Add model card

README.md

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+---
+language:
+  - en
+tags:
+  - travel
+  - india
+  - fine-tuned
+  - llama
+  - qlora
+  - itinerary-optimization
+  - price-pivot
+license: apache-2.0
+base_model: unsloth/Meta-Llama-3.1-8B
+datasets:
+  - agurusantosh/tripmind-synthetic-v2
+metrics:
+  - bertscore
+  - rouge
+model-index:
+  - name: tripmind-ft
+    results:
+      - task:
+          type: text-generation
+          name: Travel Itinerary Optimization
+        metrics:
+          - type: json_valid
+            value: 1.00
+            name: JSON Validity Rate
+          - type: savings_valid
+            value: 1.00
+            name: Savings Found Rate
+          - type: budget_compliance
+            value: 0.987
+            name: Budget Compliance
+          - type: bertscore_f1
+            value: 0.932
+            name: BERTScore F1
+          - type: grounding_accuracy
+            value: 0.895
+            name: Grounding Accuracy
+          - type: red_team_pass
+            value: 0.533
+            name: Red-Team Robustness
+---
+
+# 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](https://github.com/agurusantosh/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
+
+```bash
+# 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).