Upload GGUF model

README.md

@@ -1,3 +1,62 @@
----
-license: apache-2.0
----
+---
+language:
+  - en
+pipeline_tag: text-generation
+tags:
+  - gguf
+  - llama.cpp
+  - lmstudio
+  - traffic-signal-control
+  - simulation
+license: apache-2.0
+---
+
+# DeepSignal-4B (GGUF)
+
+This repository provides a GGUF model file for local inference (e.g., `llama.cpp` / LM Studio). It is intended for traffic-signal-control analysis and related text-generation workflows.
+
+## Files
+
+- `DeepSignal-4B_V1.F16.gguf`
+- `config.json`
+
+## Quickstart (llama.cpp)
+
+```bash
+llama-cli -m DeepSignal-4B_V1.F16.gguf -p "Summarize the traffic state and suggest a signal timing adjustment."
+```
+
+## Evaluation (Traffic Simulation)
+
+### Performance Metrics Comparison by Model
+
+| Model | Avg Saturation | Avg Queue Length | Max Saturation | Max Queue Length | Avg Throughput | Avg Congestion Index |
+|---|---:|---:|---:|---:|---:|---:|
+| Qwen3-30B-A3B | 0.1663 | 5.8604 | 0.1663 | 5.8604 | 9288.6633 | 0.1625 |
+| DeepSignal-4B | 0.1657 | 5.8301 | 0.1657 | 5.8301 | 9302.7960 | 0.1752 |
+| LightGPT-8B-Llama3 | 0.1538 | 5.7688 | 0.1538 | 5.7688 | 5510.2010 | 0.2086 |
+| SFT | 0.1604 | 6.0021 | 0.1604 | 6.0021 | 5390.4867 | 0.2093 |
+| Qwen3-4B | 0.2152 | 8.2083 | 0.2152 | 8.2083 | 7112.8433 | 0.2522 |
+| Max Pressure | 0.2059 | 8.1034 | 0.2059 | 8.1034 | 7395.1463 | 0.2556 |
+| GPT-OSS-20B | 0.2591 | 10.4292 | 0.2591 | 10.4292 | 8742.7267 | 0.3175 |
+
+### Congestion Level Distribution by Model (%)
+
+| Model | Light congestion | Smooth | Very smooth |
+|---|---:|---:|---:|
+| DeepSignal-4B | 0.00 | 12.00 | 88.00 |
+| GPT-OSS-20B | 2.00 | 53.33 | 44.67 |
+| LightGPT-8B-Llama3 | 0.00 | 21.00 | 79.00 |
+| Max Pressure | 0.00 | 36.44 | 63.56 |
+| Qwen3-30B-A3B | 0.00 | 10.00 | 90.00 |
+| Qwen3-4B | 2.33 | 32.00 | 65.67 |
+| SFT | 0.00 | 23.33 | 76.67 |
+
+### Visualization
+
+![Metrics Comparison](metrics_comparison_bars.png)
+
+## Notes
+
+- The results above are reported from a SUMO-based traffic simulation evaluation.
+- If you need to reproduce the evaluation, include the exact scenario configuration, random seeds, and controller settings in a separate README or paper appendix.