llm.py corrected

app/models/llm.py

@@ -6,9 +6,6 @@ from typing import Any
 
 from huggingface_hub import hf_hub_download
 
-# HF_REPO = os.getenv("LLAMA_HF_REPO", "openbmb/MiniCPM5-1B-GGUF")
-# HF_FILENAME = os.getenv("LLAMA_HF_FILENAME", "MiniCPM5-1B-Q4_K_M.gguf")
-
 HF_REPO = os.getenv("LLAMA_HF_REPO", "ps1811/advisor-minicpm-finetuned-gguf")
 HF_FILENAME = os.getenv("LLAMA_HF_FILENAME", "advisor-minicpm-q4_k_m.gguf")
 
@@ -16,6 +13,36 @@ _model: Any = None
 _init_lock = threading.Lock()
 
 
+def _validate_gguf_file(model_path: str) -> None:
+    if not os.path.isfile(model_path):
+        raise FileNotFoundError(f"Downloaded model file does not exist: {model_path}")
+
+    size_bytes = os.path.getsize(model_path)
+    with open(model_path, "rb") as fh:
+        magic = fh.read(4)
+        fh.seek(0)
+        first_128 = fh.read(128)
+
+    print(
+        f"[load_model] GGUF file check: size={size_bytes / (1024 ** 2):.1f} MB, "
+        f"magic={magic!r}",
+        flush=True,
+    )
+
+    if magic != b"GGUF":
+        preview = first_128.decode("utf-8", errors="replace")
+        raise RuntimeError(
+            "Downloaded file is not a valid GGUF file. "
+            f"Expected magic b'GGUF', got {magic!r}. First bytes: {preview!r}"
+        )
+
+    if size_bytes < 100 * 1024 * 1024:
+        raise RuntimeError(
+            "Downloaded GGUF file is unexpectedly small. "
+            f"Size was {size_bytes} bytes; this often means a bad upload or LFS pointer."
+        )
+
+
 def _preload_cuda_libs() -> None:
     """Expose pip-installed CUDA runtime to llama.cpp on ZeroGPU (no system libcudart)."""
     try:
@@ -45,19 +72,29 @@ def _preload_cuda_libs() -> None:
 
 def load_model() -> Any:
     global _model
-    print("🧠 [load_model] called", flush=True)
+    print("[load_model] called", flush=True)
 
     if _model is not None:
-        print("🧠 [load_model] returning cached model", flush=True)
+        print("[load_model] returning cached model", flush=True)
         return _model
 
     with _init_lock:
         if _model is not None:
             return _model
 
-        print("⬇️ [load_model] downloading model...", flush=True)
-        model_path = hf_hub_download(repo_id=HF_REPO, filename=HF_FILENAME)
-        print(f"✅ [load_model] model downloaded at {model_path}", flush=True)
+        force_download = os.getenv("LLAMA_FORCE_DOWNLOAD", "0") == "1"
+        print(
+            f"[load_model] downloading/resolving model {HF_REPO}/{HF_FILENAME} "
+            f"(force_download={force_download})...",
+            flush=True,
+        )
+        model_path = hf_hub_download(
+            repo_id=HF_REPO,
+            filename=HF_FILENAME,
+            force_download=force_download,
+        )
+        print(f"[load_model] model resolved at {model_path}", flush=True)
+        _validate_gguf_file(model_path)
 
         _preload_cuda_libs()
         from llama_cpp import Llama
@@ -65,19 +102,29 @@ def load_model() -> Any:
         gpu_layers = int(os.getenv("LLAMA_GPU_LAYERS", "-1"))
         n_ctx = int(os.getenv("LLAMA_N_CTX", "2048"))
         n_threads = int(os.getenv("LLAMA_N_THREADS", "4"))
+        verbose = os.getenv("LLAMA_VERBOSE", "0") == "1"
         print(
-            f"🚀 [load_model] initializing Llama "
-            f"(n_gpu_layers={gpu_layers}, n_ctx={n_ctx}, n_threads={n_threads})",
+            f"[load_model] initializing Llama "
+            f"(n_gpu_layers={gpu_layers}, n_ctx={n_ctx}, n_threads={n_threads}, verbose={verbose})",
             flush=True,
         )
 
-        _model = Llama(
-            model_path=model_path,
-            n_ctx=n_ctx,
-            n_gpu_layers=gpu_layers,
-            n_threads=n_threads,
-            verbose=False,
-        )
-        print("✅ [load_model] model initialized", flush=True)
+        try:
+            _model = Llama(
+                model_path=model_path,
+                n_ctx=n_ctx,
+                n_gpu_layers=gpu_layers,
+                n_threads=n_threads,
+                verbose=verbose,
+            )
+        except Exception as exc:
+            raise RuntimeError(
+                f"llama.cpp failed to load a valid GGUF file from {model_path}. "
+                "If the file check above says magic=b'GGUF' and the size is large, "
+                "this is usually a llama-cpp-python/GGUF compatibility issue or a bad quantized export. "
+                "Try setting LLAMA_VERBOSE=1 for the next run."
+            ) from exc
+
+        print("[load_model] model initialized", flush=True)
 
     return _model