face model README
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
- License
- apache-2.0
- Languages
- en
- Created
- Jun 10, 2026
- Updated
- Jul 9, 2026