add scripts/run_resume_stage2.sh

Resume the GRPO curriculum from stage 2 by pulling the SFT and
stage1 LoRA adapters from the Hub. Used after an HF Spaces /data
wipe so we don't redo SFT and stage1 work that already survived.

Made-with: Cursor

scripts/run_resume_stage2.sh

@@ -0,0 +1,115 @@
+#!/usr/bin/env bash
+# Resume the GRPO curriculum from stage 2.
+#
+# Assumes:
+#   - Stages 1 has already been pushed to
+#       shivam2k3/opensoc-defender-grpo-stage1_basic
+#   - SFT adapter has been pushed to
+#       shivam2k3/opensoc-defender-grpo-sft
+#
+# Run this from the project root *after* /data has been wiped and the
+# repo has been re-cloned. Safe to re-run.
+set -euo pipefail
+
+# Reuse the install logic from run_full_pipeline.sh by sourcing only
+# steps 1 and 2. We'd rather duplicate a few lines than risk source-ing
+# a script that exits early if SFT adapter is missing.
+echo "[1/6] Installing GPU stack ..."
+pip install -q --upgrade pip setuptools wheel
+pip install -q "future>=1.0.0"
+pip install -q "torch==2.10.0" "torchvision==0.25.0" \
+    --index-url https://download.pytorch.org/whl/cu128
+pip install -q "transformers>=4.46,<5"
+pip install -q --no-deps "unsloth @ git+https://github.com/unslothai/unsloth.git"
+pip install -q --no-deps "unsloth_zoo"
+pip install -q --no-deps "trl>=0.18.2,<=0.24.0" peft accelerate bitsandbytes
+pip install -q "datasets>=3.4.1,<4.4.0" tyro tensorboard matplotlib sentencepiece protobuf huggingface_hub
+pip install -q hf_transfer msgspec "torchao>=0.13.0" cut_cross_entropy || true
+pip install -q -r requirements.txt
+
+echo "[2/6] Building / verifying datasets ..."
+python -m data.build_holdout
+
+echo "[3/6] Skipped (SFT + Stage 1 already on Hub)."
+
+echo "[3.5/6] Pulling SFT and Stage 1 adapters from the Hub ..."
+python - <<'PY'
+import os
+from huggingface_hub import snapshot_download
+sft = snapshot_download(
+    "shivam2k3/opensoc-defender-grpo-sft",
+    local_dir="checkpoints/defender_sft_adapter",
+    local_dir_use_symlinks=False,
+)
+print("SFT adapter ->", sft)
+stage1 = snapshot_download(
+    "shivam2k3/opensoc-defender-grpo-stage1_basic",
+    local_dir="checkpoints/defender_grpo/stage1_basic/adapter",
+    local_dir_use_symlinks=False,
+)
+print("Stage 1 adapter ->", stage1)
+PY
+
+echo "[4/6] Resuming GRPO from stage 2 (3 remaining stages) ..."
+python -m train.train_grpo \
+  --model unsloth/Qwen2.5-3B-Instruct \
+  --sft-adapter checkpoints/defender_grpo/stage1_basic/adapter \
+  --stages stage2_multi,stage3_mixed,stage4_adversarial \
+  --steps-per-stage 200 --num-generations 8 \
+  --batch-size 2 --grad-accum 4 --lr 5e-6 \
+  --report-to tensorboard \
+  --out checkpoints/defender_grpo
+
+echo "[5/6] Eval + plots ..."
+python -m eval.eval \
+  --baseline unsloth/Qwen2.5-3B-Instruct \
+  --trained-adapter checkpoints/defender_grpo/stage4_adversarial/adapter \
+  --holdout data/holdout.jsonl --out-dir eval/results
+python -m eval.plot_results  --in eval/results/summary.json --out-dir eval/results
+python -m eval.plot_training --grpo-root checkpoints/defender_grpo --out-dir eval/results
+
+echo "[6/6] Baking demo data for the Gradio /demo Space ..."
+python -m eval.bake_demo \
+  --baseline unsloth/Qwen2.5-3B-Instruct \
+  --trained-adapter checkpoints/defender_grpo/stage4_adversarial/adapter \
+  --n 50 --out data/demo_examples.json
+
+if [ -n "${HF_TOKEN:-}" ] && [ -n "${HF_PUSH_TARGET:-}" ]; then
+  echo "[7/7] Uploading artifacts back to ${HF_PUSH_TARGET} ..."
+  python - <<'PY'
+import os
+from huggingface_hub import HfApi, upload_folder
+token = os.environ["HF_TOKEN"]
+target = os.environ["HF_PUSH_TARGET"]
+adapter_repo = os.environ.get(
+    "HF_ADAPTER_REPO", target.split("/")[0] + "/opensoc-defender-grpo"
+)
+api = HfApi(token=token)
+adapter_dir = "checkpoints/defender_grpo/stage4_adversarial/adapter"
+if os.path.isdir(adapter_dir):
+    api.create_repo(adapter_repo, exist_ok=True, private=False)
+    upload_folder(repo_id=adapter_repo, folder_path=adapter_dir,
+                  commit_message="GRPO-trained Qwen2.5-3B-Instruct LoRA defender adapter",
+                  token=token)
+    print("  adapter ->", adapter_repo)
+for p in [
+    "data/demo_examples.json",
+    "eval/results/summary.json",
+    "eval/results/bar_macro_f1.png",
+    "eval/results/bar_dismiss_on_malicious.png",
+    "eval/results/confusion_baseline_zero_shot.png",
+    "eval/results/confusion_opensoc_grpo.png",
+    "eval/results/training_curves.png",
+    "eval/results/training_kl_loss.png",
+]:
+    if os.path.exists(p):
+        api.upload_file(
+            path_or_fileobj=p, path_in_repo=p,
+            repo_id=target,
+            commit_message="trained: refresh " + os.path.basename(p),
+            token=token,
+        )
+        print("  ", p, "->", target)
+print("PIPELINE_DONE")
+PY
+fi