diff --git a/supporting/RAGAS_Service/regression_service.py b/supporting/RAGAS_Service/regression_service.py
index a387e63..9e53edf 100644
--- a/supporting/RAGAS_Service/regression_service.py
+++ b/supporting/RAGAS_Service/regression_service.py
@@ -3,10 +3,31 @@ from sentence_transformers import SentenceTransformer
 from fastapi import FastAPI
 import torch
 import torch.nn as nn
+from huggingface_hub import hf_hub_download
+import os
 
 app = FastAPI()
 
-MODEL_PATH = "logreg_classifier.pt"
+HF_REPO_ID = "WillJeynes/LLMsForDisinformationAnalysis-Regression"
+MODEL_FILENAME = "logreg_classifier.pt"
+CACHE_DIR = "./model_cache"
+
+
+def load_checkpoint(repo_id: str, filename: str, cache_dir: str) -> dict:
+    local_path = os.path.join(cache_dir, filename)
+    if not os.path.exists(local_path):
+        print(f"Downloading {filename} from {repo_id}...")
+        os.makedirs(cache_dir, exist_ok=True)
+        downloaded = hf_hub_download(
+            repo_id=repo_id,
+            filename=filename,
+            local_dir=cache_dir,
+        )
+        print(f"Saved to {downloaded}")
+    else:
+        print(f"Using cached model at {local_path}")
+    return torch.load(local_path, map_location="cpu")
+
 
 class LogisticNet(nn.Module):
     def __init__(self, input_dim: int, hidden_dim: int, num_classes: int, dropout: float):
@@ -23,7 +44,7 @@ class LogisticNet(nn.Module):
         return self.net(x)
 
 
-checkpoint = torch.load(MODEL_PATH, map_location="cpu")
+checkpoint = load_checkpoint(HF_REPO_ID, MODEL_FILENAME, CACHE_DIR)
 
 encoder = SentenceTransformer(checkpoint["embedding_model"])