Pass HF_TOKEN to snapshot_download calls (authenticated resolve path)

eval_full.py

@@ -22,6 +22,7 @@ import laya
 BASE_MODEL_ID = "convaiinnovations/laya"
 FT_REPO = "peter2000/laya-vulnerability-groups-v2"
 SETFIT_REPO = "peter2000/setfit-vulnerability-groups"
+TOKEN = os.environ.get("HF_TOKEN")
 PARQUET_URL = "https://huggingface.co/datasets/GIZ/vulnerability_training_data_full/resolve/refs%2Fconvert%2Fparquet/default/train/0000.parquet"
 LABELS = [
     "Agricultural communities", "Coastal communities", "Ethnic, racial or other minorities",
@@ -111,7 +112,7 @@ def main():
     results = {}
 
     print("== laya v2 fine-tuned ==", flush=True)
-    ft_dir = snapshot_download(FT_REPO, ignore_patterns=["*.py"])
+    ft_dir = snapshot_download(FT_REPO, ignore_patterns=["*.py"], token=TOKEN)
     agent = laya.load(ft_dir, device=device)
     m = eval_agent(agent, X_te, Y_te)
     results["laya_v2_fine_tuned"] = m
@@ -120,7 +121,7 @@ def main():
     torch.cuda.empty_cache()
 
     print("== laya base zero-shot (v2 climate-context instructions) ==", flush=True)
-    base_dir = snapshot_download(BASE_MODEL_ID, ignore_patterns=["multilingual/*", "typed-decisions/*", "assets/*", "eval/*", "*.py"])
+    base_dir = snapshot_download(BASE_MODEL_ID, ignore_patterns=["multilingual/*", "typed-decisions/*", "assets/*", "eval/*", "*.py"], token=TOKEN)
     _fix_tokenizer_config(base_dir)
     agent = laya.load(base_dir, device=device)
     m = eval_agent(agent, X_te, Y_te)
@@ -131,7 +132,7 @@ def main():
 
     print("== setfit ==", flush=True)
     from setfit import SetFitModel
-    sf = SetFitModel.from_pretrained(SETFIT_REPO)
+    sf = SetFitModel.from_pretrained(SETFIT_REPO, token=TOKEN)
     t0 = time.time()
     P = np.asarray(sf.predict_proba(X_te))
     m = evaluate_full(Y_te, P)
@@ -145,7 +146,7 @@ def main():
         "split": "train_test_split(random_state=42, test_size=0.2, stratify=min(n_labels,3)); n_test=95",
         "models": results,
     }
-    api = HfApi(token=os.environ.get("HF_TOKEN"))
+    api = HfApi(token=TOKEN)
     api.upload_file(
         path_or_fileobj=json.dumps(out, indent=2).encode(),
         path_in_repo="metrics_full.json",