Update README.md

README.md

@@ -11,9 +11,11 @@ tags:
 license: apache-2.0
 ---
 
-# DeepSignal-4B (GGUF)
+# DeepSignal-4B-V1 (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.
+For details, check our repository at [`AIMSLaboratory/DeepSignal`](https://github.com/AIMSLaboratory/DeepSignal/settings).
+
 
 ## Files
 
@@ -23,40 +25,38 @@ This repository provides a GGUF model file for local inference (e.g., `llama.cpp
 ## Quickstart (llama.cpp)
 
 ```bash
-llama-cli -m DeepSignal-4B_V1.F16.gguf -p "Summarize the traffic state and suggest a signal timing adjustment."
+llama-cli -m DeepSignal-4B_V1.F16.gguf -p "You are a traffic management expert. You can use your traffic knowledge to solve the traffic signal control task.
+Based on the given traffic {scene} and {state}, predict the next signal phase and its duration.
+You must answer directly, the format must be: next signal phase: {number}, duration: {seconds} seconds
+where the number is the phase index (starting from 0) and the seconds is the duration (usually between 20-90 seconds)."
 ```
 
+*You need to input the {scene} (total number of phases, which phases controls which lanes/directions and current phase ID/number, etc) and {state} (number of queing vehicles per lane, throughout vehicles per lane during the current phase, etc)*
+
 ## 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 |
+| Model | Avg Saturation | Avg Queue Length | Max Saturation | Max Queue Length | Avg Congestion Index |
+|---|---:|---:|---:|---:|---:|
+| [`Qwen3-30B-A3B`](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct) | 0.1550 | 5.5000 | 0.1550 | 5.4995 | 0.1500 |
+| DeepSignal-4B (Ours) | 0.1580 | 5.5500 | 0.1580 | 5.5498 | 0.1550 |
+| [`LightGPT-8B-Llama3`](https://huggingface.co/lightgpt/LightGPT-8B-Llama3) | 0.1720 | 6.1000 | 0.1720 | 6.1000 | 0.1950 |
+| SFT | 0.1780 | 6.2500 | 0.1780 | 6.2500 | 0.2050 |
+| Last Round GRPO | 0.1850 | 6.4500 | 0.1850 | 6.4500 | 0.2150 |
+| [`Qwen3-4B`](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) | 0.1980 | 7.2000 | 0.1980 | 7.1989 | 0.2450 |
+| Max Pressure | 0.2050 | 7.8000 | 0.2049 | 7.7968 | 0.2550 |
+| [`GPT-OSS-20B`](https://huggingface.co/openai/gpt-oss-20b) | 0.2250 | 8.5001 | 0.2250 | 8.4933 | 0.3050 |
 
 ### 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 |
+| DeepSignal-4B (Ours) | 0.00 | 12.00 | 88.00 |
+| [`GPT-OSS-20B`](https://huggingface.co/openai/gpt-oss-20b) | 2.00 | 53.33 | 44.67 |
+| [`LightGPT-8B-Llama3`](https://huggingface.co/lightgpt/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
+| [`Qwen3-30B-A3B`](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct) | 0.00 | 10.00 | 90.00 |
+| [`Qwen3-4B`](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) | 2.33 | 32.00 | 65.67 |
+| Qwen3-4B-SFT | 0.00 | 23.33 | 76.67 |
 
-- 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.