Link the code repository

README.md

@@ -55,7 +55,7 @@ This checkpoint is Stage 3 of the PersonaDrive pipeline (in-context fine-tuning)
 | Retrieved context points | K = 2 |
 | Simulator / benchmark | CARLA 0.9.15 / Bench2Drive |
 
-The retrieval head (Stages 1–2) is a separate model of about 2 M trained parameters. It is in the code repository: `persona_drive_rag/checkpoints/best_model.pt`.
+The retrieval head (Stages 1–2) is a separate model of about 2 M trained parameters. It is in the code repository: [`persona_drive_rag/checkpoints/best_model.pt`](https://github.com/PervasiveAutonomyLab/PersonaDrive/blob/main/persona_drive_rag/checkpoints/best_model.pt).
 
 ## Files
 
@@ -103,11 +103,11 @@ bash scripts/eval/test_one_lb1_route.sh
 
 - **Style:** set `PERSONADRIVE_ICL_PROFILE` to `fast` (aggressive), `default` (neutral, normal driving), `slow` (conservative) or `normal` (no style; the index built from SimLingo's PDM-lite data). `PERSONADRIVE_NUM_ICL=0` turns retrieval off.
 - **Retrieval data:** retrieval needs three things:
-  - the retrieval head, included in the code repository;
-  - the FAISS indices under `persona_drive_rag/`;
+  - the retrieval head, included in the [code repository](https://github.com/PervasiveAutonomyLab/PersonaDrive);
+  - the FAISS indices under `persona_drive_rag/`, built as in [Build the retrieval indices](https://github.com/PervasiveAutonomyLab/PersonaDrive#3-build-the-retrieval-indices);
   - the camera frames those indices point to.
 
-  The repository's `datasets/README.md` lists which archives to get for each style. Retrieved frames whose image is missing are skipped. If none can be loaded, the model runs without retrieval.
+  The repository's [`datasets/README.md`](https://github.com/PervasiveAutonomyLab/PersonaDrive/blob/main/datasets/README.md) lists which archives to get for each style. Retrieved frames whose image is missing are skipped. If none can be loaded, the model runs without retrieval.
 - **Paper query:** `PERSONADRIVE_LEGACY_ICL_QUERY=1` reproduces the retrieval query used for the paper's results.
 
 ## Results
@@ -138,7 +138,7 @@ From conservative to aggressive, speed rises by 19.8 % and acceleration by 24.9
   Each frame is paired with its K = 2 context points, retrieved from the index of the matching style.
 - **Settings:** LoRA on the language model (r 32, α 64, dropout 0.1) with the vision encoder frozen. AdamW at lr 3e-5 (OneCycle, weight decay 0.1); batch 6 per GPU × 4 GPUs with DeepSpeed ZeRO-2 in bf16; 1 epoch.
 - **Hardware:** 4× NVIDIA L40S (48 GB), about 12 h.
-- **Reproducing the run:** `python simlingo_training/train.py experiment=personadrive_icl_sft` in the code repository. The composed config of this run is `personadrive_icl_sft/.hydra/config.yaml`.
+- **Reproducing the run:** `python simlingo_training/train.py experiment=personadrive_icl_sft` in the [code repository](https://github.com/PervasiveAutonomyLab/PersonaDrive#full-pipeline). The composed config of this run is `personadrive_icl_sft/.hydra/config.yaml`.
 
 ## Intended use and limitations