Add model card for continker/Qwen3.5-9B-metro-v23

README.md

@@ -0,0 +1,104 @@
+---
+license: apache-2.0
+language:
+- en
+base_model: Qwen/Qwen3.5-9B
+tags:
+- transit
+- kiosk
+- tool-use
+- agent
+- metrollm-bench
+- qwen
+- lora
+- gguf
+- quantized
+library_name: peft
+pipeline_tag: text-generation
+---
+
+# Qwen3.5-9B + metro-v23 LoRA
+
+Domain-specialised tool-using agent for transit-kiosk tasks: routing, fare calculation,
+disruption advisories, accessibility, multilingual cultural notes, multi-turn context tracking,
+and policy adaptation across 6 metro systems (MARTA, BART, CTA, Doha, Taipei MRT, Beijing
+Subway).
+
+QLoRA r=16 fine-tune of `Qwen/Qwen3.5-9B` on 790 distilled traces from
+Qwen3.5-27B and Qwen3.5-35B-A3B teachers (filtered to tier1 ≥ 90% per case, deduplicated
+by case_id, evaluated on the [MetroLLM-Bench](https://github.com/...) v23 harness).
+
+## Files
+
+| File | Purpose |
+|---|---|
+| `Qwen3.5-9B-metro-v23-Q4_K_M.gguf` (5.3 GB) | Runtime artifact for llama.cpp / Ollama |
+| `adapter/` | Raw LoRA adapter (use with PEFT + base Qwen3.5-9B) |
+| `training_summary.json` | Hyperparameters, seed, dataset version |
+
+## Eval (v23, 6 systems, Haiku judge for Tier 2)
+
+**Cross-system average**: Tier-1 92.4, Composite 90.0
+(+2.2 T1 / +1.4 Comp vs base Qwen3.5-9B)
+
+| System | Tier-1 % |
+|---|---:|
+| MARTA | 94.0 |
+| BART | 90.7 |
+| CTA | 93.4 |
+| DOHA | 93.1 |
+| TAIPEI | 92.6 |
+| BEIJING | 90.7 |
+
+## Quickstart (llama.cpp)
+
+```bash
+huggingface-cli download continker/Qwen3.5-9B-metro-v23 \
+  Qwen3.5-9B-metro-v23-Q4_K_M.gguf --local-dir ./models
+
+llama-server -m ./models/Qwen3.5-9B-metro-v23-Q4_K_M.gguf \
+  --port 8080 --ctx-size 32768 --n-gpu-layers 999
+```
+
+## Quickstart (PEFT adapter, Python)
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B", torch_dtype="bfloat16")
+model = PeftModel.from_pretrained(base, "continker/Qwen3.5-9B-metro-v23", subfolder="adapter")
+tokenizer = AutoTokenizer.from_pretrained("continker/Qwen3.5-9B-metro-v23", subfolder="adapter")
+```
+
+## Training
+
+- Base: `Qwen/Qwen3.5-9B`
+- Method: QLoRA, rank=16, alpha=32, dropout=0.05
+- Targets: q/k/v/o + gate/up/down projections
+- Optimizer: AdamW, lr=2e-4, cosine, warmup 5%
+- Epochs: 3, effective batch 8 (per_device_train_batch_size=2 × grad_accum=4)
+- Max sequence length: 4096
+- Seed: 42 (default; multi-seed CI in progress for 27B)
+- Dataset: 790 distilled examples, see [continker/metrollm-bench-train-data-v23](https://huggingface.co/datasets/continker/metrollm-bench-train-data-v23)
+
+## Limitations
+
+- Trained on 6 metro systems; generalisation to other systems untested.
+- Tool-use schema is specific to the MetroLLM-Bench mock server (route_planner,
+  fare_calculator, station_info, disruption_feed, knowledge_base,
+  submit_assistant_state).
+- Quantised to 4-bit (Q4_K_M); for full-precision behaviour use the adapter on
+  bf16 base weights.
+
+## Citation
+
+```
+@misc{metrollm-bench-2026,
+  title={MetroLLM-Bench: Evaluating LLMs as Prompt-Driven Transit Kiosk Agents},
+  author={Hendriks, Remco and contributors},
+  year={2026},
+  publisher={HuggingFace},
+  howpublished={\url{https://huggingface.co/continker}}
+}
+```