re-activate automatic testing

docs/cached.md

@@ -13,7 +13,7 @@ However, this will cause every worker to download the model from the Hugging Fac
 A naive way to cache the model would be to store it on a network volume in RunPod and reference the model files this way:
 
 ```bash
--hf /runpod-volume/model.gguf --ctx-size 4096 # etc...
+-m /runpod-volume/model.gguf --ctx-size 4096 # etc...
 ```
 
 Unfortunately, network volume performance is often not sufficient for loading large models, leading to long load times. RunPod introduced a [caching mechanism](https://docs.runpod.io/serverless/endpoints/model-caching) to solve this problem.

src/find_cached.py

@@ -1,69 +1,57 @@
 """
-Finds the full GGUF path from the Hugging Face cache using huggingface_hub.
+Finds the full LLM GGUF path from the Hugging Face cache.
 """
 
-import argparse
 import os
-from huggingface_hub import snapshot_download, scan_cache_dir
+import argparse
+
+CACHE_DIR = "/runpod-volume/huggingface-cache/hub"
 
 
-def find_model_path(model_name: str, gguf_in_repo: str) -> str | None:
+def find_model_path(model_name, gguf_in_repo="model.gguf"):
     """
-    Resolve the GGUF file path from the Hugging Face cache.
+    Find the path to a cached model.
 
     Args:
-        model_name: Hugging Face repo id (e.g. TheBloke/Mistral-7B-GGUF)
+        model_name: The model name from Hugging Face
-        gguf_in_repo: Relative path to the GGUF file inside the repo
 
     Returns:
-        Full filesystem path to the GGUF file, or None if not found
+        The full path to the cached model, or None if not found
     """
 
-    # First try the official way: resolve snapshot path from cache
+    cache_name = model_name.replace("/", "--")
-    try:
+    snapshots_dir = os.path.join(
-        snapshot_path = snapshot_download(
+        CACHE_DIR, f"models--{cache_name}", "snapshots"
-            repo_id=model_name,
-            local_files_only=True
     )
-        candidate = os.path.join(snapshot_path, gguf_in_repo)
-        if os.path.isfile(candidate):
-            return candidate
-    except Exception:
-        pass
 
-    # Fallback: scan cache metadata explicitly (no downloads)
+    if os.path.exists(snapshots_dir):
-    cache = scan_cache_dir()
+        snapshots = os.listdir(snapshots_dir)
-    for repo in cache.repos:
+
-        if repo.repo_id == model_name:
+        if snapshots:
-            for revision in repo.revisions:
+            return os.path.join(snapshots_dir, snapshots[0], gguf_in_repo)
-                candidate = os.path.join(revision.snapshot_path, gguf_in_repo)
-                if os.path.isfile(candidate):
-                    return candidate
 
     return None
 
 
 def main():
+    """
+    Main function to find and print the model path.
+    """
+
     parser = argparse.ArgumentParser(
         description="Find the full GGUF path from the Hugging Face cache."
     )
     parser.add_argument(
-        "model",
+        "model", type=str, help="The model name from Hugging Face"
-        type=str,
-        help="Hugging Face model repo id (e.g. TheBloke/Mistral-7B-GGUF)",
     )
     parser.add_argument(
         "path",
         type=str,
-        help="Relative path to the GGUF file inside the repo",
+        help="The path to the GGUF file within the model repository",
     )
     args = parser.parse_args()
 
     model_path = find_model_path(args.model, args.path)
-
-    if model_path is None:
-        raise SystemExit("GGUF file not found in Hugging Face cache")
-
     print(model_path, end="")
 
 

src/requirements.txt

@@ -1,5 +1,3 @@
 runpod
 python-dotenv
 openai
-huggingface_hub
-hf-transfer