Add SKIP_EVAL flag to sft_on_hf.py for faster training-only runs

scripts/sft_on_hf.py

@@ -122,14 +122,18 @@ def main():
         sft_args.extend(["--max_steps", str(MAX_STEPS)])
     run(sft_args)
 
-    # 4. Eval (3 conditions: discrete-3 / in-dist / OOD)
-    eval_args = [
-        "python", "training/inference_eval.py",
-        "--model_path", OUTPUT_DIR,
-        "--num_episodes", "5",
-        "--output_file", "eval_results.json",
-    ]
-    run(eval_args)
+    # 4. Eval (optional — set SKIP_EVAL=1 to upload faster and run eval separately)
+    skip_eval = os.environ.get("SKIP_EVAL", "0") == "1"
+    if not skip_eval:
+        eval_args = [
+            "python", "training/inference_eval.py",
+            "--model_path", OUTPUT_DIR,
+            "--num_episodes", "5",
+            "--output_file", "eval_results.json",
+        ]
+        run(eval_args)
+    else:
+        print("SKIP_EVAL=1: skipping embedded eval (run scripts/eval_on_hf.py separately)")
 
     # 5. Upload to HF Hub
     token = os.environ.get("HF_TOKEN")
@@ -149,18 +153,22 @@ def main():
         repo_type="model",
         commit_message=f"SFT prime ({EPOCHS} epochs, lora r={LORA_RANK}) on teacher trajectories",
     )
-    api.upload_file(
-        path_or_fileobj="eval_results.json",
-        path_in_repo="eval_results.json",
-        repo_id=MODEL_REPO,
-        repo_type="model",
-    )
+    if not skip_eval and Path("eval_results.json").exists():
+        api.upload_file(
+            path_or_fileobj="eval_results.json",
+            path_in_repo="eval_results.json",
+            repo_id=MODEL_REPO,
+            repo_type="model",
+        )
 
     print()
     print("=" * 60)
     print("DONE")
     print(f"  SFT'd model: https://huggingface.co/{MODEL_REPO}")
-    print(f"  Eval JSON:   https://huggingface.co/{MODEL_REPO}/blob/main/eval_results.json")
+    if not skip_eval:
+        print(f"  Eval JSON:   https://huggingface.co/{MODEL_REPO}/blob/main/eval_results.json")
+    else:
+        print("  Eval skipped — run scripts/eval_on_hf.py separately")
     print("=" * 60)